Cardiovascular Risk Factors and Phenoconversion to Neurodegenerative Synucleinopathies in Idiopathic REM Sleep Behavior Disorder
Abstract
Several studies have suggested that atherosclerotic diseases and diabetes may be risk factors for α-synucleinopathies. This prospective cohort study evaluated whether cardiovascular diseases and metabolic risk factors alter the rate or type of phenoconversion from idiopathic/isolated REM sleep behavior disorder (iRBD) to parkinsonism or dementia. Polysomnography-confirmed iRBD patients recruited between 2004 and 2020 were followed annually. Baseline history of cardiovascular disorders, hypertension, hypercholesterolemia, and diabetes were compared among patients who developed outcomes versus those who remained outcome-free. No atherosclerotic risk factors were associated with development of α-synucleinopathies. Patients with hypercholesterolemia were somewhat more likely to develop dementia with Lewy bodies rather than Parkinson’s disease.
INTRODUCTION
Accumulation and alteration of α-synuclein in the brain (α-synucleinopathies) underlies neurodegenerative disorders such as Parkinson’s disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB) [1, 2]. Idiopathic/isolated rapid eye movement sleep behavior disorder (iRBD) is a parasomnia and a well-established predictive marker for development of these α-synucleinopathies [3, 4].
Several studies have investigated whether cardiovascular disorders (CVD) and metabolic risk factors (i.e., hypertension, diabetes, hypercholesterolemia) predict α-synucleinopathies, and have found contrasting findings [5, 6]. Some studies found no association between these variables and α-synucleinopathies [7], while some reported them either as a risk factor [8– 10], or protective factor for α-synucleinopathies [11– 14].
Since iRBD is a window into the early stages of synucleinopathies, we aimed to evaluate whether CVD and metabolic risk factors could predict phenoconversion to α-synucleinopathies in this population. Moreover, since cardiovascular conditions and risk factors could potentially modulate the vulnerability of different neuronal tissues to neurodegeneration (e.g., cortex vs. brainstem) [15], we assessed if they alter the type of neurodegenerative syndrome (i.e., parkinsonism-first vs. DLB-first).
MATERIALS AND METHODS
The Montreal-iRBD cohort is an ongoing cohort of iRBD patients referred to the Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada. Patients were recruited between 2004 and 2020 and were followed up annually until development of neurodegenerative disease or death. Diagnosis and recruitment details are explained elsewhere [4, 16]. Briefly, diagnosis of iRBD was confirmed with history and polysomnography according to the International Classification of Sleep Disorders (ICSD-II) criteria [17, 18]. All patients had neurological exams and complete neuropsychological assessments and were free of parkinsonism or dementia at baseline.
Complete self-reported medical history including CVD (any self-reported history of myocardial infarction, coronary artery disease, angioplasty, coronary artery bypass graft, arrhythmias, valvulopathies, and congestive heart failure), hypertension, diabetes, hypercholesterolemia, and medications were obtained from all patients.
The research protocol was approved by the local research ethics board (REB), and written informed consent was obtained from all participants.
Outcome assessment
Follow-up visits were performed annually to assess the development of parkinsonism or dementia. Home evaluation was conducted for patients who could not attend clinic visits. Parkinsonism was defined according to Movement Disorders Society (MDS) diagnostic criteria [19] as bradykinesia along with either rigidity, rest tremor, or both. Patients who developed outcome before 2015 were diagnosed according to UK Brain Bank criteria [20]. Dementia was diagnosed based on MDS Dementia criteria [21] by consensus between the neuropsychologist and neurologist. According to the 2017 DLB criteria, all dementia phenoconverters are considered as probable DLB [22, 23].
Statistical analysis
The primary analysis was to measure the risk of developing any defined neurodegenerative outcome (PD, MSA, or DLB). A secondary analysis investigated the risk of developing each individual outcome separately. We calculated the hazard ratio (HR) using Cox regression model. Both crude HR and age/sex-adjusted HR (aHR) are presented.
To compare the predictive role of CVD and metabolic risk factors for development of PD or DLB, we performed logistic regression analysis. Crude and age/sex-adjusted odds ratio (aOR) are reported.
RESULTS
A total of 183 participants who provided at least one follow-up data were analyzed in this study (Table 1). The mean age of included participants was 66.4 years (SD = 8.3), and 138 cases (75.4%) were male. The average time between RBD polysomnographic diagnosis and the last evaluation was 4.40 years (SD = 3.2). Among the 183 included participants, 73 (39.9%) developed outcomes (30 PD, 38 DLB, and 5 MSA) at an average interval of 3.7 years (range = 1– 14 years). Those who developed outcome were significantly older than outcome-free patients (68.53±8.5 vs. 64.98±7.8 years, p = 0.004) and were older at reported RBD symptom onset (60.06±10.2 years vs. 54.43±14.8 years, p = 0.013).
Table 1
Demographics and baseline characteristics
| Baseline characteristics | Developed any outcome N = 73 | Still outcome-free N = 110 | p |
| Follow-up duration – Mean±SD (min = 0.5, max = 15.5) | 3.66±3.0* | 4.90±3.2 | 0.010 |
| Age at baseline- Mean±SD (min = 39, max = 93) | 68.53±8.5 | 64.98±7.8 | 0.004 |
| Male – % (n) | 71.2 (52) | 78.2 (86) | 0.3 |
| RBD symptom duration – Mean±SD (min = 0, max = 50) | 7.36±7.9 | 8.97±9.1 | 0.2 |
| Age of RBD onset – Mean±SD (min = 14, max = 78) | 60.06±10.2 | 54.43±14.8 | 0.013 |
| B-SIT olfaction raw score- Mean±SD (min = 2, max = 12) | 6.57±2.5 | 7.94±2.6 | 0.001 |
| MDS-UPDRS3 – Mean±SD (min = 0, max = 20) | 6.19±5.2 | 3.23±2.7 | <0.001 |
| MDS-UPDRS2 – Mean±SD (min = 0, max = 8) | 2.13±2.2 | 1.21±1.6 | 0.003 |
| MoCA – Mean±SD (min = 14, max = 30) | 24.96±3.1 | 25.74±2.9 | 0.1 |
| Systolic blood pressure drop – Mean±SD (min = – 14, max = 50) | 16.03±15.5 | 10.58±15.3 | 0.021 |
| Orthostatic symptoms – Mean±SD (min = 0, max = 2) | 0.34±0.6 | 0.33±0.6 | 0.8 |
| Urinary dysfunction – Mean±SD (min = 0, max = 2) | 0.44±0.6 | 0.38±0.6 | 0.050 |
| Erectile dysfunction – Mean±SD (min = 0, max = 4) | 1.60±1.4 | 1.56±1.6 | 0.9 |
| Constipation – Mean±SD (min = 0, max = 3) | 0.90±0.9 | 0.64±0.9 | 0.1 |
| Tonic REM percent – Mean±SD (min = 2.7, max = 100) | 56.83±26.4 | 46.37±30.8 | 0.031 |
| Phasic REM percent – Mean±SD (min = 6.18, max = 89) | 36.59±17.9 | 36.80±18.2 | 0.9 |
| Beck Depression Inventory – Mean±SD (min = 0, max = 31) | 9.63±6.6 | 10.04±7.1 | 0.8 |
| Beck Anxiety Inventory – Mean±SD (min = 0, max = 35) | 8.51±8.6 | 8.35±7.0 | 0.9 |
| History of antidepressant use – % (n) | 20.8 (15) | 32.7 (36) | 0.1 |
*Follow-up is terminated at the time of phenoconversion in those who developed an outcome REM, Rapid Eye Movement; iRBD. idiopathic REM sleep behavior disorder; B-SIT, Brief Smell Identification Test; MDS-UPDRS, Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale; MoCA, Montreal Cognitive Assessment.
Comparing 73 patients who developed outcome with 110 outcome-free patients (Table 2), we found no difference in their baseline CVD (12.3% vs. 10.0%, aHR = 0.89 [0.44,1.80]), hypertension (30.1% vs. 29.1%, aHR = 0.97 [0.58,1.63]), hypercholesterolemia (31.5% vs. 40.0%, aHR = 0.84 [0.50,1.41]), or diabetes (12.3% vs. 17.3%, aHR = 0.80 [0.39,1.63]). Stratification by sex, and median age did not change the results.
Table 2
Frequency and risk of developing different α-synucleinopathies among iRBD patients with cardiovascular disease, hypertension, hypercholesterolemia, and diabetes
| Cardiovascular disease – % (n) | Hypertension – % (n) | Hypercholesterolemia – % (n) | Diabetes – % (n) | ||
| Still outcome-free | N = 110 | 10.0 (11) | 29.1 (32) | 40.0 (44) | 17.3 (19) |
| Age = 64.98±7.8 | |||||
| Male% = 78.2 | |||||
| Developed any outcome | N = 73 | 12.3 (9) | 30.1 (22) | 31.5 (23) | 12.3 (9) |
| Age = 68.53±8.5 | |||||
| Male% = 71.2 | |||||
| PD-first | N = 30 | 16.7 (5) | 20.0 (6) | 16.7 (5) | 10.0 (3) |
| Age = 66.80±8.6 | |||||
| Male% = 73.3 | |||||
| DLB-first | N = 38 | 7.9 (3) | 34.2 (13) | 44.7 (17) | 15.8 (6) |
| Age = 71.50±6.7 | |||||
| Male% = 73.7 | |||||
| MSA | N = 5* | 20.0 (1) | 60.0 (3) | 20.0 (1) | 0 (0) |
| Age = 56.40±8.3 | |||||
| Male% = 40.0 |
| Comparison between: | 95% CI’s | Cardiovascular disease | Hypertension | Hypercholesterolemia | Diabetes |
| Any outcome vs. Outcome-free | Crude HR [95% CI] | 1.00 [0.49,2.03] | 1.13 [0.68,1.87] | 0.99 [0.60,1.63] | 0.82 [0.41,1.65] |
| aHR [95% CI] | 0.89 [0.44,1.80] | 0.97 [0.58,1.63] | 0.84 [0.50,1.41] | 0.80 [0.39,1.63] | |
| PD-first vs. Outcome-free | Crude HR [95% CI] | 1.22 [0.46,3.25] | 0.73 [0.30,1.79] | 0.43 [0.16,1.12] | 0.62 [0.19,2.03] |
| aHR [95% CI] | 1.12 [0.42,2.99] | 0.66 [0.27,1.65] | 0.38 [0.14,1.02] | 0.61 [0.18,2.05] | |
| DLB-first vs. Outcome-free | Crude HR [95% CI] | 0.77 [0.23,2.56] | 1.28 [0.65,2.53] | 1.43 [0.75,2.73] | 0.95 [0.39,2.28] |
| aHR [95% CI] | 0.57 [0.17,1.91] | 0.96 [0.48,1.94] | 0.97 [0.48,1.94] | 1.07 [0.44,2.65] | |
| PD- vs. DLB-first | Crude OR [95% CI] | 2.33 [0.51,10.67] | 0.48 [0.16,1.47] | 0.25 [0.08,0.78] | 0.59 [0.14,2.60] |
| aOR [95% CI] | 3.56 [0.65,19.60] | 0.61 [0.19,1.95] | 0.30 [0.09,1.04] | 0.68 [0.15,3.09] |
A significance level of 0.05 was considered for all tests. PD, Parkinson’s disease; DLB, dementia with Lewy bodies; MSA, multiple system atrophy; HR, hazard ratio; aHR, age/sex adjusted hazard ratio; OR, odds ratio; aOR, age/sex adjusted odds ratio. *Because of the small number of MSA patients, and sample number of exposures to cardiovascular disease, hypertension, hypercholesterolemia, or diabetes, we did not perform sensitivity analysis on MSA patients.
The sensitivity analysis also found no difference between exposure to CVD and metabolic risk factors between outcome-free participants and those who developed individual outcomes of PD or DLB. We did not perform sensitivity analysis on MSA patients because of the small number of cases. Comparison of PD-first vs. dementia-first patients showed lower baseline hypercholesterolemia among PD patients (16.7% vs. 44.7%, crude OR = 0.25 [0.08,0.78]).
Those who developed outcomes and outcome-free patients had similar exposure to statins at baseline (28.8% vs. 31.8, aHR = 0.92 [0.54,1.58]).
Among the nine diabetic patients who developed outcome and 19 outcome-free diabetics, 6 (66.7%) and 10 (52.6%) took biguanide-group medications (i.e., Metformin, Glucophage) at baseline, with no significant difference between medication types (aHR = 1.67 [0.35,7.97]). A similar finding was observed for sulfonylurea-group (i.e., Gliclazide, Diamicron, Glyburide, Diabeta) (22.2% vs. 21.4%, aHR = 1.50 [0.27,8.39]).
DISCUSSION
The present study suggests no clear relationship between CVD, hypertension, hypercholesterolemia, diabetes, diabetes medications, and phenoconversion to α-synucleinopathies in iRBD.
Cardiovascular disease
Our results are broadly similar to the multicenter study on iRBD in 2015 (which included some patients from this current study), in which prior atherosclerotic disease, hypertension, and diabetes did not modulate risk of phenoconversion [24]. Similarly, Vlasov et al., in their population-based Rotterdam Study, found no association between atherosclerosis as measured by carotid intima-media thickness and the risk of all-cause parkinsonism [7].
Hypertension
The 2015 multicenter study on iRBD [24], and cohort studies by Grandinetti et al. [25] and Simon et al. [26] found no association between hypertension and PD. By contrast, a cohort of Finnish population found that hypertension was associated with an increased PD risk only among women [27]. Slight differences in how PD was confirmed (in particular the chance of ‘vascular parkinsonism’ being included), and different adjusted confounders may have influenced the results [28]. Autonomic dysfunction in prodromal stages can cause reduced blood pressure [29], thereby confounding any positive relationship; the fact that we were assessing patients from a population in which nearly all had prodromal synucleinopathy, therefore, adds non-overlapping information to that observed in population-based epidemiological studies [26, 27, 30].
Hypercholesterolemia
We found no statistically significant association between hypercholesterolemia and phenoconversion to α-synucleinopathies, but some suggestion that hypercholesterolemia may modify phenoconversion towards a ‘dementia-first’ subtype [31].
The literature on hypercholesterolemia and synucleinopathies is highly variable. Contrary to this study, 2015 iRBD multicenter study found a slightly lower occurrence of hypercholesterolemia in patients who converted to neurodegenerative disease [24]. Simon et al., and Sääksjärvi et al. found no association between cholesterol and PD, as in our study [26, 30]. Conversely, a Finnish study by Hu et al. reported that there might be a positive correlation between cholesterol and PD risk [10]. Other cohorts, including the Rotterdam study and two studies by Huang et al., found an inverse relationship between hypercholesterolemia and PD [11, 32, 33], suggesting a potentially protective mechanism. Another cohort by Rozani et al. also reported that hypercholesterolemia is linked to decreased PD risk [14]. A meta-analysis of three case-control and 12 cohort studies reported no association between PD and total serum cholesterol or high-density lipoprotein cholesterol but an inverse relationship between low-density lipoprotein cholesterol and PD [13].
It is unclear why we found a potential link between cholesterol and dementia-first phenoconversion. It is conceivable that hypercholesterolemia could increase cortical vulnerability to degeneration (e.g., via comorbid vascular pathology), so that the same synucleinopathy spread pattern preferentially causes degeneration in tissue critical to development of dementia [34]. Alternatively, this could simply be an additive effect, such that subtle vascular disease makes clinical cognitive impairment more likely, even with the same degree of synucleinopathy. Although, moving the prodromal state minimizes the role of reverse causation, our average follow-up period was still relatively short. Wang et al. in the Health, Aging and Body Composition study recently showed that circulating cholesterol may decrease as PD develops, likely starting in prodromal phase [35].
Diabetes
There is also ongoing debate about the association between diabetes and PD. Potashkin et al. reported that findings vary greatly depending on study type [28], with some cohort studies finding an increased risk of PD in diabetics [8, 36– 38] and some case-control studies finding a decreased risk or no risk at all [39, 40]. As with CVD/hypertension, it is possible that prodromal disease could modulate diabetes risk (e.g., via weight loss); so, evaluation of diabetes effect on those already in prodromal stages provides a unique study population. Moreover, as numerous diabetes medications are suggested as potential neuroprotective treatments [41– 44], it is important to study whether diabetes medications modulate the progression speed from prodromal to clinical stages. In this case, we found no clear effect of either diabetes or diabetes medications on progression speed or subtype. This might suggest that treatment of diabetes in prodromal disease may not prevent phenoconversion.
In conclusion, we found no clear evidence that either CVD or metabolic conditions accelerate phenoconversion from prodromal to clinical synucleinopathies in iRBD. Future studies using objective assessment of CVD at baseline (e.g., carotid intima-media thickness), and extending observational period and sample size (particularly to assess the effect of less-common conditions and medications) would be useful.
CONFLICT OF INTEREST
S. Zolfaghari, N. Lewandowski, A. Pelletier, S.A. Naeimi and M. Brillon-Corbeil have nothing to disclose.
J.F. Gagnon reports grants from the Fonds de Recherche du Québec – Santé, the Canadian Institutes of Health Research, the Weston-Garfield Foundation, and the National Institutes of Health/National Institute on Aging. He holds a Canada Research Chair in Cognitive Decline in Pathological Aging.
J.Y. Montplaisir received consultant honorarium from Takeda Pharmaceuticals, Novartis Pharmaceuticals, Merck Pharmaceuticals, Jazz Pharmaceuticals, UCB Canada and Valeant Canada.
R.B. Postuma reports grants and personal fees from the Fonds de Recherche du Québec – Santé, grants from the Canadian Institutes of Health Research, grants from The Parkinson Society of Canada, grants from Weston-Garfield Foundation, grants from Michael J. Fox Foundation, grants from Webster Foundation, personal fees from Takeda, personal fees from Roche/Prothena, personal fees from Teva Neurosciences, personal fees from Novartis Canada, personal fees from Biogen, personal fees from Boehringer Ingelheim, personal fees from Theranexus, personal fees from GE HealthCare, personal fees from Jazz Pharmaceuticals, personal fees from Abbvie, personal fees from Jannsen, personal fees from Otsuko, personal fees from Phytopharmics, personal fees from Inception Sciences, other from Parkinson Canada, personal fees from Curasen, outside the submitted work.
None of the financial disclosures are relevant to the submitted work.
REFERENCES
[1] | Spillantini MG , Goedert M ((2000) ) The alpha-synucleinopathies: Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. Ann N Y Acad Sci 920: , 16–27. |
[2] | Alafuzoff I , Hartikainen P ((2018) ) Chapter 24 – Alpha-synucleinopathies. In Handbook of Clinical Neurology, Kovacs GG, Alafuzoff I, eds. Elsevier, pp. 339–353. |
[3] | Claassen DO , Josephs KA , Ahlskog JE , Silber MH , Tippmann-Peikert M , Boeve BF ((2010) ) REM sleep behavior disorder preceding other aspects of synucleinopathies by up to half a century. Neurology 75: , 494–499. |
[4] | Postuma RB , Gagnon J-F , Bertrand J-A , Génier Marchand D , Montplaisir JY ((2015) ) Parkinson risk in idiopathic REM sleep behavior disorder: Preparing for neuroprotective trials. Neurology 84: , 1104–1113. |
[5] | Hu G ((2010) ) Total cholesterol and the risk of Parkinson’s disease: A review for some new findings. Parkinsons Dis 2010: , 836962. |
[6] | Yue X , Li H , Yan H , Zhang P , Chang L , Li T ((2016) ) Risk of Parkinson disease in diabetes mellitus: An updated meta-analysis of population-based cohort studies. Medicine (Baltimore) 95: , e3549. |
[7] | Vlasov V , Darweesh SKL , Stricker BH , Franco OH , Ikram MK , Kavousi M , Bos D , Klaver CCW , Ikram MA ((2017) ) Subclinical vascular disease and the risk of parkinsonism: The Rotterdam Study. Parkinsonism Relat Disord 43: , 27–32. |
[8] | Xu Q , Park Y , Huang X , Hollenbeck A , Blair A , Schatzkin A , Chen H ((2011) ) Diabetes and risk of Parkinson’s disease. Diabetes Care 34: , 910–915. |
[9] | Li Q , Wang C , Tang H , Chen S , Ma J ((2018) ) Stroke and coronary artery disease are associated with Parkinson’s disease. Can J Neurol Sci 45: , 559–565. |
[10] | Hu G , Antikainen R , Jousilahti P , Kivipelto M , Tuomilehto J ((2008) ) Total cholesterol and the risk of Parkinson disease. Neurology 70: , 1972–1979. |
[11] | de Lau LML , Koudstaal PJ , Hofman A , Breteler MMB ((2006) ) Serum cholesterol levels and the risk of Parkinson’s disease. Am J Epidemiol 164: , 998–1002. |
[12] | Scigliano G , Musicco M , Soliveri P , Piccolo I , Ronchetti G , Girotti F ((2006) ) Reduced risk factors for vascular disorders in Parkinson disease patients: A case-control study. Stroke 37: , 1184–1188. |
[13] | Jiang Z , Xu X , Gu X , Ou R , Luo X , Shang H , Song W ((2020) ) Effects of higher serum lipid levels on the risk of Parkinson’s disease: A systematic review and meta-analysis. Front Neurol 11: , 597. |
[14] | Rozani V , Gurevich T , Giladi N , El-Ad B , Tsamir J , Hemo B , Peretz C ((2018) ) Higher serum cholesterol and decreased Parkinson’s disease risk: A statin-free cohort study. Mov Disord 33: , 1298–1305. |
[15] | Kalaria RN ((2010) ) Vascular basis for brain degeneration: Faltering controls and risk factors for dementia. Nutr Rev 68: , S74–S87. |
[16] | Postuma RB , Gagnon JF , Vendette M , Montplaisir JY ((2009) ) Markers of neurodegeneration in idiopathic rapid eye movement sleep behaviour disorder and Parkinson’s disease. Brain 132: , 3298–3307. |
[17] | Neikrug AB , Ancoli-Israel S ((2012) ) Diagnostic tools for REM sleep behavior disorder. Sleep Med Rev 16: , 415–429. |
[18] | Montplaisir J , Gagnon J-F , Fantini ML , Postuma RB , Dauvilliers Y , Desautels A , Rompré S , Paquet J ((2010) ) Polysomnographic diagnosis of idiopathic REM sleep behavior disorder. Mov Disord 25: , 2044–2051. |
[19] | Postuma RB , Berg D , Stern M , Poewe W , Olanow CW , Oertel W , Obeso J , Marek K , Litvan I , Lang AE , Halliday G , Goetz CG , Gasser T , Dubois B , Chan P , Bloem BR , Adler CH , Deuschl G ((2015) ) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30: , 1591–1601. |
[20] | Gibb WR , Lees AJ ((1988) ) The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s disease. J Neurol Neurosurg Psychiatry 51: , 745–752. |
[21] | Dubois B , Burn D , Goetz C , Aarsland D , Brown RG , Broe GA , Dickson D , Duyckaerts C , Cummings J , Gauthier S , Korczyn A , Lees A , Levy R , Litvan I , Mizuno Y , McKeith IG , Olanow CW , Poewe W , Sampaio C , Tolosa E , Emre M ((2007) ) Diagnostic procedures for Parkinson’s disease dementia: Recommendations from the movement disorder society task force. Mov Disord 22: , 2314–2324. |
[22] | McKeith IG , Dickson DW , Lowe J , Emre M , O’Brien JT , Feldman H , Cummings J , Duda JE , Lippa C , Perry EK , Aarsland D , Arai H , Ballard CG , Boeve B , Burn DJ , Costa D , Del Ser T , Dubois B , Galasko D , Gauthier S , Goetz CG , Gomez-Tortosa E , Halliday G , Hansen LA , Hardy J , Iwatsubo T , Kalaria RN , Kaufer D , Kenny RA , Korczyn A , Kosaka K , Lee VM , Lees A , Litvan I , Londos E , Lopez OL , Minoshima S , Mizuno Y , Molina JA , Mukaetova-Ladinska EB , Pasquier F , Perry RH , Schulz JB , Trojanowski JQ , Yamada M ((2005) ) Diagnosis and management of dementia with Lewy bodies: Third report of the DLB Consortium. Neurology 65: , 1863–1872. |
[23] | McKeith IG , Boeve BF , Dickson DW , Halliday G , Taylor J-P , Weintraub D , Aarsland D , Galvin J , Attems J , Ballard CG , Bayston A , Beach TG , Blanc F , Bohnen N , Bonanni L , Bras J , Brundin P , Burn D , Chen-Plotkin A , Duda JE , El-Agnaf O , Feldman H , Ferman TJ , Ffytche D , Fujishiro H , Galasko D , Goldman JG , Gomperts SN , Graff-Radford NR , Honig LS , Iranzo A , Kantarci K , Kaufer D , Kukull W , Lee VMY , Leverenz JB , Lewis S , Lippa C , Lunde A , Masellis M , Masliah E , McLean P , Mollenhauer B , Montine TJ , Moreno E , Mori E , Murray M , O’Brien JT , Orimo S , Postuma RB , Ramaswamy S , Ross OA , Salmon DP , Singleton A , Taylor A , Thomas A , Tiraboschi P , Toledo JB , Trojanowski JQ , Tsuang D , Walker Z , Yamada M , Kosaka K ((2017) ) Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 89: , 88–100. |
[24] | Postuma RB , Iranzo A , Hogl B , Arnulf I , Ferini-Strambi L , Manni R , Miyamoto T , Oertel W , Dauvilliers Y , Ju Y-E , Puligheddu M , Sonka K , Pelletier A , Santamaria J , Frauscher B , Leu-Semenescu S , Zucconi M , Terzaghi M , Miyamoto M , Unger MM , Carlander B , Fantini M-L , Montplaisir JY ((2015) ) Risk factors for neurodegeneration in idiopathic rapid eye movement sleep behavior disorder: A multicenter study. Ann Neurol 77: , 830–839. |
[25] | Grandinetti A , Morens DM , Reed D , MacEachern D ((1994) ) Prospective study of cigarette smoking and the risk of developing idiopathic Parkinson’s disease. Am J Epidemiol 139: , 1129–1138. |
[26] | Simon KC , Chen H , Schwarzschild M , Ascherio A ((2007) ) Hypertension, hypercholesterolemia, diabetes, and risk of Parkinson disease. Neurology 69: , 1688–1695. |
[27] | Qiu C , Hu G , Kivipelto M , Laatikainen T , Antikainen R , Fratiglioni L , Jousilahti P , Tuomilehto J ((2011) ) Association of blood pressure and hypertension with the risk of Parkinson disease. Hypertension 57: , 1094–1100. |
[28] | Potashkin J , Huang X , Becker C , Chen H , Foltynie T , Marras C ((2020) ) Understanding the links between cardiovascular disease andParkinson’s disease. Mov Disord 35: , 55–74. |
[29] | Postuma RB , Gagnon JF , Pelletier A , Montplaisir J ((2013) ) Prodromal autonomic symptoms and signs in Parkinson’s disease and dementia with Lewy bodies. Mov Disord 28: , 597–604. |
[30] | Sääksjärvi K , Knekt P , Männistö S , Lyytinen J , Heliövaara M ((2015) ) Prospective study on the components of metabolic syndrome and the incidence of Parkinson’s disease. Parkinsonism Relat Disord 21: , 1148–1155. |
[31] | Cheng C-K , Tsao Y-C , Su Y-C , Sung F-C , Tai H-C , Kung W-M ((2018) ) Metabolic risk factors of Alzheimer’s disease, dementia with Lewy bodies, and normal elderly: A population-based study. Behav Neurol 2018: , 8312346. |
[32] | Huang X , Alonso A , Guo X , Umbach DM , Lichtenstein ML , Ballantyne CM , Mailman RB , Mosley TH , Chen H ((2015) ) Statins, plasma cholesterol, and risk of Parkinson’s disease: A prospective study. Mov Disord 30: , 552–559. |
[33] | Huang X , Abbott RD , Petrovitch H , Mailman RB , Ross GW ((2008) ) Low LDL cholesterol and increased risk of Parkinson’s disease: Prospective results from Honolulu-Asia Aging Study. Mov Disord 23: , 1013–1018. |
[34] | O’Brien JT , Thomas A ((2015) ) Vascular dementia. Lancet 386: , 1698–1706. |
[35] | Wang K , Luo Z , Li C , Huang X , Shiroma EJ , Simonsick EM , Chen H ((2021) ) Blood Cholesterol decreases as Parkinson’s disease develops and progresses. J Parkinsons Dis 11: , 1177–1186. |
[36] | De Pablo-Fernandez E , Goldacre R , Pakpoor J , Noyce AJ , Warner TT ((2018) ) Association between diabetes and subsequent Parkinson disease: A record-linkage cohort study. Neurology 91: , e139–e142. |
[37] | Hu G , Jousilahti P , Bidel S , Antikainen R , Tuomilehto J ((2007) ) Type 2 diabetes and the risk of Parkinson’s disease. Diabetes Care 30: , 842–847. |
[38] | Driver JA , Smith A , Buring JE , Gaziano JM , Kurth T , Logroscino G ((2008) ) Prospective cohort study of type 2 diabetes and the risk of Parkinson’s disease. Diabetes Care 31: , 2003–2005. |
[39] | Becker C , Brobert GP , Johansson S , Jick SS , Meier CR ((2008) ) Diabetes in patients with idiopathic Parkinson’s disease. Diabetes Care 31: , 1808–1812. |
[40] | Miyake Y , Tanaka K , Fukushima W , Sasaki S , Kiyohara C , Tsuboi Y , Yamada T , Oeda T , Miki T , Kawamura N , Sakae N , Fukuyama H , Hirota Y , Nagai M ((2010) ) Case-control study of risk of Parkinson’s disease in relation to hypertension, hypercholesterolemia, and diabetes in Japan. J Neurol Sci 293: , 82–86. |
[41] | Shi Q , Liu S , Fonseca VA , Thethi TK , Shi L ((2019) ) Effect of metformin on neurodegenerative disease among elderly adult US veterans with type 2 diabetes mellitus. BMJ Open 9: , e024954. |
[42] | Ping F , Jiang N , Li Y ((2020) ) Association between metformin and neurodegenerative diseases of observational studies: Systematic review and meta-analysis. BMJ Open Diabetes Res Care 8: , e001370. |
[43] | Athauda D , Maclagan K , Skene SS , Bajwa-Joseph M , Letchford D , Chowdhury K , Hibbert S , Budnik N , Zampedri L , Dickson J , Li Y , Aviles-Olmos I , Warner TT , Limousin P , Lees AJ , Greig NH , Tebbs S , Foltynie T ((2017) ) Exenatide once weekly versus placebo in Parkinson’s disease: A randomised, double-blind, placebo-controlled trial. Lancet 390: , 1664–1675. |
[44] | Athauda D , Wyse R , Brundin P , Foltynie T ((2017) ) Is exenatide a treatment for Parkinson’s disease? J Parkinsons Dis 7: , 451–458. |
