Introduction: The aim of the work was to establish a prediction model of mild cognitive impairment (MCI) progression based on intestinal flora by machine learning method. Method: A total of 1,013 patients were recruited, in which 87 patients with MCI finished a two-year follow-up. To establish a prediction model, 61 patients were randomly divided into a training set and 26 patients were divided into a testing set. A total of 121 features including demographic characteristics, hematological indicators, and intestinal flora abundance were analyzed. Results: Of the 87 patients who finished a two-year follow-up, 44 presented rapid progression. Model 1 was established based on 121 features with the accuracy 85%, sensitivity 85%, and specificity 83%. Model 2 was based on the first fifteen features of model 1 (triglyceride, uric acid, alanine transaminase, F-Clostridiaceae, G-Megamonas, S-Megamonas, G-Shigella, G-Shigella, S-Shigella, average hemoglobin concentration, G-Alistipes, S-Collinsella, median cell count, average hemoglobin volume, low-density lipoprotein), with the accuracy 97%, sensitivity 92%, and specificity 100%. Model 3 was based on the first ten features of model 1, with the accuracy 97%, sensitivity 86%, and specificity 100%. Other models based on the demographic characteristics, hematological indicators, or intestinal flora abundance features presented lower sensitivity and specificity. Conclusion: The 15 features (including intestinal flora abundance) could establish an effective model for predicting rapid MCI progression.

1.
Alzheimer’s disease facts and figures
.
Alzheimers Dement
.
2021
;
17
:
327
406
.
2.
Feng
Q
,
Ding
Z
.
MRI radiomics classification and prediction in Alzheimer’s disease and mild cognitive impairment: a review
.
Curr Alzheimer Res
.
2020
;
17
(
3
):
297
309
.
3.
Blackman
J
,
Swirski
M
,
Clynes
J
,
Harding
S
,
Leng
Y
,
Coulthard
E
.
Pharmacological and non-pharmacological interventions to enhance sleep in mild cognitive impairment and mild Alzheimer’s disease: a systematic review
.
J Sleep Res
.
2021
;
30
(
4
):
e13229
.
4.
Jack
CR
Jr
,
Bennett
DA
,
Blennow
K
,
Carrillo
MC
,
Dunn
B
,
Haeberlein
SB
, et al
.
NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease
.
Alzheimers Dement
.
2018
;
14
(
4
):
535
62
.
5.
Petersen
RC
,
Wiste
HJ
,
Weigand
SD
,
Fields
JA
,
Geda
YE
,
Graff-Radford
J
, et al
.
NIA-AA Alzheimer’s disease framework: clinical characterization of stages
.
Ann Neurol
.
2021
;
89
(
6
):
1145
56
.
6.
Bai
W
,
Chen
P
,
Cai
H
,
Zhang
Q
,
Su
Z
,
Cheung
T
, et al
.
Worldwide prevalence of mild cognitive impairment among community dwellers aged 50 years and older: a meta-analysis and systematic review of epidemiology studies
.
Age Ageing
.
2022
;
51
(
8
):
afac173
.
7.
Megur
A
,
Baltriukienė
D
,
Bukelskienė
V
,
Burokas
A
.
The microbiota-gut-brain Axis and Alzheimer’s disease: neuroinflammation is to blame
.
Nutrients
.
2020
;
13
(
1
):
37
.
8.
Kesika
P
,
Suganthy
N
,
Sivamaruthi
BS
,
Chaiyasut
C
.
Role of gut-brain axis, gut microbial composition, and probiotic intervention in Alzheimer’s disease
.
Life Sci
.
2021
;
264
:
118627
.
9.
Wu
Y
,
Hang
Z
,
Lei
T
,
Du
H
.
Intestinal flora affect Alzheimer’s disease by regulating endogenous hormones
.
Neurochem Res
.
2022
;
47
(
12
):
3565
82
.
10.
Merk
T
,
Peterson
V
,
Köhler
R
,
Haufe
S
,
Richardson
RM
,
Neumann
WJ
.
Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
.
Exp Neurol
.
2022
;
351
:
113993
.
11.
Silva
G
,
Fagundes
TP
,
Teixeira
BC
,
Chiavegatto Filho
A
.
Machine learning for hypertension prediction: a systematic review
.
Curr Hypertens Rep
.
2022
;
24
(
11
):
523
33
.
12.
Reisberg
B
,
Ferris
SH
,
de Leon
MJ
,
Crook
T
.
The Global Deterioration Scale for assessment of primary degenerative dementia
.
Am J Psychiatry
.
1982
;
139
:
1136
9
.
13.
Corbi
A
,
Burgos
D
.
Connection between sleeping patterns and cognitive deterioration in women with Alzheimer’s disease
.
Sleep Breath
.
2022
;
26
(
1
):
361
71
.
14.
Zhao
X
,
Sui
H
,
Yan
C
,
Zhang
M
,
Song
H
,
Liu
X
, et al
.
Machine-based learning shifting to prediction model of deteriorative MCI due to Alzheimer’s disease: a two-year follow-up investigation
.
Curr Alzheimer Res
.
2022
;
19
(
10
):
708
15
.
15.
Brickman
AM
,
Manly
JJ
,
Honig
LS
,
Sanchez
D
,
Reyes-Dumeyer
D
,
Lantigua
RA
, et al
.
Plasma p-tau181, p-tau217, and other blood-based Alzheimer’s disease biomarkers in a multi-ethnic, community study
.
Alzheimers Dement
.
2021
;
17
(
8
):
1353
64
.
16.
Chatterjee
P
,
Pedrini
S
,
Ashton
NJ
,
Tegg
M
,
Goozee
K
,
Singh
AK
, et al
.
Diagnostic and prognostic plasma biomarkers for preclinical Alzheimer’s disease
.
Alzheimers Dement
.
2022
;
18
(
6
):
1141
54
. .
17.
Wang
P
,
Zhang
H
,
Wang
Y
,
Zhang
M
,
Zhou
Y
.
Plasma cholesterol in Alzheimer’s disease and frontotemporal dementia
.
Transl Neurosci
.
2020
;
11
(
1
):
116
23
.
18.
Tian
N
,
Fa
W
,
Dong
Y
,
Liu
R
,
Liu
C
,
Liu
K
, et al
.
Triglyceride-glucose index, Alzheimer’s disease plasma biomarkers, and dementia in older adults: the MIND-China study
.
Alzheimers Dement (Amst)
.
2023
;
15
(
2
):
e12426
.
19.
Chatterjee
P
,
Fernando
M
,
Fernando
B
,
Dias
CB
,
Shah
T
,
Silva
R
, et al
.
Potential of coconut oil and medium chain triglycerides in the prevention and treatment of Alzheimer’s disease
.
Mech Ageing Dev
.
2020
;
186
:
111209
.
20.
Pedrini
S
,
Doecke
JD
,
Hone
E
,
Wang
P
,
Thota
R
,
Bush
AI
, et al
.
Plasma high-density lipoprotein cargo is altered in Alzheimer’s disease and is associated with regional brain volume
.
J Neurochem
.
2022
;
163
(
1
):
53
67
.
21.
Yang
W
,
Ansari
AR
,
Niu
X
,
Zou
W
,
Lu
M
,
Dong
L
, et al
.
Interaction between gut microbiota dysbiosis and lung infection as gut-lung axis caused by Streptococcus suis in mouse model
.
Microbiol Res
.
2022
;
261
:
127047
.
22.
Ou
YN
,
Zhao
B
,
Fu
Y
,
Sheng
ZH
,
Gao
PY
,
Tan
L
, et al
.
The association of serum uric acid level, gout, and Alzheimer’s disease: a bidirectional mendelian randomization study
.
J Alzheimers Dis
.
2022
;
89
(
3
):
1063
73
.
23.
Tana
C
,
Ticinesi
A
,
Prati
B
,
Nouvenne
A
,
Meschi
T
.
Uric acid and cognitive function in older individuals
.
Nutrients
.
2018
;
10
(
8
):
975
.
24.
Han
SW
,
Park
YH
,
Jang
ES
,
Nho
K
,
Kim
S
.
Implications of liver enzymes in the pathogenesis of Alzheimer’s disease
.
J Alzheimers Dis
.
2022
;
88
(
4
):
1371
6
.
25.
Wu
JJ
,
Weng
SC
,
Liang
CK
,
Lin
CS
,
Lan
TH
,
Lin
SY
, et al
.
Effects of kidney function, serum albumin and hemoglobin on dementia severity in the oldest old people with newly diagnosed Alzheimer’s disease in a residential aged care facility: a cross-sectional study
.
BMC Geriatr
.
2020
;
20
(
1
):
391
.
26.
Muñiz Pedrogo
DA
,
Chen
J
,
Hillmann
B
,
Jeraldo
P
,
Al-Ghalith
G
,
Taneja
V
, et al
.
An increased abundance of Clostridiaceae characterizes arthritis in inflammatory bowel disease and rheumatoid arthritis: a cross-sectional study
.
Inflamm Bowel Dis
.
2019
;
25
(
5
):
902
13
.
27.
Richarte
V
,
Sánchez-Mora
C
,
Corrales
M
,
Fadeuilhe
C
,
Vilar-Ribó
L
,
Arribas
L
, et al
.
Gut microbiota signature in treatment-naïve attention-deficit/hyperactivity disorder
.
Transl Psychiatry
.
2021
;
11
(
1
):
382
.
28.
Jiang
Z
,
Zhuo
LB
,
He
Y
,
Fu
Y
,
Shen
L
,
Xu
F
, et al
.
The gut microbiota-bile acid axis links the positive association between chronic insomnia and cardiometabolic diseases
.
Nat Commun
.
2022
;
13
(
1
):
3002
.
29.
Baker
S
,
The
HC
.
Recent insights into Shigella
.
Curr Opin Infect Dis
.
2018
;
31
(
5
):
449
54
.
30.
Parker
BJ
,
Wearsch
PA
,
Veloo
A
,
Rodriguez-Palacios
A
.
The genus Alistipes: gut bacteria with emerging implications to inflammation, cancer, and mental Health
.
Front Immunol
.
2020
;
11
:
906
.
31.
Hirayama
M
,
Nishiwaki
H
,
Hamaguchi
T
,
Ito
M
,
Ueyama
J
,
Maeda
T
, et al
.
Intestinal Collinsella may mitigate infection and exacerbation of COVID-19 by producing ursodeoxycholate
.
PLoS One
.
2021
;
16
(
11
):
e0260451
.
You do not currently have access to this content.