1
|
Lim SY, Klein C. Parkinson's Disease is Predominantly a Genetic Disease. JOURNAL OF PARKINSON'S DISEASE 2024; 14:467-482. [PMID: 38552119 DOI: 10.3233/jpd-230376] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
The discovery of a pathogenic variant in the alpha-synuclein (SNCA) gene in the Contursi kindred in 1997 indisputably confirmed a genetic cause in a subset of Parkinson's disease (PD) patients. Currently, pathogenic variants in one of the seven established PD genes or the strongest known risk factor gene, GBA1, are identified in ∼15% of PD patients unselected for age at onset and family history. In this Debate article, we highlight multiple avenues of research that suggest an important - and in some cases even predominant - role for genetics in PD aetiology, including familial clustering, high rates of monogenic PD in selected populations, and complete penetrance with certain forms. At first sight, the steep increase in PD prevalence exceeding that of other neurodegenerative diseases may argue against a predominant genetic etiology. Notably, the principal genetic contribution in PD is conferred by pathogenic variants in LRRK2 and GBA1 and, in both cases, characterized by an overall late age of onset and age-related penetrance. In addition, polygenic risk plays a considerable role in PD. However, it is likely that, in the majority of PD patients, a complex interplay of aging, genetic, environmental, and epigenetic factors leads to disease development.
Collapse
Affiliation(s)
- Shen-Yang Lim
- The Mah Pooi Soo and Tan Chin Nam Centre for Parkinson's and Related Disorders, University of Malaya, Kuala Lumpur, Malaysia
- Department of Medicine, Faculty of Medicine, Division of Neurology, University of Malaya, Kuala Lumpur, Malaysia
| | - Christine Klein
- Institute of Neurogenetics, University of Luebeck, Luebeck, Germany
| |
Collapse
|
2
|
Shang N, Zhang L, Gao Q, Li W, Wang S, Gao X, Chen J, Zhang L, Niu Q, Zhang Q. Simultaneous effects of aluminum exposure on the homeostasis of essential metal content in rat brain and perturbation of gut microbiota. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 254:114707. [PMID: 36893695 DOI: 10.1016/j.ecoenv.2023.114707] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 06/18/2023]
Abstract
The theory of the brain-gut axis has confirmed that gut microbiota and metabolites are involved in the progression of neurodegenerative diseases through multiple pathways. However, few studies have highlighted the role of gut microbiota in cognitive impairment induced by aluminum (Al) exposure and its correlations with the homeostasis of essential metal content in the brain. To explore the relationship between alterations in the content of essential metals in the brain and relative abundance changes in gut microbiota induced by Al exposure, the Al, zinc (Zn), copper (Cu), iron (Fe), chromium (Cr), manganese (Mn), and cobalt (Co) content level in the hippocampus, olfactory bulb, and midbrain tissue were measured by inductively coupled plasma mass spectrometry (ICP-MS) methods after Al maltolate was intraperitoneally injected every other day for exposed groups. Then the unsupervised principal coordinates analysis (PCoA) and linear discriminant analysis effect size (LEfSe) were used to analyze the relative abundance of the gut microbiota community and the structure of the gut microbiome. Finally, the correlations between gut microbiota composition and essential metal content in the different exposure groups were explored by using the Pearson correlation coefficient method. Based on the results, we indicated that the content of Al in the hippocampus, olfactory bulb, and midbrain tissue was increased and then decreased with the increasing exposure duration, with peaks occurring between 14 and 30 days. Concomitantly, Al-exposure decreased the Zn, Fe, and Mn levels in these tissues. 16 S rRNA gene sequencing results indicated that significant differences in the intestinal microbial community structure at the phylum, family, and genus levels were found in the Day 90 exposed group compared with the Day 7 exposed group. Ten enriched species in the exposed group were identified as markers at the three levels. Furthermore, ten bacteria at the genus level were identified to have a significantly strong correlation (r = 0.70-0.90) with Fe, Zn, Mn, and Co.
Collapse
Affiliation(s)
- Nan Shang
- Department of Pharmacy, First Hospital of Shanxi Medical University, Taiyuan Shanxi 030001, China.
| | - Lan Zhang
- Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan Shanxi 030001, China
| | - Qi Gao
- School of Pharmacy, Shanxi Medical University, Taiyuan Shanxi 030001, China
| | - Weipeng Li
- School of Pharmacy, Shanxi Medical University, Taiyuan Shanxi 030001, China
| | - Shanshan Wang
- Section of Occupational Medicine, Department of Special Medicine, Shanxi Medical University, Taiyuan Shanxi 030001, China
| | - Xiaocheng Gao
- Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan Shanxi 030001, China
| | - Jin Chen
- Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan Shanxi 030001, China
| | - Ling Zhang
- Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan Shanxi 030001, China
| | - Qiao Niu
- Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan Shanxi 030001, China
| | - Qinli Zhang
- Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan Shanxi 030001, China
| |
Collapse
|
3
|
Xu X, Sun M, Zhang L, Fu C, Bai Y, Li C. Factory employment exposure and human health: Evidence from rural China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 259:113619. [PMID: 32191994 DOI: 10.1016/j.envpol.2019.113619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 11/04/2019] [Accepted: 11/11/2019] [Indexed: 06/10/2023]
Abstract
Quantitating the health effects of employment history in factories, especially polluting ones, is essential for understanding the benefits or losses of industrialization in rural areas. Using a traced subset of nationwide panel data from 2005 covering five provinces, 101 villages, and 2026 households (collected recently in 2016) and the econometric models, this study estimated the effect of factory employment history on workers' health. The results showed that: the absolute number of factory workers increased from 1998 to 2015, and the proportion of factory workers was 7.68% in 2015; the absolute number and the proportion of farmers decreased from 63.84% in 1998 to 29.06% in 2015. Given that all the respondents live in rural areas, the HlthPlace (the first place the individual went to for their last illness in 2015) was selected as the main dependent variable of interest, and Hlthexp (Healthcare expenditure per person at last illness in 2015) and self-reported health were used as auxiliary dependent variables. The findings revealed that, after controlling the characteristics of individual, household, hospital and area, a one year increase of factory employment history corresponded to a 0.035 level increase in the probability of people choosing high-level hospital (p < 0.01) and a 237.61 yuan increase in healthcare expenditure (p < 0.1). The results also showed the adverse effect of self-reported health on factory employment history (p < 0.01). In addition, the relationship between the farming history and health was evaluated, and the econometric results showed that compared with factory employment history, farming history had opposite impacts on health (p < 0.01). Finally, the robustness check showed that the empirical results were reliable and that the initial results were robust. Generally, this study revealed the effect of overall factory employment on health, which is a useful research supplement to the studies on the health effects of specific pollution exposure.
Collapse
Affiliation(s)
- Xiangbo Xu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; UN Environment-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China
| | - Mingxing Sun
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; UN Environment-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China.
| | - Linxiu Zhang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; UN Environment-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China
| | - Chao Fu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; UN Environment-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China
| | - Yunli Bai
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; UN Environment-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China
| | - Chang Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; UN Environment-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| |
Collapse
|