Parkinson's disease (PD) is characterized by the pathological accumulation of α-synuclein, which has driven extensive research into the role of exosomes in disease mechanisms. Exosomes are nanoscale vesicles enriched with proteins, RNA, and lipids that facilitate critical intercellular communication processes. Recent studies have elucidated the role of exosomes in transmitting misfolded proteins among neurons, which significantly impacts the progression of PD. The presence of disease-associated exosomes in cerebrospinal fluid and blood highlights their substantial diagnostic potential for PD. Specifically, exosomes derived from the central nervous system (CNS) have emerged as promising biomarkers because of their ability to accurately reflect pathological states. Furthermore, the isolation of exosomes from distinct brain cell types allows the identification of precise biomarkers, increasing diagnostic specificity and accuracy. In addition to being useful for diagnostics, exosomes hold therapeutic promise given their ability to cross the blood–brain barrier (BBB) and selectively modulate their cargo. These findings suggest that these materials could be used as delivery systems for therapeutic drugs for the treatment of neurodegenerative diseases. This review comprehensively examines the multifaceted roles of exosomes in PD pathogenesis, diagnosis, and treatment. It also addresses the associated clinical challenges and underscores the urgent need for further research and development to fully leverage exosome-based strategies in PD management.
Graphical abstract
Citation: Naeimeh Akbari-Gharalari, Maryam Ghahremani-Nasab, Roya Naderi, Leila Chodari, Farshad Nezhadshahmohammad. The potential of exosomal biomarkers: Revolutionizing Parkinson's disease: How do they influence pathogenesis, diagnosis, and therapeutic strategies?[J]. AIMS Neuroscience, 2024, 11(3): 374-397. doi: 10.3934/Neuroscience.2024023
| [1] | XinQi Dong, Manrui Zhang . The Prevalence of Neurological Symptoms among Chinese Older Adults in the Greater Chicago Area. AIMS Medical Science, 2015, 2(1): 35-50. doi: 10.3934/medsci.2015.1.35 |
| [2] | Xinqi Dong, E-Shien Chang, Stephanie Bergren . The Prevalence of Musculoskeletal Symptoms among Chinese older Adults in the Greater Chicago Area—Findings from the PINE Study. AIMS Medical Science, 2014, 1(2): 87-102. doi: 10.3934/medsci.2014.2.87 |
| [3] | Xinqi Dong, E-Shien Chang, Melissa A. Simon . Sense of Community among Chinese Older Adults in the Greater Chicago Area: Findings from the PINE Study. AIMS Medical Science, 2014, 1(1): 28-39. doi: 10.3934/medsci.2014.1.28 |
| [4] | XinQi Dong . Addressing Health and Well-being of U.S. Chinese Older Adults through Community-Based Participatory Research: Introduction to the PINE Study. AIMS Medical Science, 2015, 2(3): 261-270. doi: 10.3934/medsci.2015.3.261 |
| [5] | E-Shien Chang, Xinqi Dong . Personality Traits among Community-Dwelling Chinese Older Adults in the Greater Chicago Area. AIMS Medical Science, 2014, 1(2): 73-86. doi: 10.3934/Medsci.2014.2.73 |
| [6] | Manrui Zhang, Melissa A. Simon, Xinqi Dong . The Prevalence of Perceived Stress among U.S. Chinese Older Adults. AIMS Medical Science, 2014, 1(1): 40-56. doi: 10.3934/medsci.2014.1.40 |
| [7] | Xinqi Dong, E-Shien Chang, Stephanie Bergren BA . The Burden of Grandparenting among Chinese older adults in the Greater Chicago area—The PINE Study. AIMS Medical Science, 2014, 1(2): 125-140. doi: 10.3934/medsci.2014.2.125 |
| [8] | Xinqi Dong, Manrui Zhang, Melissa A. Simon . Self-mastery among Chinese Older Adults in the Greater Chicago Area. AIMS Medical Science, 2014, 1(1): 57-72. doi: 10.3934/medsci.2014.1.57 |
| [9] | Ruijia Chen, Melissa A. Simon, Xinqi Dong . Gender Differences in Depressive Symptoms in U.S. Chinese Older Adults. AIMS Medical Science, 2014, 1(1): 13-27. doi: 10.3934/Medsci.2014.1.13 |
| [10] | Xinqi Dong, Ruijia Chen, Susan K. Roepke-Buehler . Characteristics Associated with Psychological, Physical, Sexual Abuse, Caregiver Neglect and Financial Exploitation in U.S. Chinese Older Adults: Findings from the Population-Based Cohort Study in the Greater Chicago Area. AIMS Medical Science, 2014, 1(2): 103-124. doi: 10.3934/medsci.2014.2.103 |
Parkinson's disease (PD) is characterized by the pathological accumulation of α-synuclein, which has driven extensive research into the role of exosomes in disease mechanisms. Exosomes are nanoscale vesicles enriched with proteins, RNA, and lipids that facilitate critical intercellular communication processes. Recent studies have elucidated the role of exosomes in transmitting misfolded proteins among neurons, which significantly impacts the progression of PD. The presence of disease-associated exosomes in cerebrospinal fluid and blood highlights their substantial diagnostic potential for PD. Specifically, exosomes derived from the central nervous system (CNS) have emerged as promising biomarkers because of their ability to accurately reflect pathological states. Furthermore, the isolation of exosomes from distinct brain cell types allows the identification of precise biomarkers, increasing diagnostic specificity and accuracy. In addition to being useful for diagnostics, exosomes hold therapeutic promise given their ability to cross the blood–brain barrier (BBB) and selectively modulate their cargo. These findings suggest that these materials could be used as delivery systems for therapeutic drugs for the treatment of neurodegenerative diseases. This review comprehensively examines the multifaceted roles of exosomes in PD pathogenesis, diagnosis, and treatment. It also addresses the associated clinical challenges and underscores the urgent need for further research and development to fully leverage exosome-based strategies in PD management.
Graphical abstract
Alzheimer's disease;
ALG2-interacting protein X;
autophagy–lysosome pathway;
amyotrophic lateral sclerosis;
atypical parkinsonian syndrome;
blood–brain barrier;
corticobasal degeneration;
central nervous system;
cerebrospinal fluid;
dementia with Lewy bodies;
exosomal transfer into cells;
frontotemporal dementia;
glucocerebrosidase (GBA);
β-glucocerebrosidase;
heat shock protein 70;
heat shock protein 90;
Huntington's disease;
intercellular adhesion molecule 1;
intraluminal vesicles;
idiopathic PD;
lymphocyte function-associated antigen 1;
long noncoding RNAs;
leucine-rich repeat kinase 2;
L1 cell adhesion molecule;
microtubule-associated protein;
microtubule-associated protein tau gene;
microRNAs;
multiple sclerosis;
multiple system atrophy;
mesenchymal stem cell;
multivesicular bodies;
olivopontocerebellar atrophy;
oxidized DJ-1;
Parkinson's disease;
PTEN-induced kinase 1;
progressive supranuclear palsy;
rapid eye movement sleep behavior disorder;
RNA interference;
Ser-1292-phosphorylated LRRK2;
shRNA minicircles;
small interfering RNAs;
alpha-synuclein gene;
striatonigral degeneration;
tumor susceptibility gene 101;
α-Synuclein
Gambling takes place in various forms (e.g., lotteries, bingo, casinos, or card games) and can be categorized into different types, including social/recreational gambling and problem gambling. Due to increased social acceptability, accessibility, and legalization, gambling has become a highly visible form of recreation. In the field of public health, gambling has become a serious concern, as a growing number of studies have linked gambling to poorer health [1,2,3]. In particular, problem gambling, which is defined as the gambling behavior that causes disruption in any major area of life [4], has been consistently shown to be associated with substance abuse [5], suicide attempts [3], and family violence [1].
Gambling is common among older adults, with 50.3% of U.S. adults aged 65 years and over reporting gambling in the past year, according to the Gambling Impact and Behavior Study [6]. Gambling motivation, behavior, and impacts among older adults may be different from that of younger adults [7]. Unlike younger adults who tend to participate in gambling activities for financial gains, older adults may consider gambling as a source of recreation, given an increased amount of free time after retirement [7]. In addition, older adults may participate in gambling to maintain cognitive function, escape age-related stressors, or get rid of boredom [6]. Further, in contrast to younger adults who may involve in multiple types of games at the same time, older adults tend to focus on limited types of gambling [8]. Differences in health impacts of gambling between older and younger age groups are also evident. According to a study comparing health correlates of gambling between younger adults and older adults, recreational gambling was associated with better self-reported general health among older adults, but such pattern of association was not observed among younger adults [6].
While gambling may have potential positive effects on older adults’ health, recent studies demonstrate that older adults are at high risk for experiencing problem gambling. In a survey of 1,260 older residents aged 55 and older in Florida, 0.7% of the participants were problem gamblers and 3.7% were at-risk gamblers [9]. In another study of 343 adults aged 60 years and older, 6.4% of the participants were problem gamblers whose physical and mental health problems were significantly greater than that of the non-problem gamblers [10]. Older adults may be more susceptible to problem gambling than younger adults because they tend to have fixed income, reducing their likelihood of recovery after losing money gambling. Additionally, program gambling may exacerbate chronic medical conditions such as hypertension, liver disease, and arthritis, which are more common among older adults than in younger adults [11]. A better understanding of problem gambling among older adults is warranted to facilitate the development of effective gambling interventions.
Studies on gambling must take into account the cultural background of the gamblers. Cultural values and beliefs may influence older adults’ attitude towards gambling, affect their gambling pattern and behavior, and guide their help-seeking behavior and service utilization [12]. Researchers have suggested that gambling is more prevalent in a society where people hold positive attitudes towards gambling than in where people condemn gambling participation [12]. Indeed, in Chinese society, gambling is deemed as a socially acceptable activity. In particular, social gambling is prevalent during festive seasons such as the Chinese New Year, and during gatherings, such as birthdays and weddings [13]. Superstitious beliefs may also contribute to the initiation and maintenance of gambling in Chinese culture [14]. Believing in the power of luck, Chinese may be more likely to engage in risk-taking activities such as gambling. Compared to other racial ethnic groups, Chinese adults have been shown to have a higher prevalence of gambling and greater levels of problem gambling and addiction [15]. A cross-cultural study of gambling among Arabic, Chinese, Greek, and Vietnamese adults in Australia found that Chinese had a disproportionate number of problem gamblers [16].
Although gambling may be socially acceptable in Chinese culture, losing control over gambling is not tolerated in Chinese society [17]. Problem gamblers in Chinese culture tend to be characterized as irresponsible and deceitful. Due to a fear of losing respect, Chinese gamblers may be reluctant to admit their problems and seek treatments [13]. In addition, although problem gambling has been well recognized as a psychological disorder in Western culture, it remains difficult for Chinese to accept it as an illness that requires psychiatry treatments [18]. Even if problem gambling is perceived as an illness, Chinese may believe that disclosing psychological issues would not only bring shame to the problem gambler but also threaten family reputation, due to the social stigma attached to mental illness. Thus, to save “face” [19], Chinese gamblers may conceal the issue and bail out the problems by themselves, as opposed to seek professional help.
Immigration may affect the magnitude and severity of gambling to some extent. Gambling may be a way for immigrants to cope with external immigration stressors and to achieve a sense of belonging to the country of residence. It has also been postulated that leaving the home country to the U.S. is a gamble itself, and therefore immigrants are often risk-takers by nature and are perhaps more likely to engage in gambling [20]. Chinese older adults represent the largest segment of Asian American older adult population. In addition to acculturation stress and great health disparities, recent studies consistently demonstrate that U.S. community-dwelling Chinese older adults are at risk for a wide range of psychosocial issues [21,22,23,24], emphasizing the urgent need to improve understanding of the social and psychological well-being of this vulnerable population.
However, very few studies to date have been conducted to examine gambling participation and problems among Chinese immigrant older adults. To narrow the knowledge gap, this study aimed to: 1) describe the prevalence, frequency, and types of gambling participation and problems; and 2) explore the sociodemographic and health-related correlates of gambling and problem gambling among Chinese older adults in the U.S.
The Population Study of Chinese Elderly in Chicago (PINE) is a community-engaged, population-based epidemiological study of Chinese older adults aged 60 and over in the greater Chicago area. Briefly, the purpose of the PINE study istocollect community-level data to examine the key social and cultural determinants of health and well-being among Chinese older adults in the U.S. [25]. The project was initiated by a synergistic community-academic collaboration among the Rush Institute for Healthy Aging, Northwestern University, and many community-based social services agencies and organizations throughout the greater Chicago area. The study was approved by the Institutional Review Board of the Rush University Medical Center.
In order to ensure study relevance to the well-being of the Chinese community and increase community participation, the PINE study implemented extensive culturally and linguistically appropriate community recruitment strategies strictly guided by a community-based participatory research (CBPR) approach. The formation of this community-academic partnership allowed us to develop appropriate research methodology in accordance with the local Chinese cultural context. The community advisory board (CAB) plays a pivotal role in providing insights and strategies for conducting research [26,27]. Board members were community stakeholders and residents enlisted through over twenty civic, health, social and advocacy groups, community centers and clinics in the city and suburbs of Chicago. The board works extensively with investigative team to develop and examine study instruments to ensure cultural sensitivity and appropriateness [28].
Over twenty social services agencies, community centers, health advocacy agencies, faith-based organizations, senior apartments and social clubs served as the basis of study recruitment sites. Several additional strategies were employed to encourage participation in the study and increase cultural and linguistic appropriateness of the study. Flyers and posters advertising the study were placed in the public spaces including restaurants, teahouses, and parks frequented by Chinese families. Due to the closely-knitted ethnic social network connecting the families of Chinese immigrants, over a third of PINE study participants learned about the project through family members, neighbors, acquaintance, or friends. The eligibility criteria for participation were: 1) age ≥ 60 years;2) self identified as Chinese; and 3) community-dwelling older adults. Out of 3,542 eligible older adults responding to our multiple recruitment channels, 3,159 (91.9%) agreed to participate in the study
Our bilingual research team translated the scales into Chinese and back translated it into English. The translations were further scrutinized by investigators to ensure content and face validity. The participant signed a consent form approved by the Institutional Review Board of the Rush University Medical Center prior to the interview. Trained multicultural and multilingual interviewers conducted face-to-face home interviews with participants in their preferred language and dialects, such as English, Cantonese, Taishanese, Mandarin, or Teochew dialect. Data were collected using state-of-science innovative web-based software which recorded simultaneously in English, Chinese traditional and simplified characters. This transformative technological platform minimized any information that may have been “lost in translation”, thus providing deeper meaning to the data collected.
Basic demographic information included age (in years), sex (female and male), education (years of education completed), personal income (0-5,000-10,000-15,000-20,000 per year), marital status (married, separated, divorced, widowed, or never married), number of children and living arrangement (living alone, living with 1 person, living with 2-3 persons, or living with 4 or more persons). Number of years in the community and years in the U.S., and country of origin were also assessed in all participants.
Overall health status was measured by “In general, how would you rate your health?” on a four-point scale (1 = poor, 2 = fair, 3 = good, 4 = very good). Quality of life was assessed by asking “In general, how would you rate your quality of life?” also on a four-point scale ranging from 1= poor to 4 = very good. Health changes over the last year was measured by “Compared to one year ago, how would you rate your health now?” on a three-point scale (1 = worsened, 2 = same, 3 = improved).
We used the Modified South Oaks Gambling Screen (MSOGS) to assess the frequency and types of gambling. Older adults were asked how often they played the following types of gambling: (1) purchase state lottery tickets; (2) bet money on Mahjong or card games; (3) bet money on sport games; (4) go to casino; (5) play slot machines or bet money on video poker. Answers ranged from 0 = “never” to 6 = “once per week. ”
Participants were first asked whether or not they had ever gambled in the past 12 months. Among those who had gambled in the past twelve months, the 9-item Problem Gambling Severity Index (PGSI), derived from the 31-item Canadian Problem Gambling Index (CPGI), was utilized to measure problem gambling [29]. Respondents indicated answers to each question on a four-point scale ranging from 0 =“never”to 3 = “almost always. ”The total score ranges from 0 to 27, with a score of 0 indicating non-problem gambling and a score of ≥ 1 indicating any risk of problem gambling [29]. The PGSI has been validated among Chinese Americans and has good inter-rater reliability [30]. The standardized Cronbach’s alpha of PGSI in the PINE study was 0.81.
Descriptive analyses were used to calucate the frequency of gambling activities and problems. Chi-square analysis was conducted to compare the socio-demographic and health-related charateristics of older adults who have participated in gambling in the past twelve months and those who have not. Fisher’s exact test was then conducted to compare the socio-demographic and health-related charateristics of gamblers with and without any risk of problem gambling.Pearson Correlation Coefficients and Spearman Coefficients were used to calculate the correlates of gambling and problem gambling. Statistical analysis was consucted, using SAS, Version 9.2 (SAS Institute, Inc, Cary, NC).
In total, 3,159 older Chinese adults participated in the study, of which 58.9% were women and the average age was 72.8 years (SD = 8.3, range = 60-105). Overall, 467 (14.8%) older adults had engaged in gambling in the past twelve months. Table 1 shows the frequencies of different types of gambling activities. Visiting a casino was the most commonly reported form of gambling activities. Betting on Mahjong or card games had the highest frequency, with 8.8% of the older adults betting once per week and 1.9% betting once per month.
|
Never, N (%) |
Once per 2 years, N (%) |
Once per 1 year, N (%) |
Once per 6 months, N (%) |
Once per 3 months, N (%) |
Once per month, N (%) |
Once per week, N (%) |
|
| 1. State lottery tickets | 2785 (88.6) | 82 (2.6) | 60 (1.9) | 41 (1.3) | 53 (1.7) | 69 (2.2) | 53 (1.7) |
| 2. Mahjong or card games | 2692 (85.7) | 53 (1.7) | 21 (0.7) | 22 (0.7) | 21 (0.7) | 58 (1.9) | 276 (8.8) |
| 3. Sports games | 3123 (99.4) | 8 (0.3) | 0 (0.0) | 0 (0.0) | 1 (0.03) | 3 (0.1) | 6 (0.2) |
| 4. Casino | 2671 (85.0) | 181 (5.8) | 62 (2.0) | 0 (0.0) | 38 (1.2) | 70 (2.2) | 120 (3.8) |
| 5. Slot machines or bet money on video poker | 2836 (90.3) | 110 (3.5) | 35 (1.1) | 32 (1.0) | 23 (0.7) | 56 (1.8) | 49 (1.6) |
DownLoad: CSVTable 2 presents the sociodemographic and health-related characteristics of older adults who had engaged in gambling and those who had not in the past twelve months. Compared with older adults who had not gambled, older adults who had participated in gambling in the past twelve months were more likely to be male, with 0-6 educational levels, with an annual income level that was higher than $10, 000, living in the U.S. for more than 20 years, living in the community for more than 20 years, having 3 or more children, having good or very good health status, perceiving fair quality of life, and reporting the same level of health as compared to that of last year.
|
Gamblers N = 467 |
Non-Gamblers N = 2, 675 |
p-value | ||
| Age, N (%) | ||||
| 60-64 | 111 (23.8) | 568 (21.2) | ||
| 65-69 | 101 (21.6) | 540 (20.2) | ||
| 70-74 | 83 (17.8) | 522 (19.5) | ||
| 75-79 | 78 (16.7) | 473 (17.7) | ||
| 80 and over | 94 (20.1) | 572 (21.4) | 2.7 | 0.605 |
| Gender, N (%) | ||||
| Female | 236 (50.5) | 1589 (59.4) | ||
| Male | 231 (49.5) | 1086 (40.6) | 12.8 | < 0.001 |
| Education (years), N (%) | ||||
| 0-6 | 232 (49.8) | 1135 (42.6) | ||
| 7-12 | 187 (40.1) | 915 (34.3) | ||
| 13 and over | 47 (10.1) | 616 (23.1) | 40.3 | < 0.001 |
| Income (USD), N (%) | ||||
| 4,999 | 114 (24.5) | 925 (34.9) | ||
| 9,999 | 266 (57.2) | 1346 (50.8) | ||
| 14,999 | 55 (11.8) | 255 (9.6) | ||
| 19,999 | 11 (2.4) | 57 (2.2) | ||
| $20,000 and over | 19 (4.1) | 68 (2.6) | 21.2 | < 0.001 |
| Marital Status, N (%) | ||||
| Married | 326 (70) | 1898 (71.1) | ||
| Separated | 12 (2.6) | 45 (1.7) | ||
| Divorced | 17 (3.7) | 56 (2.1) | ||
| Widowed | 107 (23) | 659 (24.7) | ||
| Never Married | 4 (0.9) | 12 (0.5) | 7.7 | 0.104 |
| Number of Children, N (%) | ||||
| 0 | 20 (4.3) | 108 (4) | ||
| 1-2 | 164 (35) | 1104 (41.4) | ||
| 3 or more | 282 (60.5) | 1458 (54.6) | 2 | 0.043 |
| Living Arrangement, N (%) | ||||
| 0 | 115 (24.6) | 560 (21) | ||
| 1 | 181 (38.8) | 1126 (42.1) | ||
| 2-3 | 80 (17.1) | 399 (14.9) | ||
| 4 or more | 91 (19.5) | 589 (22) | 6 | 0.110 |
| Years in the U.S., N (%) | ||||
| 0-10 | 58 (12.5) | 784 (29.4) | ||
| 11-20 | 150 (32.3) | 806 (30.3) | ||
| 21-30 | 123 (26.5) | 640 (24) | ||
| 31 and more | 133 (28.7) | 434 (16.3) | 77 | < 0.001 |
| Years in the Community, N (%) | ||||
| 0-10 | 212 (45.7) | 1588 (60) | ||
| 11-20 | 129 (27.8) | 608 (23) | ||
| 21-30 | 80 (17.2) | 306 (11.5) | ||
| 31 and more | 43 (9.3) | 166 (6.2) | 33.6 | < 0.001 |
| Origin, N (%) | ||||
| China | 423 (90.6) | 2490 (93.1) | ||
| Hong Kong and Macau | 21 (4.5) | 83 (3.1) | ||
| Taiwan | 5 (1.1) | 37 (1.4) | ||
| Other | 18 (3.9) | 65 (2.4) | 6 | 0.114 |
| Overall Health Status, N (%) | ||||
| Very good | 17 (3.6) | 122 (4.6) | ||
| Good | 194 (41.5) | 897 (33.5) | ||
| Fair | 192 (41.1) | 1124 (42) | ||
| Poor | 64 (13.7) | 532 (19.9) | 16.2 | < 0.001 |
| Quality of Life, N (%) | ||||
| Very good | 18 (3.9) | 196 (7.3) | ||
| Good | 193 (41.3) | 1186 (44.4) | ||
| Fair | 248 (53.1) | 1198 (44.8) | ||
| Poor | 8 (1.7 ) | 93 (3.4) | 17.7 | < 0.001 |
| Health Changes Over the Last Year, N (%) | ||||
| Improved | 37 (8.0) | 238 (8.9) | ||
| Same | 253 (54.0) | 1276 (47.7) | ||
| Worsened | 177 (38.0) | 1159 (43.4) | 6.6 | 0.037 |
DownLoad: CSVAmong older adults who had participated in gambling in the past twelve months, 65 (13.9%) had experienced any risk of problem gambling. Table 3 presents the prevalence of each item of the PGSI among the participants. Of those who had participated in gambling in the past twelve months, 8.8% had been criticized about their gambling or been told that they had a gamble problem and 6.9% had gone back another day to try to win back money, representing the first two common gambling problems. Compared with older adults without any risk of problem gambling, those with any problem gambling risks were more likely to be male, with an annual income less than $5,000, and with worsening health over the past year.
|
Never, N (%) |
Sometimes, N (%) |
Most of the time, N (%) |
Almost Always, N (%) |
|
| 1. Have you bet more than you could really afford to lose? | 463 (99.1) | 3 (0.6) | 1 (0.2) | 0 (0.0) |
| 2. Have you needed to gamble with larger amounts of money to get the same feeling of excitement? | 458 (98.1) | 8 (1.7) | 1 (0.2) | 0 (0.0) |
| 3. When you gambled, did you go back another day to try to win back the money you lost? | 435 (93.2) | 20 (4.3) | 5 (1.1) | 7 (1.5) |
| 4. Have you borrowed money or sold anything to get money to gamble? | 463 (99.1) | 3 (0.6) | 1 (0.2) | 0 (0.0) |
| 5. Have you felt that you might have a problem with gambling? | 447 (95.7) | 15 (3.2) | 2 (0.4) | 3 (0.6) |
| 6. Has gambling caused you any health problems, including stress and or anxiety? | 450 (96.4) | 11 (2.4) | 4 (0.9) | 2 (0.4) |
| 7. Have people criticized your betting or told you that you had a gambling problem, regardless of whether or not you thought it was true? | 425 (91.2) | 20 (4.3) | 8 (1.7) | 13 (2.8) |
| 8. Has your gambling caused any financial problems for you or your household? | 456 (97.6) | 6 (1.3) | 2 (0.4) | 3 (0.6) |
| 9. Have you felt guilty about the way you gamble or what happens when you gamble? | 447 (95.7) | 12 (2.6) | 4 (0.9) | 4 (0.9) |
DownLoad: CSVCorrelates of gambling and problem gambling are presented in Table4. Being male (r = 0.06, p < 0.001), lower educational levels (r = 0.08, p < 0.001), higher income levels (r = 0.06, p < 0.01), having more children (r = 0.05, p < 0.01), living in the U.S. for a longer period of time (r = 0.16, p < 0.001), living in the community for a longer period of time (r = 0.10, p < 0.001), better health status(r = 0.05, p < 0.01), lower quality of life (r = 0.04, p< 0.05), and improved health over the past year (r = 0.04, p< 0.05) were significantly correlated with any gambling in the past year.
| *p < 0.05; **p < 0.01; ***p < 0.001
Notes: Edu = Education; Children = Number of Children; MS = Marital Status; Living = Living Arrangement; Yrs in U.S. = Years in the US; Yrs in com = Years in the Community; Origin = Country of Origin; OHS = Overall Health Status; QOL = Quality of Life; HC = Health Changes over the Last Year; PG = any gambling related problems | |||||||||||||||
| Age | Sex | Edu | Income | MS | Living | Children | Yrs in U.S. | Yrs in com | Origin | OHS | QOL | HC | Gambling | PG | |
| Age | 1.0 | ||||||||||||||
| Sex | 0.01 | 1.0 | |||||||||||||
| Edu | −0.12*** | −0.21*** | 1.0 | ||||||||||||
| Income | 0.05** | 0.00 | 0.01 | 1.0 | |||||||||||
| MS | −0.33*** | −0.32*** | 0.22 | −0.03 | 1.0 | ||||||||||
| Living | −0.35*** | −0.07*** | 0.02 | 0.16*** | 0.24 *** | 1.0 | |||||||||
| Children | 0.32*** | 0.09 *** | −0.38*** | 0.00 | −0.13*** | −0.07*** | 1.0 | ||||||||
| Yrs in U.S. | 0.35 *** | 0.03 | −0.10*** | 0.35*** | −0.2*** | −0.31*** | 0.15*** | 1.0 | |||||||
| Yrs in com | 0.23 *** | 0.02 | −0.11*** | 0.24*** | −0.13*** | −0.18*** | 0.10 *** | 0.66*** | 1.0 | ||||||
| Origin | 0.04* | −0.01 | −0.08*** | −0.20 | 0.05 ** | 0.05** | 0.04* | −0.2*** | −0.15*** | 1.0 | |||||
| OHS | −0.08*** | −0.06 ** | 0.06*** | 0.12*** | 0.05** | −0.00 | −0.00 | −0.01 | 0.05* | −0.03 | 1.0 | ||||
| QOL | 0.06*** | 0.05** | 0.09*** | 0.08*** | 0.03 | −0.01 | 0.04* | 0.00 | −0.02 | −0.04* | 0.32*** | 1.0 | |||
| HC | −0.11*** | −0.03 | 0.02 | 0.05** | 0.07*** | 0.01 | −0.02 | −0.04* | 0.03 | 0.00 | 0.35*** | 0.15*** | 1.0 | ||
| Gambling | −0.03 | −0.06*** | −0.08*** | 0.06** | −0.01 | −0.01 | 0.05** | 0.16*** | 0.10*** | −0.03 | 0.05** | −0.04* | 0.04* | 1.00 | |
| PG | −0.09* | −0.23*** | 0.04 | −0.06 | 0.05 | 0.11* | −0.06 | −0.02 | −0.00 | 0.07 | −0.05 | −0.09 | 0.03 | 1.00 | 1.00 |
DownLoad: CSVOn the other hand, younger age (r = 0.09, p < 0.05), being male (r = 0.23, p < 0.001), and living with more people (r = 0.11, p < 0.05) were significantly correlated with a higher levels of experiencing any risk of problem gambling in the past year.
As one of the largest epidemiology study examining social and psychological well-being among Chinese older adults, this study sheds light on gambling participation and problem gambling among Chinese older adults in the U.S. The study demonstrated that visiting a casino was the most common gambling activity among Chinese older adults, whereas betting money on Mahjong or card games had the highest frequency. Being male, lower educational levels, higher income levels, having more children, living in the U.S. for a longer period of time, living in the community for a longer period of time, better health status, lower quality of life, and improved health over the past year were significantly correlated with participating in gambling in the past twelve months. Younger age, being male, and living with more people were correlated with experiencing any risk of problem gambling.
In this study, 14.8% of the older adults had participated in gambling in the past twelve months. Surprisingly, the prevalence of gambling participation in Chinese older adults was lower than that of other racial/ethnic groups. In a random sample of 810 older adults aged 55 years and older in Canada, 47.5% had gambled in the past year [31]. In a study of 1, 512 older adults aged 62 and above in Oregon, 58.2% reported gambling in the past twelve months [32]. We suspect that because of cultural influences, many of Chinese older adults, especially new immigrants, may be unfamiliar with traditional gambling activities (e.g., bingo) that are popular among U.S. older adults. Due to limited gambling options, immigrant older adults were less likely to involve in gambling in the past year. The comparison of prevalence figures of gambling across different racial/ethnic groups should be interpreted cautiously because of differences in research methodologies and settings among studies.
Visiting a casino was the most commonly reported gambling activity among Chinese older adults. This may be due to the marketing strategies employed by casinos, such as free transportation, cheap buffets, gambling coupons, and other discounts. U.S. Chinese older adults often confront transportation and language barriers when seeking social activities, but the free bus ride and the bilingual staff offered by the casinos may address older adults’ concerns and enhance their motivation for visiting the casino. On the other hand, betting on Mahjong or card games was the most frequent gambling activity. Mahjong is also reported to be a predominate form of gambling activities among 2,272 Chinese older adults aged 55 years and older in Canada [33]. As a culturally specific form of gambling, Mahjong, is originated from China and has become a wide spread form of gambling in Chinese society; it is often played by four people who are always families, friends, and neighbors. Playing Mahjong provides older adults opportunities to interact with family members and friends, while at the same time, may give rise to conflicts and disputes among the gamblers and affect older adults’ social relationship. Given the high frequency of playing Mahjong or card games among older adults, future studies should explore the health outcomes of playing Mahjong.
In this study, older Chinese men were more likely to report gambling in the past twelve months than women. The finding is in contrast to a study of 2,768 U.S. adults aged 63 years and over, which found that the percentage of female gamblers was higher than that of male gamblers [34]. We suspect that compared with women, men may have a wider scope of gambling options, increasing their likelihood of gambling. In addition, exiting social activities for women may be more diverse than that for men. Many older Chinese women have involved in community social activities such as dancing and singing, which are less likely to attract men. Gambling may therefore become the main outlet for social engagement in older men due to a lack of diverse social activities.
Interestingly, living in the U.S. for a longer period of time was associated with increased gambling participation among the participants. Older adults who have been in the U.S. longer tend to be more familiar with the available gambling resources, and thus are more likely to engage in gambling activities. In addition, older adults who live in the U.S. for a longer period of time may have higher financial stability, thereby increasing their likelihoods of gambling. This finding is consistent with a study of gambling participation among Mexican immigrants in New York City, which found that immigrants who had lived in the U.S. longer were more likely to report gambling [35]. Another intriguing finding was that better health status and improved health over the past year were correlated with increased gambling participation. Gambling may improve immigrant older adults’ health status through increasing social engagement and reducing loneliness. However, it should be mentioned that, due to the cross-sectional nature of the present study, we were unable to determine the causal relationship between gambling and health. It may be that healthier older adults were more active in social activities, and therefore were more likely to participate in gambling. Future studies are needed to elucidate the mechanisms through which gambling links with health status,
With respect to problem gambling, the study demonstrated being criticized is the most frequently endorsed gambling problem, followed by going back to win money. This finding is consistent with a study of older adults in Ontario, which found that being criticized by others and going back to win money were commonly reported gambling problems [36]. In Asian culture, chasing losses can be seen as a way of blocking shame. Asians believe that they could lose the respect of family and peers and think it is their responsibility to take control over their gambling issues [16,37]. This could explain why they would perhaps go back another day to try to win back the money that they lost, rather than confront the issue, with support from family members. When comparing the socioeconomic profiles of problem gamblers and non-problem gamblers, we found that older adults at risk for problem gambling had a higher percentage of whom with an annual income level lower than $5,000 than the non-problem gamblers. In a telephone interview of 1,121 adults aged 15 to 64 years in Macao, China, adults with lower income reported a significant higher risk for problem and pathological gambling [38]. Older adults with lower income may be less capable of recovering losses, and thus are more likely to feel guilty by losing money gambling or be criticized by gambling.
While this study has improved our understanding of gambling among Chinese older adults, interpretations of the findings should consider limitations. First, because this study only examined selective types of gambling activities; there may be other gambling activities that have not been captured by the measure. Second, this study was designed as a descriptive study, which may be subject to statistical limitations. Future studies should conduct more rigorous analysis to delineate the association between gambling and physical and mental health among Chinese older adults. Third, due to cultural stigma on problem gambling and the use of self-report approach, there may be reporting bias in the present study. However, the utilization of the community-based participatory research approach may improve the trust of the participants and the research assistants, and thus help reduce potential reporting bias. Fourth, this study employed a cross-sectional design, and we were unable to make causal inferences. Future longitudinal studies are required to better examine factors associated with problem gambling. Finally, the study was conducted among Chinese older adults in the greater Chicago area, and therefore our findings may not be generalizable to Chinese aging populations in other areas.
Despite the limitations, this study has important research and policy implications. Research should continue to explore factors associated with gambling and problem gambling among Chinese older adults so as to facilitate the development of gambling prevention and intervention programs. Community organizations should improve the availability of community-based cultural competent mental health services for older adults at risk for program gambling; they should also increase education and awareness of problem gambling among older adults and their family members, especially among male and younger adults.
This study found that 14.8% of the older adults had participated in gambling in the past twelve months, and more than one in ten gamblers had experienced any risk of problem gambling. Visiting a casino was the most commonly reported type of gambling and playing Mahjong was the most frequent gambling activity. Gambling participation and problems were correlated with various socio-demographic and health related characteristics. Future longitudinal studies are warranted to better explore factors associated with gambling and problem gambling among Chinese older adults in the U.S.
The authors declare no conflict of interest.
| [1] | Global, regional, and national burden of Parkinson's disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol (2018) 17: 939-953. |
| [2] | Movement Disorders: Volume 35, Number S1, September 2020. Mov Disord (2020) 35:. https://doi.org/10.1002/mds.28267 |
| [3] |
Ascherio A, Schwarzschild MA (2016) The epidemiology of Parkinson's disease: risk factors and prevention. Lancet Neurol 15: 1257-1272. https://doi.org/10.1016/S1474-4422(16)30230-7 
|
| [4] |
Kim CY, Alcalay RN (2017) Genetic Forms of Parkinson's Disease. Semin Neurol 37: 135-146. https://doi.org/10.1055/s-0037-1601567
|
| [5] |
Dauer W, Przedborski S (2003) Parkinson's disease: mechanisms and models. Neuron 39: 889-909. https://doi.org/10.1016/S0896-6273(03)00568-3
|
| [6] |
Braak H, Del Tredici K, Bratzke H, et al. (2002) Staging of the intracerebral inclusion body pathology associated with idiopathic Parkinson's disease (preclinical and clinical stages). J Neurol 249 Suppl 3: Iii/1-5. https://doi.org/10.1007/s00415-002-1301-4
|
| [7] |
Jankovic J (2008) Parkinson's disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79: 368-376. https://doi.org/10.1136/jnnp.2007.131045
|
| [8] |
Langston JW (2006) The parkinson's complex: Parkinsonism is just the tip of the iceberg. Ann Neurol 59: 591-596. https://doi.org/10.1002/ana.20834
|
| [9] |
Postuma RB, Berg D, Stern M, et al. (2015) MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord 30: 1591-1601. https://doi.org/10.1002/mds.26424
|
| [10] |
Langston JW (1990) Predicting Parkinson's disease. Neurology 40: suppl 70-74; discussion 75–76.
|
| [11] |
Braak H, Del Tredici K, Rüb U, et al. (2003) Staging of brain pathology related to sporadic Parkinson's disease. Neurobiol Aging 24: 197-211. https://doi.org/10.1016/S0197-4580(02)00065-9
|
| [12] |
Bellingham S, Guo B, Coleman B, et al. (2012) Exosomes: Vehicles for the Transfer of Toxic Proteins Associated with Neurodegenerative Diseases?. Front Physiol 3. https://doi.org/10.3389/fphys.2012.00124
|
| [13] |
Guo M, Wang J, Zhao Y, et al. (2020) Microglial exosomes facilitate α-synuclein transmission in Parkinson's disease. Brain 143: 1476-1497. https://doi.org/10.1093/brain/awaa090
|
| [14] |
Yang T, Martin P, Fogarty B, et al. (2015) Exosome delivered anticancer drugs across the blood-brain barrier for brain cancer therapy in Danio rerio. Pharm Res 32: 2003-2014. https://doi.org/10.1007/s11095-014-1593-y
|
| [15] |
Goetzl EJ, Boxer A, Schwartz JB, et al. (2015) Altered lysosomal proteins in neural-derived plasma exosomes in preclinical Alzheimer disease. Neurology 85: 40-47. https://doi.org/10.1212/WNL.0000000000001702
|
| [16] |
Pardridge WM (2012) Drug transport across the blood–brain barrier. J Cerebr Blood F Met 32: 1959-1972. https://doi.org/10.1038/jcbfm.2012.126
|
| [17] |
Qu M, Lin Q, Huang L, et al. (2018) Dopamine-loaded blood exosomes targeted to brain for better treatment of Parkinson's disease. J Control Release 287: 156-166. https://doi.org/10.1016/j.jconrel.2018.08.035
|
| [18] |
Haney MJ, Klyachko NL, Zhao Y, et al. (2015) Exosomes as drug delivery vehicles for Parkinson's disease therapy. J Control Release 207: 18-30. https://doi.org/10.1016/j.jconrel.2015.03.033
|
| [19] |
Cooper JM, Wiklander PO, Nordin JZ, et al. (2014) Systemic exosomal siRNA delivery reduced alpha-synuclein aggregates in brains of transgenic mice. Mov Disord 29: 1476-1485. https://doi.org/10.1002/mds.25978
|
| [20] |
Guy R, Offen D (2020) Promising opportunities for treating neurodegenerative diseases with mesenchymal stem cell-derived exosomes. Biomolecules 10: 1320. https://doi.org/10.3390/biom10091320
|
| [21] |
Théry C, Zitvogel L, Amigorena S (2002) Exosomes: composition, biogenesis and function. Nat Rev Immunol 2: 569-579. https://doi.org/10.1038/nri855
|
| [22] |
Akbari-Gharalari N, Khodakarimi S, Nezhadshahmohammad F, et al. (2024) Exosomes in neuron-glia communication: A review on neurodegeneration. Bioimpacts 14: 30153-30153. https://doi.org/10.34172/bi.2023.30153
|
| [23] |
Kalluri R, LeBleu VS (2020) The biology, function, and biomedical applications of exosomes. Science 367. https://doi.org/10.1126/science.aau6977
|
| [24] | Omrani M, Beyrampour-Basmenj H, Jahanban-Esfahlan R, et al. (2023) Global trend in exosome isolation and application: an update concept in management of diseases. Mol Cell Biochem : 1-13. https://doi.org/10.1007/s11010-023-04756-6 |
| [25] |
Lachenal G, Pernet-Gallay K, Chivet M, et al. (2011) Release of exosomes from differentiated neurons and its regulation by synaptic glutamatergic activity. Mol Cell Neurosci 46: 409-418. https://doi.org/10.1016/j.mcn.2010.11.004
|
| [26] |
Wubbolts R, Leckie RS, Veenhuizen PT, et al. (2003) Proteomic and biochemical analyses of human B cell-derived exosomes. Potential implications for their function and multivesicular body formation. J Biol Chem 278: 10963-10972. https://doi.org/10.1074/jbc.M207550200
|
| [27] |
van Meer G, Voelker DR, Feigenson GW (2008) Membrane lipids: where they are and how they behave. Nat Rev Mol Cell Biol 9: 112-124. https://doi.org/10.1038/nrm2330
|
| [28] |
Zhang Y, Bi J, Huang J, et al. (2020) Exosome: A Review of Its Classification, Isolation Techniques, Storage, Diagnostic and Targeted Therapy Applications. Int J Nanomedicine 15: 6917-6934. https://doi.org/10.2147/IJN.S264498
|
| [29] |
Skokos D, Le Panse S, Villa I, et al. (2001) Mast cell-dependent B and T lymphocyte activation is mediated by the secretion of immunologically active exosomes. J Immunol 166: 868-876. https://doi.org/10.4049/jimmunol.166.2.868
|
| [30] |
Chen G, Huang AC, Zhang W, et al. (2018) Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature 560: 382-386. https://doi.org/10.1038/s41586-018-0392-8
|
| [31] |
Valadi H, Ekström K, Bossios A, et al. (2007) Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol 9: 654-659. https://doi.org/10.1038/ncb1596
|
| [32] |
Sharma A, Johnson A (2020) Exosome DNA: Critical regulator of tumor immunity and a diagnostic biomarker. J Cell Physiol 235: 1921-1932. https://doi.org/10.1002/jcp.29153
|
| [33] |
Harding CV, Heuser JE, Stahl PD (2013) Exosomes: looking back three decades and into the future. Cell Biol 200: 367-371. https://doi.org/10.1083/jcb.201212113
|
| [34] |
Pravin DP, Aashutosh US (2016) Molecular Biomarkers for Diagnosis & Therapies of Alzheimer's Disease. AIMS Neurosci 3: 433-453. https://doi.org/10.3934/Neuroscience.2016.4.433
|
| [35] |
Yakubovich E, Polischouk A, Evtushenko V (2022) Principles and problems of exosome isolation from biological fluids. Biochem Mosc Suppl S 16: 115-126. https://doi.org/10.1134/S1990747822030096
|
| [36] |
Kučuk N, Primožič M, Knez Ž, et al. (2021) Exosomes engineering and their roles as therapy delivery tools, therapeutic targets, and biomarkers. Int J Mol Sci 22: 9543. https://doi.org/10.3390/ijms22179543
|
| [37] |
Budnik V, Ruiz-Cañada C, Wendler F (2016) Extracellular vesicles round off communication in the nervous system. Nat Rev Neurosci 17: 160-172. https://doi.org/10.1038/nrn.2015.29
|
| [38] |
Desplats P, Lee HJ, Bae EJ, et al. (2009) Inclusion formation and neuronal cell death through neuron-to-neuron transmission of alpha-synuclein. Proc Natl Acad Sci U S A 106: 13010-13015. https://doi.org/10.1073/pnas.0903691106
|
| [39] |
Crews L, Spencer B, Desplats P, et al. (2010) Selective Molecular Alterations in the Autophagy Pathway in Patients with Lewy Body Disease and in Models of α-Synucleinopathy. PLOS ONE 5: e9313. https://doi.org/10.1371/journal.pone.0009313
|
| [40] |
Minakaki G, Menges S, Kittel A, et al. (2018) Autophagy inhibition promotes SNCA/alpha-synuclein release and transfer via extracellular vesicles with a hybrid autophagosome-exosome-like phenotype. Autophagy 14: 98-119. https://doi.org/10.1080/15548627.2017.1395992
|
| [41] |
Delenclos M, Trendafilova T, Mahesh D, et al. (2017) Investigation of Endocytic Pathways for the Internalization of Exosome-Associated Oligomeric Alpha-Synuclein. Front Neurosci 11. https://doi.org/10.3389/fnins.2017.00172
|
| [42] |
Huang Y, Liu Z, Li N, et al. (2022) Parkinson's Disease Derived Exosomes Aggravate Neuropathology in SNCA*A53T Mice. Ann Neurol 92: 230-245. https://doi.org/10.1002/ana.26421
|
| [43] |
Han C, Xiong N, Guo X, et al. (2019) Exosomes from patients with Parkinson's disease are pathological in mice. J Mol Med (Berl) 97: 1329-1344. https://doi.org/10.1007/s00109-019-01810-z
|
| [44] | Sano K, Atarashi R, Satoh K, et al. (2018) Prion-Like Seeding of Misfolded α-Synuclein in the Brains of Dementia with Lewy Body Patients in RT-QUIC. Mol Neurobiol 55: 3916-3930. https://doi.org/10.1007/s12035-017-0624-1 |
| [45] |
Danzer KM, Kranich LR, Ruf WP, et al. (2012) Exosomal cell-to-cell transmission of alpha synuclein oligomers. Mol Neurodegener 7: 42. https://doi.org/10.1186/1750-1326-7-42
|
| [46] |
Stuendl A, Kunadt M, Kruse N, et al. (2016) Induction of α-synuclein aggregate formation by CSF exosomes from patients with Parkinson's disease and dementia with Lewy bodies. Brain 139: 481-494. https://doi.org/10.1093/brain/awv346
|
| [47] |
Herman S, Djaldetti R, Mollenhauer B, et al. (2022) CSF-derived extracellular vesicles from patients with Parkinson's disease induce symptoms and pathology. Brain 146: 209-224. https://doi.org/10.1093/brain/awac261
|
| [48] | Yakabi K, Berson E, Montine KS, et al. (2023) Human cerebrospinal fluid single exosomes in Parkinson's and Alzheimer's diseases. bioRxiv : 2023.2012.2022.573124. https://doi.org/10.1101/2023.12.22.573124 |
| [49] |
Shi M, Liu C, Cook TJ, et al. (2014) Plasma exosomal α-synuclein is likely CNS-derived and increased in Parkinson's disease. Acta Neuropathol 128: 639-650. https://doi.org/10.1101/2023.12.22.573124
|
| [50] |
Kenwrick S, Watkins A, Angelis ED (2000) Neural cell recognition molecule L1: relating biological complexity to human disease mutations. Hum Mol Genet 9: 879-886. https://doi.org/10.1093/hmg/9.6.879
|
| [51] |
Niu M, Li Y, Li G, et al. (2020) A longitudinal study on α-synuclein in plasma neuronal exosomes as a biomarker for Parkinson's disease development and progression. Eur J Neurol 27: 967-974. https://doi.org/10.1111/ene.14208
|
| [52] |
Yan YQ, Pu JL, Zheng R, et al. (2022) Different patterns of exosomal α-synuclein between Parkinson's disease and probable rapid eye movement sleep behavior disorder. Eur J Neurol 29: 3590-3599. https://doi.org/10.1111/ene.15537
|
| [53] |
Postuma RB, Gagnon JF, Bertrand JA, et al. (2015) Parkinson risk in idiopathic REM sleep behavior disorder: preparing for neuroprotective trials. Neurology 84: 1104-1113. https://doi.org/10.1212/WNL.0000000000001364
|
| [54] | Shim KH, Go HG, Bae H, et al. (2021) Decreased Exosomal Acetylcholinesterase Activity in the Plasma of Patients With Parkinson's Disease. Front Aging Neurosci 13. https://doi.org/10.3389/fnagi.2021.665400 |
| [55] |
Si X, Tian J, Chen Y, et al. (2019) Central Nervous System-Derived Exosomal Alpha-Synuclein in Serum May Be a Biomarker in Parkinson's Disease. Neuroscience 413: 308-316. https://doi.org/10.1016/j.neuroscience.2019.05.015
|
| [56] | Kluge A, Bunk J, Schaeffer E, et al. (2022) Correction to: Detection of neuron-derived pathological α-synuclein in blood. Brain 146: e6-e6. https://doi.org/10.1093/brain/awac370 |
| [57] |
Zheng H, Xie Z, Zhang X, et al. (2021) Investigation of α-Synuclein Species in Plasma Exosomes and the Oligomeric and Phosphorylated α-Synuclein as Potential Peripheral Biomarker of Parkinson's Disease. Neuroscience 469: 79-90. https://doi.org/10.1016/j.neuroscience.2021.06.033
|
| [58] |
Zhentang C, Yufeng W, Genliang L, et al. (2019) α-Synuclein in salivary extracellular vesicles as a potential biomarker of Parkinson's disease. Neurosci Lett 696: 114-120. https://doi.org/10.1016/j.neulet.2018.12.030
|
| [59] |
Rani K, Mukherjee R, Singh E, et al. (2019) Neuronal exosomes in saliva of Parkinson's disease patients: A pilot study. Parkinsonism Relat D 67: 21-23. https://doi.org/10.1016/j.parkreldis.2019.09.008
|
| [60] |
Kang W, Chen W, Yang Q, et al. (2016) Salivary total α-synuclein, oligomeric α-synuclein and SNCA variants in Parkinson's disease patients. Sci Rep 6: 28143. https://doi.org/10.1038/srep28143
|
| [61] |
Shu H, Zhang P, Gu L (2024) Alpha-synuclein in peripheral body fluid as a biomarker for Parkinson's disease. Acta Neurol Belg 124: 831-842. https://doi.org/10.1007/s13760-023-02452-2
|
| [62] |
Clements CM, McNally RS, Conti BJ, et al. (2006) DJ-1, a cancer- and Parkinson's disease-associated protein, stabilizes the antioxidant transcriptional master regulator Nrf2. Proc Natl Acad Sci U S A 103: 15091-15096. https://doi.org/10.1073/pnas.0607260103
|
| [63] | Jang J, Jeong S, Lee SI, et al. (2018) Oxidized DJ-1 levels in urine samples as a putative biomarker for Parkinson's disease. Parkinsons Dis 2018. https://doi.org/10.1155/2018/1241757 |
| [64] |
An C, Pu X, Xiao W, et al. (2018) Expression of the DJ-1 protein in the serum of Chinese patients with Parkinson's disease. Neurosci Lett 665: 236-239. https://doi.org/10.1016/j.neulet.2017.12.023
|
| [65] |
Waragai M, Nakai M, Wei J, et al. (2007) Plasma levels of DJ-1 as a possible marker for progression of sporadic Parkinson's disease. Neurosci Lett 425: 18-22. https://doi.org/10.1016/j.neulet.2007.08.010
|
| [66] |
T. dos Santos MC, Scheller D, Schulte C, et al. (2018) Evaluation of cerebrospinal fluid proteins as potential biomarkers for early stage Parkinson's disease diagnosis. PLOS ONE 13: e0206536. https://doi.org/10.1371/journal.pone.0206536
|
| [67] |
Zhao Z-H, Chen Z-T, Zhou R-L, et al. (2019) Increased DJ-1 and α-synuclein in plasma neural-derived exosomes as potential markers for Parkinson's disease. Front Aging Neurosci 10: 438. https://doi.org/10.3389/fnagi.2018.00438
|
| [68] |
Gonzales PA, Pisitkun T, Hoffert JD, et al. (2009) Large-Scale Proteomics and Phosphoproteomics of Urinary Exosomes. J Am Soc Nephrol 20: 363-379. https://doi.org/10.1681/ASN.2008040406
|
| [69] |
Pisitkun T, Shen R-F, Knepper MA (2004) Identification and proteomic profiling of exosomes in human urine. P Natl Acad Sci 101: 13368-13373. https://doi.org/10.1073/pnas.0403453101
|
| [70] | Ho DH, Yi S, Seo H, et al. (2014) Increased DJ-1 in urine exosome of Korean males with Parkinson's disease. Biomed Res Int 2014: 704678. https://doi.org/10.1155/2014/704678 |
| [71] |
Goedert M, Spillantini MG (2006) A century of Alzheimer's disease. Science 314: 777-781. https://doi.org/10.1126/science.1132814
|
| [72] |
Spillantini MG, Goedert M (2013) Tau pathology and neurodegeneration. Lancet Neurol 12: 609-622. https://doi.org/10.1016/S1474-4422(13)70090-5
|
| [73] |
Wills J, Jones J, Haggerty T, et al. (2010) Elevated tauopathy and alpha-synuclein pathology in postmortem Parkinson's disease brains with and without dementia. Exp Neurol 225: 210-218. https://doi.org/10.1016/j.expneurol.2010.06.017
|
| [74] |
Edwards TL, Scott WK, Almonte C, et al. (2010) Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease. Ann Hum Genet 74: 97-109. https://doi.org/10.1111/j.1469-1809.2009.00560.x
|
| [75] |
Simón-Sánchez J, Schulte C, Bras JM, et al. (2009) Genome-wide association study reveals genetic risk underlying Parkinson's disease. Nat Genet 41: 1308-1312. https://doi.org/10.1038/ng.487
|
| [76] |
Vermilyea SC, Christensen A, Meints J, et al. (2022) Loss of tau expression attenuates neurodegeneration associated with α-synucleinopathy. Transl Neurodegener 11: 34. https://doi.org/10.1186/s40035-022-00309-x
|
| [77] |
Shi M, Kovac A, Korff A, et al. (2016) CNS tau efflux via exosomes is likely increased in Parkinson's disease but not in Alzheimer's disease. Alzheimers Dement 12: 1125-1131. https://doi.org/10.1016/j.jalz.2016.04.003
|
| [78] |
Berg D, Schweitzer KJ, Leitner P, et al. (2005) Type and frequency of mutations in the LRRK2 gene in familial and sporadic Parkinson's disease*. Brain 128: 3000-3011. https://doi.org/10.1093/brain/awh666
|
| [79] |
Paisán-Ruíz C, Jain S, Evans EW, et al. (2004) Cloning of the gene containing mutations that cause PARK8-linked Parkinson's disease. Neuron 44: 595-600. https://doi.org/10.1016/j.neuron.2004.10.023
|
| [80] | Rosenbusch KE, Kortholt A (2016) Activation Mechanism of LRRK2 and Its Cellular Functions in Parkinson's Disease. Parkinsons Dis 2016: 7351985. https://doi.org/10.1155/2016/7351985 |
| [81] |
Fraser KB, Moehle MS, Daher JP, et al. (2013) LRRK2 secretion in exosomes is regulated by 14-3-3. Hum Mol Genet 22: 4988-5000. https://doi.org/10.1093/hmg/ddt346
|
| [82] |
Fraser KB, Moehle MS, Alcalay RN, et al. (2016) Urinary LRRK2 phosphorylation predicts parkinsonian phenotypes in G2019S LRRK2 carriers. Neurology 86: 994-999. https://doi.org/10.1212/WNL.0000000000002436
|
| [83] |
Di Maio R, Hoffman EK, Rocha EM, et al. (2018) LRRK2 activation in idiopathic Parkinson's disease. Sci Transl Med 10. https://doi.org/10.1126/scitranslmed.aar5429
|
| [84] |
Fraser KB, Rawlins AB, Clark RG, et al. (2016) Ser(P)-1292 LRRK2 in urinary exosomes is elevated in idiopathic Parkinson's disease. Mov Disord 31: 1543-1550. https://doi.org/10.1002/mds.26686
|
| [85] |
Wang H (2021) MicroRNAs, Parkinson's Disease, and Diabetes Mellitus. Int J Mol Sci 22: 2953. https://doi.org/10.3390/ijms22062953
|
| [86] |
Gui Y, Liu H, Zhang L, et al. (2015) Altered microRNA profiles in cerebrospinal fluid exosome in Parkinson disease and Alzheimer disease. Oncotarget 6: 37043-37053. https://doi.org/10.18632/oncotarget.6158
|
| [87] |
Nie C, Sun Y, Zhen H, et al. (2020) Differential Expression of Plasma Exo-miRNA in Neurodegenerative Diseases by Next-Generation Sequencing. Front Neurosci 14: 438. https://doi.org/10.3389/fnins.2020.00438
|
| [88] | Yao YF, Qu MW, Li GC, et al. (2018) Circulating exosomal miRNAs as diagnostic biomarkers in Parkinson's disease. Eur Rev Med Pharmacol Sci 22: 5278-5283. |
| [89] |
Margis R, Margis R, Rieder CR (2011) Identification of blood microRNAs associated to Parkinsonĭs disease. J Biotechnol 152: 96-101. https://doi.org/10.1016/j.jbiotec.2011.01.023
|
| [90] |
Botta-Orfila T, Morató X, Compta Y, et al. (2014) Identification of blood serum micro-RNAs associated with idiopathic and LRRK2 Parkinson's disease. J Neurosci Res 92: 1071-1077. https://doi.org/10.1002/jnr.23377
|
| [91] |
Vallelunga A, Ragusa M, Di Mauro S, et al. (2014) Identification of circulating microRNAs for the differential diagnosis of Parkinson's disease and Multiple System Atrophy. Front Cell Neurosci 8. https://doi.org/10.3389/fncel.2014.00156
|
| [92] |
Cao XY, Lu JM, Zhao ZQ, et al. (2017) MicroRNA biomarkers of Parkinson's disease in serum exosome-like microvesicles. Neurosci Lett 644: 94-99. https://doi.org/10.1016/j.neulet.2017.02.045
|
| [93] |
Qian X, Zhao J, Yeung PY, et al. (2019) Revealing lncRNA Structures and Interactions by Sequencing-Based Approaches. Trends Biochem Sci 44: 33-52. https://doi.org/10.1016/j.tibs.2018.09.012
|
| [94] |
Wu P, Zuo X, Deng H, et al. (2013) Roles of long noncoding RNAs in brain development, functional diversification and neurodegenerative diseases. Brain Res Bull 97: 69-80. https://doi.org/10.1016/j.brainresbull.2013.06.001
|
| [95] |
Zou J, Guo Y, Wei L, et al. (2020) Long Noncoding RNA POU3F3 and α-Synuclein in Plasma L1CAM Exosomes Combined with β-Glucocerebrosidase Activity: Potential Predictors of Parkinson's Disease. Neurotherapeutics 17: 1104-1119. https://doi.org/10.1007/s13311-020-00842-5
|
| [96] | Boer DEC, van Smeden J, Bouwstra JA, et al. (2020) Glucocerebrosidase: Functions in and Beyond the Lysosome. J Clin Med 9. https://doi.org/10.3390/jcm9030736 |
| [97] |
Fanciulli A, Wenning GK (2015) Multiple-system atrophy. N Engl J Med 372: 249-263. https://doi.org/10.1056/NEJMra1311488
|
| [98] |
Gilman S, Wenning GK, Low PA, et al. (2008) Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71: 670-676. https://doi.org/10.1212/01.wnl.0000324625.00404.15
|
| [99] |
Fanciulli A, Stankovic I, Krismer F, et al. (2019) Multiple system atrophy. Int Rev Neurobiol 149: 137-192. https://doi.org/10.1016/bs.irn.2019.10.004
|
| [100] |
Galvin JE, Lee VM, Trojanowski JQ (2001) Synucleinopathies: clinical and pathological implications. Arch Neurol 58: 186-190. https://doi.org/10.1001/archneur.58.2.186
|
| [101] |
Wenning GK, Ben-Shlomo Y, Hughes A, et al. (2000) What clinical features are most useful to distinguish definite multiple system atrophy from Parkinson's disease?. J Neurol Neurosurg Psychiatry 68: 434-440. https://doi.org/10.1136/jnnp.68.4.434
|
| [102] |
Jiang C, Hopfner F, Katsikoudi A, et al. (2020) Serum neuronal exosomes predict and differentiate Parkinson's disease from atypical parkinsonism. J Neurol Neurosurg Psychiatry 91: 720-729. https://doi.org/10.1136/jnnp-2019-322588
|
| [103] |
Dutta S, Hornung S, Kruayatidee A, et al. (2021) α-Synuclein in blood exosomes immunoprecipitated using neuronal and oligodendroglial markers distinguishes Parkinson's disease from multiple system atrophy. Acta Neuropathol 142: 495-511. https://doi.org/10.1007/s00401-021-02324-0
|
| [104] |
Höglinger GU, Respondek G, Stamelou M, et al. (2017) Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria. Mov Disord 32: 853-864. https://doi.org/10.1002/mds.26987
|
| [105] |
Manna I, Quattrone A, De Benedittis S, et al. (2021) Exosomal miRNA as peripheral biomarkers in Parkinson's disease and progressive supranuclear palsy: A pilot study. Parkinsonism Relat Disord 93: 77-84. https://doi.org/10.1016/j.parkreldis.2021.11.020
|
| [106] |
McKeith IG, Dickson DW, Lowe J, et al. (2005) Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 65: 1863-1872. https://doi.org/10.1212/01.wnl.0000187889.17253.b1
|
| [107] | Lippa CF, Duda JE, Grossman M, et al. (2007) DLB and PDD boundary issues. Diagnosis Treatment Molecular Pathology Biomarkers 68: 812-819. https://doi.org/10.1212/01.wnl.0000256715.13907.d3 |
| [108] |
Bonanni L, Thomas A, Onofrj M (2006) Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 66: 1455; author reply 1455. https://doi.org/10.1212/01.wnl.0000224698.67660.45
|
| [109] |
Emre M, Aarsland D, Brown R, et al. (2007) Clinical diagnostic criteria for dementia associated with Parkinson's disease. Mov Disord 22: 1689-1707; quiz 1837. https://doi.org/10.1002/mds.21507
|
| [110] |
Walker Z, Possin KL, Boeve BF, et al. (2015) Lewy body dementias. Lancet 386: 1683-1697. https://doi.org/10.1016/S0140-6736(15)00462-6
|
| [111] |
Swallow DMA, Counsell CE (2023) The evolution of diagnosis from symptom onset to death in progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) compared to Parkinson's disease (PD). J Neurol 270: 3464-3474. https://doi.org/10.1007/s00415-023-11629-x
|
| [112] |
Meloni M, Agliardi C, Guerini FR, et al. (2023) Oligomeric α-synuclein and tau aggregates in NDEVs differentiate Parkinson's disease from atypical parkinsonisms. Neurobiol Dis 176: 105947. https://doi.org/10.1016/j.nbd.2022.105947
|
| [113] |
Jiang C, Hopfner F, Berg D, et al. (2021) Validation of α-Synuclein in L1CAM-Immunocaptured Exosomes as a Biomarker for the Stratification of Parkinsonian Syndromes. Mov Disord 36: 2663-2669. https://doi.org/10.1002/mds.28591
|
| [114] |
Müller T (2012) Drug therapy in patients with Parkinson's disease. Translational Neurodegener 1: 1-13. https://doi.org/10.1186/2047-9158-1-10
|
| [115] |
Zhuang X, Xiang X, Grizzle W, et al. (2011) Treatment of brain inflammatory diseases by delivering exosome encapsulated anti-inflammatory drugs from the nasal region to the brain. Mol Ther 19: 1769-1779. https://doi.org/10.1038/mt.2011.164
|
| [116] | Lai CP-K, Breakefield XO (2012) Role of exosomes/microvesicles in the nervous system and use in emerging therapies. Front Physiol 3: 228. https://doi.org/10.3389/fphys.2012.00228 |
| [117] |
Akbari-Gharalari N, Ghahremani-Nasab M, Naderi R, et al. (2023) Improvement of spinal cord injury symptoms by targeting the Bax/Bcl2 pathway and modulating TNF-α/IL-10 using Platelet-Rich Plasma exosomes loaded with dexamethasone. AIMS Neurosci 10: 332-353. https://doi.org/10.3934/Neuroscience.2023026
|
| [118] |
Kojima R, Bojar D, Rizzi G, et al. (2018) Designer exosomes produced by implanted cells intracerebrally deliver therapeutic cargo for Parkinson's disease treatment. Nat Commun 9: 1305. https://doi.org/10.1038/s41467-018-03733-8
|
| [119] |
Izco M, Blesa J, Schleef M, et al. (2019) Systemic exosomal delivery of shRNA minicircles prevents parkinsonian pathology. Mol Ther 27: 2111-2122. https://doi.org/10.1016/j.ymthe.2019.08.010
|
| [120] |
Mianehsaz E, Mirzaei HR, Mahjoubin-Tehran M, et al. (2019) Mesenchymal stem cell-derived exosomes: a new therapeutic approach to osteoarthritis?. Stem Cell Res Ther 10: 1-13. https://doi.org/10.1186/s13287-019-1445-0
|
| [121] |
Li Z, Liu F, He X, et al. (2019) Exosomes derived from mesenchymal stem cells attenuate inflammation and demyelination of the central nervous system in EAE rats by regulating the polarization of microglia. Int Immunopharmacol 67: 268-280. https://doi.org/10.1016/j.intimp.2018.12.001
|
| [122] |
Vilaça-Faria H, Salgado AJ, Teixeira FG (2019) Mesenchymal stem cells-derived exosomes: a new possible therapeutic strategy for Parkinson's disease?. Cells 8: 118. https://doi.org/10.3390/cells8020118
|
| [123] |
Heris RM, Shirvaliloo M, Abbaspour-Aghdam S, et al. (2022) The potential use of mesenchymal stem cells and their exosomes in Parkinson's disease treatment. Stem Cell Res Ther 13: 371. https://doi.org/10.1186/s13287-022-03050-4
|
| [124] |
Abrishamdar M, Jalali MS, Yazdanfar N (2023) The role of exosomes in pathogenesis and the therapeutic efficacy of mesenchymal stem cell-derived exosomes against Parkinson's disease. Neurol Sci 44: 2277-2289. https://doi.org/10.1007/s10072-023-06706-y
|
| [125] |
Kattaia A, Abd EL-Baset S, Abdul-Maksoud R, et al. (2022) The therapeutic potential of exosomes derived from mesenchymal stem cells in experimentally induced hypertensive encephalopathy. J Med Histol 6: 16-33. https://doi.org/10.21608/jmh.2022.145535.1100
|
| [126] |
Tomlinson PR, Zheng Y, Fischer R, et al. (2015) Identification of distinct circulating exosomes in Parkinson's disease. Ann Clin Transl Neur 2: 353-361. https://doi.org/10.1002/acn3.175
|
| [127] |
Sarko DK, McKinney CE (2017) Exosomes: origins and therapeutic potential for neurodegenerative disease. Front Neurosci 11: 82. https://doi.org/10.3389/fnins.2017.00082
|
| 1. | Mary Keovisai, Wooksoo Kim, “It’s Not Officially Gambling”: Gambling Perceptions and Behaviors Among Older Chinese Immigrants, 2019, 35, 1573-3602, 1317, 10.1007/s10899-019-09841-4 | |
| 2. | Wooksoo Kim, Healthy mahjong, little mahjong: social gambling among older Chinese immigrants in the U.S, 2020, 20, 1445-9795, 97, 10.1080/14459795.2019.1672767 | |
| 3. | Steve Sharman, Kevin Butler, Amanda Roberts, Psychosocial risk factors in disordered gambling: A descriptive systematic overview of vulnerable populations, 2019, 99, 03064603, 106071, 10.1016/j.addbeh.2019.106071 | |
| 4. | Wooksoo Kim, Sungjae Kim, “Gambling Can’t Be Positive, Can it?”: Gambling Beliefs and Behaviors Among Older Korean Immigrants, 2020, 35, 0169-3816, 291, 10.1007/s10823-020-09407-7 | |
| 5. | Bei Wang, YingXiao Hua, XinQi Dong, Development and validation of a predictive index of elder self-neglect risk among a Chinese population, 2020, 1360-7863, 1, 10.1080/13607863.2020.1758903 | |
| 6. | Grace Yi, Lei Huang, Agnes I.F. Lam, Carl Latkin, Brian J. Hall, Spatial and sociodemographic correlates of gambling participation and disorder among female Filipino migrant workers in Macao, People's Republic of China, 2019, 97, 03064603, 49, 10.1016/j.addbeh.2019.05.021 | |
| 7. | Nannan Zhang, Mengting Li, XinQi Dong, Associations Between Gambling and Cognitive Function among U.S. Chinese Older Adults, 2022, 44, 0164-0275, 682, 10.1177/01640275221074020 | |
| 8. | Victoria Rowlatt, Darren Wraith, Thuy-Vi Minh Doan, Christina Malatzky, Culturally and Linguistically Diverse Gamblers of East Asian Descent in Australia: A Comprehensive Review of Current Evidence, 2023, 1573-3602, 10.1007/s10899-023-10202-5 | |
| 9. | W. Zhang, C. B. Fouché, P. J. Adams, A process model for responding to casino gambling harm experienced by Chinese migrants, 2023, 1445-9795, 1, 10.1080/14459795.2023.2238039 |
Figures(2) / Tables(1)
Naeimeh Akbari-Gharalari, Maryam Ghahremani-Nasab, Roya Naderi, Leila Chodari, Farshad Nezhadshahmohammad. The potential of exosomal biomarkers: Revolutionizing Parkinson's disease: How do they influence pathogenesis, diagnosis, and therapeutic strategies?[J]. AIMS Neuroscience, 2024, 11(3): 374-397. doi: 10.3934/Neuroscience.2024023
|
Never, N (%) |
Once per 2 years, N (%) |
Once per 1 year, N (%) |
Once per 6 months, N (%) |
Once per 3 months, N (%) |
Once per month, N (%) |
Once per week, N (%) |
|
| 1. State lottery tickets | 2785 (88.6) | 82 (2.6) | 60 (1.9) | 41 (1.3) | 53 (1.7) | 69 (2.2) | 53 (1.7) |
| 2. Mahjong or card games | 2692 (85.7) | 53 (1.7) | 21 (0.7) | 22 (0.7) | 21 (0.7) | 58 (1.9) | 276 (8.8) |
| 3. Sports games | 3123 (99.4) | 8 (0.3) | 0 (0.0) | 0 (0.0) | 1 (0.03) | 3 (0.1) | 6 (0.2) |
| 4. Casino | 2671 (85.0) | 181 (5.8) | 62 (2.0) | 0 (0.0) | 38 (1.2) | 70 (2.2) | 120 (3.8) |
| 5. Slot machines or bet money on video poker | 2836 (90.3) | 110 (3.5) | 35 (1.1) | 32 (1.0) | 23 (0.7) | 56 (1.8) | 49 (1.6) |
DownLoad: CSV|
Gamblers N = 467 |
Non-Gamblers N = 2, 675 |
p-value | ||
| Age, N (%) | ||||
| 60-64 | 111 (23.8) | 568 (21.2) | ||
| 65-69 | 101 (21.6) | 540 (20.2) | ||
| 70-74 | 83 (17.8) | 522 (19.5) | ||
| 75-79 | 78 (16.7) | 473 (17.7) | ||
| 80 and over | 94 (20.1) | 572 (21.4) | 2.7 | 0.605 |
| Gender, N (%) | ||||
| Female | 236 (50.5) | 1589 (59.4) | ||
| Male | 231 (49.5) | 1086 (40.6) | 12.8 | < 0.001 |
| Education (years), N (%) | ||||
| 0-6 | 232 (49.8) | 1135 (42.6) | ||
| 7-12 | 187 (40.1) | 915 (34.3) | ||
| 13 and over | 47 (10.1) | 616 (23.1) | 40.3 | < 0.001 |
| Income (USD), N (%) | ||||
| 4,999 | 114 (24.5) | 925 (34.9) | ||
| 9,999 | 266 (57.2) | 1346 (50.8) | ||
| 14,999 | 55 (11.8) | 255 (9.6) | ||
| 19,999 | 11 (2.4) | 57 (2.2) | ||
| $20,000 and over | 19 (4.1) | 68 (2.6) | 21.2 | < 0.001 |
| Marital Status, N (%) | ||||
| Married | 326 (70) | 1898 (71.1) | ||
| Separated | 12 (2.6) | 45 (1.7) | ||
| Divorced | 17 (3.7) | 56 (2.1) | ||
| Widowed | 107 (23) | 659 (24.7) | ||
| Never Married | 4 (0.9) | 12 (0.5) | 7.7 | 0.104 |
| Number of Children, N (%) | ||||
| 0 | 20 (4.3) | 108 (4) | ||
| 1-2 | 164 (35) | 1104 (41.4) | ||
| 3 or more | 282 (60.5) | 1458 (54.6) | 2 | 0.043 |
| Living Arrangement, N (%) | ||||
| 0 | 115 (24.6) | 560 (21) | ||
| 1 | 181 (38.8) | 1126 (42.1) | ||
| 2-3 | 80 (17.1) | 399 (14.9) | ||
| 4 or more | 91 (19.5) | 589 (22) | 6 | 0.110 |
| Years in the U.S., N (%) | ||||
| 0-10 | 58 (12.5) | 784 (29.4) | ||
| 11-20 | 150 (32.3) | 806 (30.3) | ||
| 21-30 | 123 (26.5) | 640 (24) | ||
| 31 and more | 133 (28.7) | 434 (16.3) | 77 | < 0.001 |
| Years in the Community, N (%) | ||||
| 0-10 | 212 (45.7) | 1588 (60) | ||
| 11-20 | 129 (27.8) | 608 (23) | ||
| 21-30 | 80 (17.2) | 306 (11.5) | ||
| 31 and more | 43 (9.3) | 166 (6.2) | 33.6 | < 0.001 |
| Origin, N (%) | ||||
| China | 423 (90.6) | 2490 (93.1) | ||
| Hong Kong and Macau | 21 (4.5) | 83 (3.1) | ||
| Taiwan | 5 (1.1) | 37 (1.4) | ||
| Other | 18 (3.9) | 65 (2.4) | 6 | 0.114 |
| Overall Health Status, N (%) | ||||
| Very good | 17 (3.6) | 122 (4.6) | ||
| Good | 194 (41.5) | 897 (33.5) | ||
| Fair | 192 (41.1) | 1124 (42) | ||
| Poor | 64 (13.7) | 532 (19.9) | 16.2 | < 0.001 |
| Quality of Life, N (%) | ||||
| Very good | 18 (3.9) | 196 (7.3) | ||
| Good | 193 (41.3) | 1186 (44.4) | ||
| Fair | 248 (53.1) | 1198 (44.8) | ||
| Poor | 8 (1.7 ) | 93 (3.4) | 17.7 | < 0.001 |
| Health Changes Over the Last Year, N (%) | ||||
| Improved | 37 (8.0) | 238 (8.9) | ||
| Same | 253 (54.0) | 1276 (47.7) | ||
| Worsened | 177 (38.0) | 1159 (43.4) | 6.6 | 0.037 |
DownLoad: CSV|
Never, N (%) |
Sometimes, N (%) |
Most of the time, N (%) |
Almost Always, N (%) |
|
| 1. Have you bet more than you could really afford to lose? | 463 (99.1) | 3 (0.6) | 1 (0.2) | 0 (0.0) |
| 2. Have you needed to gamble with larger amounts of money to get the same feeling of excitement? | 458 (98.1) | 8 (1.7) | 1 (0.2) | 0 (0.0) |
| 3. When you gambled, did you go back another day to try to win back the money you lost? | 435 (93.2) | 20 (4.3) | 5 (1.1) | 7 (1.5) |
| 4. Have you borrowed money or sold anything to get money to gamble? | 463 (99.1) | 3 (0.6) | 1 (0.2) | 0 (0.0) |
| 5. Have you felt that you might have a problem with gambling? | 447 (95.7) | 15 (3.2) | 2 (0.4) | 3 (0.6) |
| 6. Has gambling caused you any health problems, including stress and or anxiety? | 450 (96.4) | 11 (2.4) | 4 (0.9) | 2 (0.4) |
| 7. Have people criticized your betting or told you that you had a gambling problem, regardless of whether or not you thought it was true? | 425 (91.2) | 20 (4.3) | 8 (1.7) | 13 (2.8) |
| 8. Has your gambling caused any financial problems for you or your household? | 456 (97.6) | 6 (1.3) | 2 (0.4) | 3 (0.6) |
| 9. Have you felt guilty about the way you gamble or what happens when you gamble? | 447 (95.7) | 12 (2.6) | 4 (0.9) | 4 (0.9) |
DownLoad: CSV| *p < 0.05; **p < 0.01; ***p < 0.001
Notes: Edu = Education; Children = Number of Children; MS = Marital Status; Living = Living Arrangement; Yrs in U.S. = Years in the US; Yrs in com = Years in the Community; Origin = Country of Origin; OHS = Overall Health Status; QOL = Quality of Life; HC = Health Changes over the Last Year; PG = any gambling related problems | |||||||||||||||
| Age | Sex | Edu | Income | MS | Living | Children | Yrs in U.S. | Yrs in com | Origin | OHS | QOL | HC | Gambling | PG | |
| Age | 1.0 | ||||||||||||||
| Sex | 0.01 | 1.0 | |||||||||||||
| Edu | −0.12*** | −0.21*** | 1.0 | ||||||||||||
| Income | 0.05** | 0.00 | 0.01 | 1.0 | |||||||||||
| MS | −0.33*** | −0.32*** | 0.22 | −0.03 | 1.0 | ||||||||||
| Living | −0.35*** | −0.07*** | 0.02 | 0.16*** | 0.24 *** | 1.0 | |||||||||
| Children | 0.32*** | 0.09 *** | −0.38*** | 0.00 | −0.13*** | −0.07*** | 1.0 | ||||||||
| Yrs in U.S. | 0.35 *** | 0.03 | −0.10*** | 0.35*** | −0.2*** | −0.31*** | 0.15*** | 1.0 | |||||||
| Yrs in com | 0.23 *** | 0.02 | −0.11*** | 0.24*** | −0.13*** | −0.18*** | 0.10 *** | 0.66*** | 1.0 | ||||||
| Origin | 0.04* | −0.01 | −0.08*** | −0.20 | 0.05 ** | 0.05** | 0.04* | −0.2*** | −0.15*** | 1.0 | |||||
| OHS | −0.08*** | −0.06 ** | 0.06*** | 0.12*** | 0.05** | −0.00 | −0.00 | −0.01 | 0.05* | −0.03 | 1.0 | ||||
| QOL | 0.06*** | 0.05** | 0.09*** | 0.08*** | 0.03 | −0.01 | 0.04* | 0.00 | −0.02 | −0.04* | 0.32*** | 1.0 | |||
| HC | −0.11*** | −0.03 | 0.02 | 0.05** | 0.07*** | 0.01 | −0.02 | −0.04* | 0.03 | 0.00 | 0.35*** | 0.15*** | 1.0 | ||
| Gambling | −0.03 | −0.06*** | −0.08*** | 0.06** | −0.01 | −0.01 | 0.05** | 0.16*** | 0.10*** | −0.03 | 0.05** | −0.04* | 0.04* | 1.00 | |
| PG | −0.09* | −0.23*** | 0.04 | −0.06 | 0.05 | 0.11* | −0.06 | −0.02 | −0.00 | 0.07 | −0.05 | −0.09 | 0.03 | 1.00 | 1.00 |
DownLoad: CSV|
Never, N (%) |
Once per 2 years, N (%) |
Once per 1 year, N (%) |
Once per 6 months, N (%) |
Once per 3 months, N (%) |
Once per month, N (%) |
Once per week, N (%) |
|
| 1. State lottery tickets | 2785 (88.6) | 82 (2.6) | 60 (1.9) | 41 (1.3) | 53 (1.7) | 69 (2.2) | 53 (1.7) |
| 2. Mahjong or card games | 2692 (85.7) | 53 (1.7) | 21 (0.7) | 22 (0.7) | 21 (0.7) | 58 (1.9) | 276 (8.8) |
| 3. Sports games | 3123 (99.4) | 8 (0.3) | 0 (0.0) | 0 (0.0) | 1 (0.03) | 3 (0.1) | 6 (0.2) |
| 4. Casino | 2671 (85.0) | 181 (5.8) | 62 (2.0) | 0 (0.0) | 38 (1.2) | 70 (2.2) | 120 (3.8) |
| 5. Slot machines or bet money on video poker | 2836 (90.3) | 110 (3.5) | 35 (1.1) | 32 (1.0) | 23 (0.7) | 56 (1.8) | 49 (1.6) |
|
Gamblers N = 467 |
Non-Gamblers N = 2, 675 |
p-value | ||
| Age, N (%) | ||||
| 60-64 | 111 (23.8) | 568 (21.2) | ||
| 65-69 | 101 (21.6) | 540 (20.2) | ||
| 70-74 | 83 (17.8) | 522 (19.5) | ||
| 75-79 | 78 (16.7) | 473 (17.7) | ||
| 80 and over | 94 (20.1) | 572 (21.4) | 2.7 | 0.605 |
| Gender, N (%) | ||||
| Female | 236 (50.5) | 1589 (59.4) | ||
| Male | 231 (49.5) | 1086 (40.6) | 12.8 | < 0.001 |
| Education (years), N (%) | ||||
| 0-6 | 232 (49.8) | 1135 (42.6) | ||
| 7-12 | 187 (40.1) | 915 (34.3) | ||
| 13 and over | 47 (10.1) | 616 (23.1) | 40.3 | < 0.001 |
| Income (USD), N (%) | ||||
| 4,999 | 114 (24.5) | 925 (34.9) | ||
| 9,999 | 266 (57.2) | 1346 (50.8) | ||
| 14,999 | 55 (11.8) | 255 (9.6) | ||
| 19,999 | 11 (2.4) | 57 (2.2) | ||
| $20,000 and over | 19 (4.1) | 68 (2.6) | 21.2 | < 0.001 |
| Marital Status, N (%) | ||||
| Married | 326 (70) | 1898 (71.1) | ||
| Separated | 12 (2.6) | 45 (1.7) | ||
| Divorced | 17 (3.7) | 56 (2.1) | ||
| Widowed | 107 (23) | 659 (24.7) | ||
| Never Married | 4 (0.9) | 12 (0.5) | 7.7 | 0.104 |
| Number of Children, N (%) | ||||
| 0 | 20 (4.3) | 108 (4) | ||
| 1-2 | 164 (35) | 1104 (41.4) | ||
| 3 or more | 282 (60.5) | 1458 (54.6) | 2 | 0.043 |
| Living Arrangement, N (%) | ||||
| 0 | 115 (24.6) | 560 (21) | ||
| 1 | 181 (38.8) | 1126 (42.1) | ||
| 2-3 | 80 (17.1) | 399 (14.9) | ||
| 4 or more | 91 (19.5) | 589 (22) | 6 | 0.110 |
| Years in the U.S., N (%) | ||||
| 0-10 | 58 (12.5) | 784 (29.4) | ||
| 11-20 | 150 (32.3) | 806 (30.3) | ||
| 21-30 | 123 (26.5) | 640 (24) | ||
| 31 and more | 133 (28.7) | 434 (16.3) | 77 | < 0.001 |
| Years in the Community, N (%) | ||||
| 0-10 | 212 (45.7) | 1588 (60) | ||
| 11-20 | 129 (27.8) | 608 (23) | ||
| 21-30 | 80 (17.2) | 306 (11.5) | ||
| 31 and more | 43 (9.3) | 166 (6.2) | 33.6 | < 0.001 |
| Origin, N (%) | ||||
| China | 423 (90.6) | 2490 (93.1) | ||
| Hong Kong and Macau | 21 (4.5) | 83 (3.1) | ||
| Taiwan | 5 (1.1) | 37 (1.4) | ||
| Other | 18 (3.9) | 65 (2.4) | 6 | 0.114 |
| Overall Health Status, N (%) | ||||
| Very good | 17 (3.6) | 122 (4.6) | ||
| Good | 194 (41.5) | 897 (33.5) | ||
| Fair | 192 (41.1) | 1124 (42) | ||
| Poor | 64 (13.7) | 532 (19.9) | 16.2 | < 0.001 |
| Quality of Life, N (%) | ||||
| Very good | 18 (3.9) | 196 (7.3) | ||
| Good | 193 (41.3) | 1186 (44.4) | ||
| Fair | 248 (53.1) | 1198 (44.8) | ||
| Poor | 8 (1.7 ) | 93 (3.4) | 17.7 | < 0.001 |
| Health Changes Over the Last Year, N (%) | ||||
| Improved | 37 (8.0) | 238 (8.9) | ||
| Same | 253 (54.0) | 1276 (47.7) | ||
| Worsened | 177 (38.0) | 1159 (43.4) | 6.6 | 0.037 |
|
Never, N (%) |
Sometimes, N (%) |
Most of the time, N (%) |
Almost Always, N (%) |
|
| 1. Have you bet more than you could really afford to lose? | 463 (99.1) | 3 (0.6) | 1 (0.2) | 0 (0.0) |
| 2. Have you needed to gamble with larger amounts of money to get the same feeling of excitement? | 458 (98.1) | 8 (1.7) | 1 (0.2) | 0 (0.0) |
| 3. When you gambled, did you go back another day to try to win back the money you lost? | 435 (93.2) | 20 (4.3) | 5 (1.1) | 7 (1.5) |
| 4. Have you borrowed money or sold anything to get money to gamble? | 463 (99.1) | 3 (0.6) | 1 (0.2) | 0 (0.0) |
| 5. Have you felt that you might have a problem with gambling? | 447 (95.7) | 15 (3.2) | 2 (0.4) | 3 (0.6) |
| 6. Has gambling caused you any health problems, including stress and or anxiety? | 450 (96.4) | 11 (2.4) | 4 (0.9) | 2 (0.4) |
| 7. Have people criticized your betting or told you that you had a gambling problem, regardless of whether or not you thought it was true? | 425 (91.2) | 20 (4.3) | 8 (1.7) | 13 (2.8) |
| 8. Has your gambling caused any financial problems for you or your household? | 456 (97.6) | 6 (1.3) | 2 (0.4) | 3 (0.6) |
| 9. Have you felt guilty about the way you gamble or what happens when you gamble? | 447 (95.7) | 12 (2.6) | 4 (0.9) | 4 (0.9) |
| *p < 0.05; **p < 0.01; ***p < 0.001
Notes: Edu = Education; Children = Number of Children; MS = Marital Status; Living = Living Arrangement; Yrs in U.S. = Years in the US; Yrs in com = Years in the Community; Origin = Country of Origin; OHS = Overall Health Status; QOL = Quality of Life; HC = Health Changes over the Last Year; PG = any gambling related problems | |||||||||||||||
| Age | Sex | Edu | Income | MS | Living | Children | Yrs in U.S. | Yrs in com | Origin | OHS | QOL | HC | Gambling | PG | |
| Age | 1.0 | ||||||||||||||
| Sex | 0.01 | 1.0 | |||||||||||||
| Edu | −0.12*** | −0.21*** | 1.0 | ||||||||||||
| Income | 0.05** | 0.00 | 0.01 | 1.0 | |||||||||||
| MS | −0.33*** | −0.32*** | 0.22 | −0.03 | 1.0 | ||||||||||
| Living | −0.35*** | −0.07*** | 0.02 | 0.16*** | 0.24 *** | 1.0 | |||||||||
| Children | 0.32*** | 0.09 *** | −0.38*** | 0.00 | −0.13*** | −0.07*** | 1.0 | ||||||||
| Yrs in U.S. | 0.35 *** | 0.03 | −0.10*** | 0.35*** | −0.2*** | −0.31*** | 0.15*** | 1.0 | |||||||
| Yrs in com | 0.23 *** | 0.02 | −0.11*** | 0.24*** | −0.13*** | −0.18*** | 0.10 *** | 0.66*** | 1.0 | ||||||
| Origin | 0.04* | −0.01 | −0.08*** | −0.20 | 0.05 ** | 0.05** | 0.04* | −0.2*** | −0.15*** | 1.0 | |||||
| OHS | −0.08*** | −0.06 ** | 0.06*** | 0.12*** | 0.05** | −0.00 | −0.00 | −0.01 | 0.05* | −0.03 | 1.0 | ||||
| QOL | 0.06*** | 0.05** | 0.09*** | 0.08*** | 0.03 | −0.01 | 0.04* | 0.00 | −0.02 | −0.04* | 0.32*** | 1.0 | |||
| HC | −0.11*** | −0.03 | 0.02 | 0.05** | 0.07*** | 0.01 | −0.02 | −0.04* | 0.03 | 0.00 | 0.35*** | 0.15*** | 1.0 | ||
| Gambling | −0.03 | −0.06*** | −0.08*** | 0.06** | −0.01 | −0.01 | 0.05** | 0.16*** | 0.10*** | −0.03 | 0.05** | −0.04* | 0.04* | 1.00 | |
| PG | −0.09* | −0.23*** | 0.04 | −0.06 | 0.05 | 0.11* | −0.06 | −0.02 | −0.00 | 0.07 | −0.05 | −0.09 | 0.03 | 1.00 | 1.00 |
