PMID- 35293378
OWN - NLM
STAT- MEDLINE
DCOM- 20220531
LR  - 20230922
IS  - 1546-4156 (Electronic)
IS  - 0893-0341 (Print)
IS  - 0893-0341 (Linking)
VI  - 36
IP  - 2
DP  - 2022 Apr-Jun 01
TI  - Temporal Speech Parameters Indicate Early Cognitive Decline in Elderly Patients 
      With Type 2 Diabetes Mellitus.
PG  - 148-155
LID - 10.1097/WAD.0000000000000492 [doi]
AB  - INTRODUCTION: The earliest signs of cognitive decline include deficits in 
      temporal (time-based) speech characteristics. Type 2 diabetes mellitus (T2DM) 
      patients are more prone to mild cognitive impairment (MCI). The aim of this study 
      was to compare the temporal speech characteristics of elderly (above 50 y) T2DM 
      patients with age-matched nondiabetic subjects. MATERIALS AND METHODS: A total of 
      160 individuals were screened, 100 of whom were eligible (T2DM: n=51; 
      nondiabetic: n=49). Participants were classified either as having healthy 
      cognition (HC) or showing signs of MCI. Speech recordings were collected through 
      a phone call. Based on automatic speech recognition, 15 temporal parameters were 
      calculated. RESULTS: The HC with T2DM group showed significantly shorter 
      utterance length, higher duration rate of silent pause and total pause, and 
      higher average duration of silent pause and total pause compared with the HC 
      without T2DM group. Regarding the MCI participants, parameters were similar 
      between the T2DM and the nondiabetic subgroups. CONCLUSIONS: Temporal speech 
      characteristics of T2DM patients showed early signs of altered cognitive 
      functioning, whereas neuropsychological tests did not detect deterioration. This 
      method is useful for identifying the T2DM patients most at risk for manifest MCI, 
      and could serve as a remote cognitive screening tool.
CI  - Copyright (c) 2022 The Author(s). Published by Wolters Kluwer Health, Inc.
FAU - Imre, Nora
AU  - Imre N
AD  - Departments of Psychiatry.
FAU - Balogh, Reka
AU  - Balogh R
AD  - Departments of Psychiatry.
FAU - Gosztolya, Gabor
AU  - Gosztolya G
AD  - MTA-SZTE Research Group on Artificial Intelligence, University of Szeged, Szeged.
FAU - Toth, Laszlo
AU  - Toth L
AD  - MTA-SZTE Research Group on Artificial Intelligence, University of Szeged, Szeged.
FAU - Hoffmann, Ildiko
AU  - Hoffmann I
AD  - Hungarian Linguistics.
AD  - Hungarian Research Centre for Linguistics, Eotvos Lorand Research Network, 
      Budapest, Hungary.
FAU - Varkonyi, Tamas
AU  - Varkonyi T
AD  - Internal Medicine.
FAU - Lengyel, Csaba
AU  - Lengyel C
AD  - Internal Medicine.
FAU - Pakaski, Magdolna
AU  - Pakaski M
AD  - Departments of Psychiatry.
FAU - Kalman, Janos
AU  - Kalman J
AD  - Departments of Psychiatry.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220316
PL  - United States
TA  - Alzheimer Dis Assoc Disord
JT  - Alzheimer disease and associated disorders
JID - 8704771
SB  - IM
MH  - Aged
MH  - Cognition
MH  - *Cognitive Dysfunction/diagnosis
MH  - *Diabetes Mellitus, Type 2/complications
MH  - Humans
MH  - Neuropsychological Tests
MH  - Speech
PMC - PMC9132238
COIS- The authors declare no conflicts of interest.
EDAT- 2022/03/17 06:00
MHDA- 2022/06/01 06:00
PMCR- 2022/05/25
CRDT- 2022/03/16 08:44
PHST- 2021/05/07 00:00 [received]
PHST- 2021/12/28 00:00 [accepted]
PHST- 2022/03/17 06:00 [pubmed]
PHST- 2022/06/01 06:00 [medline]
PHST- 2022/03/16 08:44 [entrez]
PHST- 2022/05/25 00:00 [pmc-release]
AID - 00002093-202204000-00008 [pii]
AID - 10.1097/WAD.0000000000000492 [doi]
PST - ppublish
SO  - Alzheimer Dis Assoc Disord. 2022 Apr-Jun 01;36(2):148-155. doi: 
      10.1097/WAD.0000000000000492. Epub 2022 Mar 16.