scholarly journals The Occurrence of Alzheimer’s Disease and Parkinson’s Disease in Individuals With Osteoporosis: A Longitudinal Follow-Up Study Using a National Health Screening Database in Korea

2021 ◽  
Vol 13 ◽  
Author(s):  
Mi Jung Kwon ◽  
Joo-Hee Kim ◽  
Ji Hee Kim ◽  
Seong Jin Cho ◽  
Eun Sook Nam ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
National Health ◽  
Health Screening ◽  
Incidence Rates ◽  
Residential Area ◽  
Follow Up Study

Background: Public health concerns regarding the potential link between osteoporosis and the increased occurrence of Alzheimer’s disease (AD) and Parkinson’s disease (PD) have been raised, but the results remain inconsistent and require further validation. Here, we investigated the long-term relationship of osteoporosis with the occurrence of AD/PD using data from a large-scale nationwide cohort.Methods: This longitudinal follow-up study included 78,994 patients with osteoporosis and 78,994 controls from the Korean National Health Insurance Service-Health Screening Cohort database (2002–2015) who were matched using propensity score matching at a 1:1 ratio based on age, sex, income, and residential area. A Cox proportional hazard model was used to assess the association between osteoporosis and the occurrence of AD/PD after adjusting for multiple covariates.Results: During the follow-up period, AD occurred in 5,856 patients with osteoporosis and 3,761 controls (incidence rates: 10.4 and 6.8 per 1,000 person-years, respectively), and PD occurred in 1,397 patients and 790 controls (incidence rates: 2.4 and 1.4 per 1,000 person-years, respectively). The incidences of AD and PD were significantly higher in the osteoporosis group than in the matched control group. After adjustment, the osteoporosis group exhibited 1.27-fold and 1.49-fold higher occurrences of AD (95% confidence interval (CI) = 1.22–1.32) and PD (95% CI = 1.36–1.63) than the controls, respectively. The results of subgroup analyses supported the increased occurrence of AD and PD in patients with osteoporosis, independent of income, residential area, obesity, smoking, alcohol consumption, hyperlipidemia, hypertension, or blood glucose level.Conclusion: Our results indicate that the presence of osteoporosis may increase the likelihood of developing two common neurodegenerative diseases in adults aged ≥40 years.

scholarly journals Correction: Glaucoma, Alzheimer's Disease, and Parkinson's Disease: An 8-Year Population-Based Follow-Up Study

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0150789 ◽  
Author(s):  
I-Chan Lin ◽  
Yuan-Hung Wang ◽  
Tsung-Jen Wang ◽  
I-Jong Wang ◽  
Yun-Dun Shen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Population Based ◽  
Follow Up Study

scholarly journals Glaucoma, Alzheimer's Disease, and Parkinson's Disease: An 8-Year Population-Based Follow-Up Study

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e108938 ◽  
Author(s):  
I-Chan Lin ◽  
Yuan-Hung Wang ◽  
Tsung-Jen Wang ◽  
I-Jong Wang ◽  
Yun-Den Shen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Population Based ◽  
Follow Up Study

Prospective comparative study of the evolution of probable Alzheimer's disease and Parkinson's disease dementia

1998 ◽  
Vol 4 (3) ◽  
pp. 279-284 ◽  
Author(s):  
YAAKOV STERN ◽  
MING-XIN TANG ◽  
DIANE M. JACOBS ◽  
MARY SANO ◽  
KAREN MARDER ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Test Performance ◽  
Repeated Measures ◽  
Cognitive Changes ◽  
Delayed Recall ◽  
Category Fluency

No previous comparison of test performance in probable Alzheimer's disease (pAD) and Parkinson's disease (PD) dementia has provided information about potential differences in the dementing process. This study compared the evolution of cognitive changes associated with these dementias. Generalized estimating equations (GEE) applied to regression analyses with repeated measures were used to evaluate cognitive changes over 1 to 3 years prior to the point when dementia was diagnosed in 40 matched pairs of patients with incident pAD and PD dementia. Both groups' performance declined on the Short Blessed, Selective Reminding Test (SRT; total recall, long-term retrieval, and delayed recall), Boston Naming Test, Category Fluency, and Similarities. The decline on naming and SRT delayed recall was more rapid in the PD dementia group, suggesting that these performance deficits emerge earlier in the development of pAD. The PD dementia group performed worse on Category Fluency throughout the follow-up period, suggesting either that dementia is overlaid on this preexisting performance deficit or that this type of executive deficit is an early manifestation of dementia in PD. The pAD group performed more poorly throughout the follow-up period on SRT delayed recognition, consistent with a pAD-specific encoding deficit. We conclude that while pAD and PD dementia are similar in many respects, differences in their evolution support previous observation of unique features in the 2 dementias and suggest different underlying pathologies. (JINS, 1998, 4, 279–284.)

scholarly journals Biomarkers of neurodegeneration and glial activation validated in Alzheimer’s disease assessed in longitudinal cerebrospinal fluid samples of Parkinson’s disease

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257372
Author(s):  
Michael Bartl ◽  
Mohammed Dakna ◽  
Douglas Galasko ◽  
Samantha J. Hutten ◽  
Tatiana Foroud ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Cerebrospinal Fluid ◽  
Cognitive Decline ◽  
Neurofilament Light Chain ◽  
Significant Difference

Aim Several pathophysiological processes are involved in Parkinson’s disease (PD) and could inform in vivo biomarkers. We assessed an established biomarker panel, validated in Alzheimer’s Disease, in a PD cohort. Methods Longitudinal cerebrospinal fluid (CSF) samples from PPMI (252 PD, 115 healthy controls, HC) were analyzed at six timepoints (baseline, 6, 12, 24, 36, and 48 months follow-up) using Elecsys® electrochemiluminescence immunoassays to quantify neurofilament light chain (NfL), soluble TREM2 receptor (sTREM2), chitinase-3-like protein 1 (YKL40), glial fibrillary acidic protein (GFAP), interleukin-6 (IL-6), S100, and total α-synuclein (αSyn). Results αSyn was significantly lower in PD (mean 103 pg/ml vs. HC: 127 pg/ml, p<0.01; area under the curve [AUC]: 0.64), while all other biomarkers were not significantly different (AUC NfL: 0.49, sTREM2: 0.54, YKL40: 0.57, GFAP: 0.55, IL-6: 0.53, S100: 0.54, p>0.05) and none showed a significant difference longitudinally. We found significantly higher levels of all these markers between PD patients who developed cognitive decline during follow-up, except for αSyn and IL-6. Conclusion Except for αSyn, the additional biomarkers did not differentiate PD and HC, and none showed longitudinal differences, but most markers predict cognitive decline in PD during follow-up.

Neurobehavioral Consequences Associated with Long Term Tramadol Utilization and Pathological Mechanisms

2020 ◽  
Vol 18 (10) ◽  
pp. 758-768 ◽  
Author(s):  
Khadga Raj ◽  
Pooja Chawla ◽  
Shamsher Singh
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Serotonin Reuptake ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Serotonin Syndrome ◽  
Dopamine Synthesis ◽  
Synthetic Analog

: Tramadol is a synthetic analog of codeine used to treat pain of moderate to severe intensity and is reported to have neurotoxic potential. At therapeutic dose, tramadol does not cause major side effects in comparison to other opioid analgesics, and is useful for the management of neurological problems like anxiety and depression. Long term utilization of tramadol is associated with various neurological disorders like seizures, serotonin syndrome, Alzheimer’s disease and Parkinson’s disease. Tramadol produces seizures through inhibition of nitric oxide, serotonin reuptake and inhibitory effects on GABA receptors. Extensive tramadol intake alters redox balance through elevating lipid peroxidation and free radical leading to neurotoxicity and produces neurobehavioral deficits. During Alzheimer’s disease progression, low level of intracellular signalling molecules like cGMP, cAMP, PKC and PKA affect both learning and memory. Pharmacologically tramadol produces actions similar to Selective Serotonin Reuptake Inhibitors (SSRIs), increasing the concentration of serotonin, which causes serotonin syndrome. In addition, tramadol also inhibits GABAA receptors in the CNS has been evidenced to interfere with dopamine synthesis and release, responsible for motor symptoms. The reduced level of dopamine may produce bradykinesia and tremors which are chief motor abnormalities in Parkinson’s Disease (PD).

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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