Glycative Stress Research
Online edition : ISSN 2188-3610
Print edition : ISSN 2188-3602
Received : January 17, 2018
Accepted : February 10, 2018
Published online : March 31, 2018

Review article

Cognitive impairment and glycative stressMichiya Igase 1), Keiji Igase 2)
1) Department of Geriatric Medicine and Neurology, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
2) Department of Advanced Medicine and Neurology, Ehime University Graduate School of Medicine, Toon, Ehime, Japan

Abstract

The relationship between dementia and advanced glycation end products (AGEs), one of the glycative stress markers, hasbeen gaining attention in recent years. The increased production of AGEs is related to many conditions, such as increased
inflammation and oxidant stress associated with diabetes. Aging is one of the conditions, and dementia, like Alzheimer’s
disease, is more accelerated than normal aging. It is known that in the case of diabetes, the increase of AGEs causes dementia
or mild cognitive impairment (MCI). However, there are almost no data regarding the relationship between AGEs and cognitive
function under non-diabetic states. We examined the relationship between the accumulated amount of skin auto fluorescence(SAF) and MCI of anti-aging medical checkup examinees who were not presenting clear symptoms of diabetes. The number ofsubjects was 226 (84 males with an average age of 69 ± 11 and 142 females with an average age of 67 ± 10). Kidney function,
brain natriuretic peptide (BNP), brachial ankle pulse wave velocity (baPWV), brain atrophy (temporal horn area) and SAF
value were significantly higher in the group of the subjects who were evaluated as showing MCI (n = 18) than those of thegroup showing no MCI. Even after the adjustment of various confounding factors using logistic regression analysis, the fact> 2.27 ) was significantly related to the existence of MCI. The measurement of the SAF level isthat the SAF value was high ( =considered to be useful from the perspective of prevention of dementia.

KEY WORDS: advanced glycation end products (AGEs), mild cognitive impairment (MCI),		

Anti-Aging Medical Checkup, MCI screen, skin auto fluorescence (SAF)

Introduction
Cognitive impairment (dementia) is one of the mostimportant diseases in an aging society. There are approximately
4.62 million dementia patients in Japan, and furthermore,
there are approximately four million elderly presenting mild
cognitive impairment (MCI) who are considered to be future
dementia patents. MCI was described as a general disorderof aging increasing the risk of dementia including Alzheimer
disease (AD) for the first time in 1999, and it is known that if
MCI is diagnosed, the patient progresses toward dementia at
the rate of 10 -15% a year.
It is estimated that dementia patients will increase toseven million by 2025. However, no method leading to theradical cure of dementia has been established. Because there
is no fundamental treatment method for dementia at present,
it is important to detect the onset of dementia and MCI andappropriately intervene in the lives of patients as soon as
possible.
Meanwhile, advanced glycation end products (AGEs) are
drawing attention as glycative stress marker in association
Correspondence to: Michiya Igase MD, PhD
Department of Geriatric Medicine and Neurology,
Ehime University Graduate School of Medicine
Shitsukawa, Toon, Ehime 791- 0295, Japan
TEL: + 81- 89 -960 -5851 FAX: +81- 89-960 -5852
eMail: migase@m.ehime-u.ac.jp
Co-author: Igase K, igase@sadamoto-hsp.jp

with dementia recently. AGEs are protein and lipid
nonenzymatic glycative products. The AGEs produced are not
only caused by diabetes as being imagined from its naming,but also caused by aging, oxidative stress, inflammatory
conditions and kidney failure and at present, it is considered
to be one of main causes of the progression of arterialsclerosis. Most of the AGEs have not been covered by healthinsurance. However, pentosidine is a fluorescent and crosslinking AGE effectively produced from ribose, arginine andlysine and this AGE only is covered by health insurance as
an early marker of kidney diseases.
This research focused on AGEs and outlined therelationship of AD, a typical dementia, and MCI with AGEs
also using the data of our laboratory.

Background– Relationship of AD and MCI with AGEs –AD patients are considered to have an accumulation ofAGEs 1). The accumulation of AGEs in cells and tissues is a_ 45 _Glycative Stress Research 2018; 5 (1): 045-049(c) Society for Glycative Stress ResearchCognitive Impairment and Glycative Stress.
normal characteristic of aging: however it is accelerated in
the case of AD, suggesting its definite contribution to thecause of AD. According to recent research, in the case ofdiabetic patients, the level of AGEs rises in relation to theenhancement of inflammation and oxidative stress, whichleads to the onset of dementia and MCI 2). On the other hand,the data similar to the above can be rarely observed in thecase of non-diabetic patients. Therefore, in order to better
understand the relationship between MCI and AGEs in thecase of healthy people without clear diabetic symptoms, we
examined the accumulation status of AGEs in the tissuesof community members who received thorough medicalcheckups.

Method– Including MCI Screening ofAnti-Aging Medical Checkup –The subjects were the participants in Anti-Aging
Medical Checkups of a health examination in the Anti-Aging
Center (AAC) in Ehime University Hospital specificallydesigned to evaluate age-related diseases including arterialsclerosis and problems of physical functions 3 - 5) for theperiod from April 2013 to April 2015. All clinical data
were obtained through the Anti-Aging Medical Checkup
inspection process. The requirements for participation in thisresearch were “the subject should not have received periodic
drug treatment for hypertension or diabetes for at least 6months and be 4 0 years or older.” In order to exclude theinfluence of cerebral-and-cardio-vascular disease treatment
as an exclusion requirement, in addition to the items
described above, the data of the participants with a history ofstroke and cardiovascular diseases including ischemic heartdisease were excluded. Items for inspection were, generalblood tests, brachial ankle pulse wave velocity (baPWV)
test and the measurements of clinical parameters relating
to arterial sclerosis, including brain MRI examination, inaddition to the measurement of the accumulation volumeof AGEs in skin tissue and MCI screening test, which will
be discussed later. For general blood examination, blood
samples were collected from the cubital vein from 9 am to
10 am after a fasting period of longer than 12 hours and thefollowing biochemical parameters were measured: fastingblood sugar (FBS), HbA1c, serum creatinine (sCr), highdensity-lipoprotein-cholesterol (HDL-C), triglyceride (TG),high-sensitivity-C-reactive protein (hsCRP), brain natriuretic
peptide (BNP), low-density-lipoprotein-cholesterol (LDL-C)
and estimated glomerular filtration rate (eGFR). LDL-C
level was calculated using Friedewald’s formula 6). eGFR wascalculated using Cockcroft-Gault 7). baPWV was measured
using the device (FORM/ABI ; Omron Colin Co. Ltd.,
Komaki, Ehime, Japan) 8) after five-minute rest and theaverage value of right and left was used as a data. Aftera brain MRI examination was conducted, as was alreadyreported, right and left sides of temporal horn area (THA)
were measured at the level of pentagon using a T2 flairweighted image and the average value was used as data. It
is reported that THA evaluated by MRI is an independent
predictive factor for the progress of dementia 9).
This clinical research was conducted by strictly
observing the ethical principles based upon the Helsinki
Declaration and it was also approved by the EthicalCommittee of Ehime University Graduate School of Medicine.
Informed consent forms were received from all subjects
before the research.

Measurement of Accumulation Amount ofAGEs in Skin Tissue

SAF (skin auto fluorescence) is used to measure theAGEs in tissue using AGE-reader (DiagnoOptics Technologies
VB, Groningen, The Nederland) by the methods based on
several fluorescence properties. An AGE-reader is a desktoptype device and it can presume the level of AGE accumulationin the skin by noninvasive measurement 10). Specifically,
it is to measure AGEs utilizing the characteristic of AGEs
that when an ultraviolet ray irradiates the skin, fluorescent
AGEs accumulated in the skin tissue are excited and radiatea unique auto-fluorescence. All measurements are conductedon the palmar side of forearm in the range from 10 cm to 15
cm under the elbow. The measurement value is calculated
in a way that the average value of emitted light intensity pernm in the range from 420 nm to 600 nm is divided by theaverage value of excitation light intensity per nm in the range
from 300 nm and 420 nm, and it is shown by arbitrary unit(AU).
In a precedent research, it was verified that the SAF
value is determined by the accumulation of multiple AGEsand that there are strong correlations between SAF value andthe amounts of AGEs (pentosidine, carboxymethyl-lysine[CML], carboxycthyl-lysine [CEL] in particular) contained inskin tissue 10). Furthermore, it is reported in the same researchthat the error rate of SAF value per day is 5.0 %, and that due
to seasonal change, it is 5.9 %.

Screening Test of MCI

Although various methods for diagnosis of MCI areinternationally approved, only a few Japanese versions havebeen studied for validation. We have been using the Japaneseversion of the MCI screening inspection “MCI screen”
developed by Shankle et al. 11, 12) since the start of our “AntiAging Medical Checkup” in 2006. This is for a staff
management test that can be conducted in 10 minutes. Its
cross-validation was confirmed by referring to clinicaldementia rating scale (CDRS) and its overall accuracyof identifying the subjects presenting MCI is 97 % 13). It is
one of the methods verified as the most highly sensitivein the medical institutions in the community and has been
employed by an internet healthy subjects registration system,
IROOP TM (Integrated Registry of Orange Plan: Iroop,Kodaira, Tokyo, Japan) aiming at the prevention of the onset
of dementia in Japan since last year.

Results– SAF value as Independent Risk Factor ofthe Existence of MCI –The number of the subjects who were evaluated as
exhibiting MCI was 18 (7.9%) out of 226 subjects (84males with an average age of 69 ± 11 and 142 femaleswith an average age of 67 ± 10). The clinical characteristicsof subjects showing MCI and showing no MCI weresummarized in Table 1. The group showing MCI wassignificantly older than the group showing no MCI. There
were no significant differences in other known dementia
risk factors including systolic blood pressure between
the two groups. However, the values of sCr, eGFR, BNP,baPWV, THA and SAF of the group showing MCI weresignificantly higher than those of the group showing no MCI.
By univariate analysis, there were significant correlations of_ 46 _

Glycative Stress Research

SAF value with the values of age, sCr, eGFR, UACR, BNP,baPWV, and THA ( Table 2 ). No significant difference wasrecognized in education level between the two groups.
The ratio of subjects showing MCI was analyzed using
the tertile method by each of six factors. The mobility ofMCI was different among the tertile method groups ofage, eGFR, BNP, baPWV, THA and SAF ( Fig. 1-a ~ f ).
Furthermore, in order to obtain an SAF cut-off value, a curve
analysis of receiver operating characteristic (ROC) regarding
the existence of MCI was conducted, and as a result, thehighest sensitivity was obtained at SAF value 2.27 and it
was employed as a cut-off value. Finally a logistic regression
analysis was conducted. As shown in Table 3, even after theadjustments of confounding factors considered to include> 2.27 wasage, eGFR, BNP, baPWV and THA, SAF value =significantly related to the existence of MCI (odds ratio
6.402; 95% Cl, 1.590 – 25.773, p = 0.009).
Although, there have been multiple reports concerning
the significant relationships between SAF value with brain
atrophy and cognitive decline in diabetic patients 14, 15), these
relationships in the non-diabetic patient group are reported
for the first time in our research. However, recent research
by Waateringe et al. verifies that SAF value is related tosome clinical and life-style factors including age, BMI andkidney function in non-diabetic patients 16). Furthermore, it
is clarified that AGEs are formed not only under diabetic
conditions, but also under disease conditions including

chronic kidney disease associated with high level oxidant
stress. Therefore, a higher level of circulation AGEs may
possibly cause neurotoxicity through oxidant stress 17). Putting
all of this together, the existence of an important relationshipbetween MCI and SAF in non-diabetic patients that wedemonstrated in this research should be further verified.

Conclusion

The recent development of research concerning antiaging is remarkable. As introduced in this research, it wasclarified that SAF value reflecting the accumulation of AGEs
in skin tissue can be an independent screening marker ofMCI. Although this research is relatively small scaled andfurther research is required, it is considered that when anabnormal value is recognized in the measurement of SAF
during an anti-aging medical checkup, it is necessary to keep
following-up with outpatients with risk of onset of dementia
in the future being taken into account even if the patient isin a non-diabetic state, and needless to say if the patient is indiabetic state.

Conflict of interest

The authors declare no conflict of interest in this study.

Table 1. Profile of participants.

Index
NumberAge

UnityearMennumber (%)

BMI

kg/m

Presently smoking
SBP

HbA1c [NGSP]
sCr

eGFR

UACR

LDL-C

HDL-C
TG

hsCRP

number (%)
2mmHg%mg/dLmL/min/1.73 m
mg/gCr
mg/dL
mg/dL
mg/dL
mg/L

BNP

pg/dL

THAx10 cm

baPWV
SAF

cm/s-222MCI

Normalp value

18208-76.5 ± 6.7
9 (50)

67.2 ± 9.9
75 (37)< 0.0012 (12)

8 (4)

0.189

22.9 ± 2.1

22.8 ± 3.0

124.4 ± 13.3

123.7 ± 14.9

5.9 ± 0.8

5.8 ± 0.6

0.84 ± 0.18

0.74 ± 0.16

59.1 ± 9.5

68.4 ± 11.1

39.2 ± 66.5

24.0 ± 48.7

119.2 ± 32.6

117.1 ± 29.7

61.6 ± 11.7

61.9 ± 14.6

90.2 ± 38.1

97.9 ± 48.5

0.10 ± 0.09

0.08 ± 0.12

47.5 ± 27.9

28.8 ± 35.71770 ± 319

1577 ± 277

24.5 ± 12.2

13.0 ± 7.6

2.56 ± 0.55

2.10 ± 0.41

0.190
0.826
0.843
0.256
0.012
0.002
0.242
0.779
0.952
0.528
0.610
0.039
0.006
< 0.001< 0.001

MCI, mild cognitive impairment; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c [NGSP], HbA1c equivalent to theinternationally used HbA1c defined by the National Glycohemoglobin Standardization Program; sCr, serum creatinine; eGFR, creatinine-based estimatedglomerular filtration rate; UACR, urinary albumin-to-creatinine ratio; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol;
TG, serum triglyceride; hs-CRP, high sensitivity C-reactive protein; BNP, brain natriuretic peptide; baPWV, brachial ankle pulse wave velocity; SAF, skinautofluorescence; THA, temporal horn area

_ 47 _Cognitive Impairment and Glycative Stress.
Fig. 1-bFig. 1-a
20%

20%

18%

18%

16%

16%

Prevalence of MCI

p = 0.028

Prevalence of MCI

p = 0.001

14%
12%
10%
8%6%
4%

14%
12%
10%
8%
6%
4%2%2%0%0%Lower Tertile(n =71)

Middle Tertile(n =78)Upper Tertile(n =77 )Fig. 1-cFig. 1- d

20%

20%

18%

18%

16%

16%

14%
12%
10%
8%

1, 12

6%
4%
2%

Upper Tertile(n =71)

14%
12%
10%
8%
1, 10

6%
4%
2%

Lower Tertile(n =75 )Middle Tertile(n =75 )0%Upper Tertile(n =76)

Brain natriuretic peptide (BNP)

Lower Tertile(n = 78)

Middle Tertile(n =77 )Upper Tertile(n = 71)

Brachial ankle pulse wave velocity (baPWV)

Fig. 1- e

Fig. 1- f

20%20%18%18%

16%

16%

14%
12%
10%

1, 108%6%
4%

Prevalence of MCI

p = 0.006

p = 0.011

Prevalence of MCI

Middle Tertile(n =77 )p = 0.049

Prevalence of MCI

Prevalence of MCI

p = 0.003

14%
12%
10%
8%

1, 12

6%
4%2%2%
0%

Lower Tertile(n =74)

Estimated glomerular filtration rate (eGFR)

Age0%

1, 1

Lower Tertile(n = 75 )Middle Tertile(n = 76)0%Upper Tertile(n =75 )Lower Tertile(n = 76)

Middle Tertile(n = 71)

Upper Tertile(n =79)Skin autofluorescence (SAF)

Temporal Horn Area (THA)

Fig. 1. Analysis of MCI prevalence by participant’s profile indexa) Age, b) eGFR, c) BNP, d) baPWV, e) THA, f) SAF. MCI prevalence (%) in the participants is analyzed by each index. In these analyses,
there are significantly differences in tertiles by one way analysis of variance (ANOVA). MCI, mild cognitive impairment; eGFR, estimatedglomerular filtration rate; BNP, brain natriuretic peptide; baPWV, brachial ankle pulse wave velocity; THA, temporal horn area; SAF, skinauto fluorescence.

_ 48 _Glycative Stress Research

Table 2. Simple correlation analysis between SAF and MCI-related parameter.

rp value

Age

0.423

< 0.001

eGFR− 0.255

0.001

baPWV

0.182

sCr

BNP

0.251

THA

0.001

0.290

< 0.001

0.419< 0.001

0.017

Pearson’ s correlation analysis, n = 226. SAF, skin auto fluorescence; MCI, mild cognitive impairment;
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; PR, pulse rate; FBS,fasting blood sugar; IRI, immunoreactive insulin; LDL-C, low-density lipoprotein cholesterol; HDL-C,high-density lipoprotein cholesterol; TG, serum triglyceride; sCr, serum creatinine; eGFR, estimatedglomerular filtration rate; UACR, urinary albumin-to-creatinine ratio; hs-CRP, high sensitivity C-reactiveprotein; BNP, brain natriuretic peptide; baPWV, brachial ankle pulse wave velocity

Table 3. Odds ratio of predictor variables regarding to the objective variable “ Presence of MCI ” .

OR95% C I

p value

SAF ≥ 2.27

6.402

1.590 – 25.773

0.009

THA1.059

1.002 – 1.119

0.044

Age1.116

1.013 – 1.231

0.027

Multivariate logistic regression analysis, n = 226. OR, odds ratio; CI, confident interval; SAF, skin autofluorescence;
THA, temporal horn area

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