The protective role of cigarette smoking against Parkinson’s disease via moderation of the interaction between iron deposition in the nigrostriatal pathway and clinical symptoms

Introduction

Parkinson’s disease (PD) is characterized by neurodegeneration in the deep brain nuclei located in the nigrostriatal pathway (1), which can be reflected by changes in the iron deposition in these nuclei (2-5). Cigarette smoking has long been considered a health hazard linked to a variety of illnesses: it causes vascular complications and subclinical brain infarctions (6) by increasing oxidative stress, which deteriorates vascular function and promotes vascular remodeling in the brain (7). However, prior research has repeatedly revealed a strong reverse association between cigarette smoking and PD progression (8-11), in which smoking delays the onset of PD (12) and reduces the risk of PD by 41–58% (13). In contrast to non-smokers, most PD patients with a history of smoking have less severe motor symptoms, such as tremor (14) and levodopa-induced dyskinesia (15), but worse non-motor symptoms, including cognition, sleep disorders, and psychiatric disorders such as impulsive-compulsive behaviors (16-18). Some researchers have suggested that this reverse association is related to a pathophysiological rationale based on the imbalance between nicotinic cholinergic and dopaminergic neurotransmitter systems in the nigrostriatal pathway (19-21), but the exact mechanism is still unclear.

In consideration of previous studies, we aimed to demonstrate the impact of cigarette smoking on the interplay between PD progression and severity of clinical symptoms using quantified iron deposition as a proxy for PD pathology. We hypothesized that cigarette smoking rendered a positive impact on the interaction between iron deposition and PD symptoms by affecting key nodes within the nigrostriatal pathway. The quantitative susceptibility mapping (QSM) technique is a novel and robust magnetic resonance imaging (MRI) technique quantifying abnormal iron accumulation in the brain, which has been shown to align closely with the core pathology of PD (2-5). The QSM was used by our present study as a surrogate biomarker for PD pathology, with the aim to further the understanding of the protective role of cigarette smoking for PD. We present the following article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-21-1090/rc).

Methods

Participants

We performed a case-control, observational study for patients diagnosed by the Department of Neurology at the Second Affiliated Hospital of Zhejiang University School of Medicine, from 1 August, 2014, to 1 December, 2018. The exclusion criteria included a self-reported history of neurological or psychiatric disease, previous drug or alcohol use, or traumatic brain injury. All enrolled participants were able to tolerate MRI scans and cooperatively complete all the scale tests. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethical Review Board at Zhejiang University, and informed consent was provided by all participants. There were 227 patients with an initial diagnosis of PD, of which 122 were eventually diagnosed; of these, 35 patients with PD agreed to participate in the present study and were able to complete all the scale tests. The healthy controls (HCs; n=167) were recruited in and around Hangzhou City, including 66 males and 101 females, of which 45 males were enrolled and able to tolerate MRI scans. Thus, before the study started, we enrolled a total of 35 patients with PD (aged 57.75±8.06 years) and 47 age-matched HCs (aged 60.77±6.69 years). The diagnosis of PD was made according to the UK Parkinson’s Disease Society Brain Bank criteria (22). The patients had a longer than 12-hour withdrawal from medication before commencement of the study and were all off medication during the clinical battery assessment, which included the duration of disease, Hoehn and Yahr (H&Y) scale (23), Unified Parkinson’s Disease Rating Scale (UPDRS) total score, including part III scores, the tremor, rigidity, bradykinesia, and axial scores (24), the Mini-Mental State Examination (MMSE) (25), and cigarette consumption [e.g., smoking index (SI) (26)]. The duration of disease was defined as the time from when a patient became self-conscious of exhibiting parkinsonian signs.

To eliminate the confusing effect of smoking cessation, only continuous and current smokers were enrolled in the present study. As male smokers account for the vast majority of smokers in China (52.9% of men and 2.4% of women are current smokers in China) (27), we recruited exclusively male participants. We calculated the SI according to the formula proposed in a study by Indrayan et al. (26), as follows:

S=(3−a15)12∑pinixi−0.5−y; for S ≥0, and ∑pinixi≥0.5; otherwise, zero (use a=30 for a>30); where a is the age at the start of smoking, p_i is the proportion of smoke inhaled in the case of passive smoking (or adjustment for filter cigarettes or for other forms of smoking), x_i is the number of cigarettes smoked for n_i years, and y is the number of years elapsed since cessation by ex-smokers, which measures the present burden in an absolute sense and not the risk of smoking-related diseases (26). The non-smoking participants were defined as those who had smoked less than one-half of a cigarette-year in a lifetime (26).

We divided the participants into 4 subgroups based on smoking status (yes/no): 24 cases in the HC non-smoker subgroup (ns-HC, aged 60.66±6.25 years), 17 in the PD non-smoker subgroup (ns-PD, aged 57.71±8.65 years), 23 in the HC smoker subgroup (s-HC, aged 60.88±7.27 years), and 18 in the PD smoker subgroup (s-PD, aged 57.79±7.70 years). In total, 35 PD patients and 47 HCs were recruited into the present study, of which 41 participants were smokers and 41were non-smokers. We performed the normality test for all the epidemiological data using the Kolmogorov-Smirnov test. During the inter-group comparisons, we used one-way analysis of variance (ANOVA) and the Student’s t-test for data that were normally distributed; we used the non-parametric method for data that were not normally distributed. The statistical significance was set at P <0.05. The participants’ profiles are detailed in Table 1.

MRI acquisition and QSM data processing

All participants were scanned using an 8-channel head/neck coil on a 3.0 Tesla MRI scanner (GE Discovery 750, GE Healthcare, Milwaukee, WI, USA). The head was stabilized with restraining foam pads during the scanning, and earplugs were provided to decrease the noise. A 3D-multi-echo gradient recalled echo (GRE) sequence was used to acquire enhanced susceptibility-weighted angiography (ESWAN) images, and the imaging parameters were as follows: repetition time =33.7 ms; 1st echo time/spacing/8th echo time =4.556 ms/3.648 ms/30.092 ms; flip angle =20 degrees; field of view =240×240 mm²; matrix =416×384; slice thickness =2 mm. All the images obtained from the participants were carefully checked after scanning. Image quality was checked by 2 experienced radiologists to make sure participants had no visible head movements.

Phase images were calculated on a computer cluster using the Susceptibility Tensor Imaging (STI) Suite v3.0 software package (https://people.eecs.berkeley.edu/chunlei.liu/software.html). First, the raw phase images were unwrapped using the Laplacian method based on the sine and cosine functions of the phase angle, and then the normalized phase images were calculated (28-30). Next, the background phase information was removed using the spherical-mean-value filtering (V-SHARP) method (29). After the background phase removal process, tissue susceptibilities were calculated using the Streaking Artifact Reduction for QSM (STAR-QSM) method (31,32). The mean magnetic susceptibility mapping of each individual brain was used as the susceptibility reference.

Region of interest determination

The tissue susceptibility of native subcortical nuclei in the bilateral substantia nigra pars compacta (SNc) and pars reticulata (SNr), red nuclei, putamen, caudates, globi pallidus, and thalami was determined (shown in Figure 1) (2). The specific methods were as follows: (I) the native QSM image was registered to a newly constructed QSM template derived from a cohort of aging brains, using Advanced Neuroimaging Tools-Symmetric Normalization (ANTs-SyN) coregistration algorithms (33); (II) the labels covering the subcortical nuclei were defined in the QSM template; (III) the labels in the QSM template were then warped to the native QSM image space by inverting the transformation matrix calculated in the first step; (IV) manual refinement was performed to ensure segmentation precision using ITK-SNAP v3.2 software (http://www.itksnap.org). The regions of interest (ROIs) of each nucleus were extracted from all the slices of each participant where the nucleus could be found. A radiologist (with 5 years of experience in neuroimaging diagnosis) who was blind to the information of the participants, including disease condition and demographics, performed the ROI drawing twice. The ROIs of each participant were defined using the averaged masks of the repeatedly drawn ROIs. Finally, the mean tissue susceptibility of bilateral subcortical nuclei was calculated, indicating the iron content in each of the 14 nuclei.

Figure 1 The ROIs of the deep brain nuclei. (A) The anatomical diagram of the 14 nuclei; the legend on the right indicates the various colors representing different nuclei; (B) the native QSM image used in this study; (C) final labels in the data analysis after manual revision. ROIs, regions of interest; QSM, quantitative susceptibility mapping.

Total intracranial volume (TIV) calculation

The structural images were preprocessed using the Computational Anatomy Toolbox 12 (CAT12, http://www.neuro.uni-jena.de/cat/) of the statistical parametric mapping 12 (SPM12) software on the Matrix Laboratory (MATLAB) platform (MathWorks Inc., Natick, MA, USA). Briefly, the brain 3D T1-weighted images were normalized to the Montreal Neurological Institute (MNI) 152 template and then segmented into gray matter, white matter, and cerebrospinal fluid (CSF). After that, the TIV of individuals was separately calculated.

QSM statistical analysis and moderation analysis

The statistical analyses were performed using the software SPSS 26.0 (IBM Corp., Armonk, NY, USA). An inter-group, one-way analysis of covariance (ANCOVA) test followed by pair-wise comparisons was performed to compare the QSM values across the groups, with age and TIV as covariates. Another 2-way multivariate analysis of covariance (MANCOVA) test (2-tailed) was then performed with the covariates of age and TIV set to eliminate the effects from them, in which the two factors were PD condition and cigarette smoking status (yes/no) and the dependent variables were QSM values of the ROIs, as previously mentioned. To further find out where the significance came from, post-hoc pairwise t-tests were used following the ANCOVA test. A significance level of P<0.05 was applied, with correction for multiple comparisons using the Bonferroni correction.

Moderation analysis for PD patients was performed via the PROCESS macro (Model 1) with covariates of ages and TIV in SPSS (34). The conceptual form of the moderated model is presented in Figure 2. For each moderation model, separate analyses for the moderator variables (labelled M) of smoking status (yes/no) and the SI were run, with outcome variables (labelled Y) as quantified scoring of clinical symptoms (including H&Y stage, disease duration, total and each part of UPDRS scores, targeted symptoms such as axial symptoms, tremor, rigidity, bradykinesia scores, and MMSE scores) and predictor variables (labelled X) as QSM values derived from the nuclei, as mentioned earlier in the description of ROI drawing. The difference in path coefficients with statistical significance (P<0.05) between the two groups was taken as proof of the moderation effect in this path (35). A 5,000 times bootstrapping procedure was used to test the significance of the differences at levels of moderator variables. The variables were all mean-centered for construction of products in the moderated regression model.

Figure 2 The model of moderation analysis. The moderation analysis was performed based on Model 1 in the PROCESS macro using SPSS statistical software (version 3.5 by Andrew Hayes, http://www.afhayes.com). According to this model of moderated multiple regression, for each PD patient, the outcome variable (labelled Y) was quantified clinical symptoms, the predictor or independent variable (labelled X) was a QSM measure derived from each ROI as defined in the Methods section, and the moderator variable (labelled M) was specified for smoking status (yes/no). PD, Parkinson’s disease; QSM, quantitative susceptibility mapping; ROI, regions of interest.

Results

We carefully matched for other possible confounding factors across the four subgroups, including the duration of the condition, general severity of the disease, state of movement, and cigarette smoking status. Table 1 shows there were no significant inter-group differences in the demographic information.

QSM values

In the ANCOVA test, there was significant inter-group difference in the QSM values (F=1.573, P=0.022). In the following pair-wise comparisons, the difference was found to come from the right globus pallidus (ns-HC vs. s-PD, P=0.028), left red nucleus (ns-PD vs. s-HC, P=0.028), left SNc (ns-PD vs. s-HC, P=0.003) (Table 2, Figure S1). The MANCOVA test further studied the main and interaction effects of the variables. As shown in the results, on the smoking status, participants who did not smoke had significantly higher QSM values than those who smoked in ROIs of the bilateral globi pallidi (left: F=7.197, P=0.009; right: F=9.150, P=0.003), bilateral SNc (left: F=11.435, P=0.001; right: F=4.488, P=0.037), and right SNr (F=10.435, P=0.002). Besides, under the PD condition, the PD patients exhibited significantly higher QSM values than the HCs in the left red nucleus (F=5.327, P=0.024) (Table 3). However, the interactional effect between smoking and PD conditions was not significant (F=1.080, P=0.393). We provide the details of the susceptibility values of all deep brain nuclei (Table S1) and the ROIs with significant differences in QSM values across the four subgroups (Figure S2) in the Supplementary material section. The results of inter-group QSM value comparisons were also shown with age, TIV, and disease duration as covariates (please refer to Tables S2,S3).

Moderation analysis

By controlling the impacts of TIV and age, we found the statistically significant moderating effect of cigarette smoking status on the interactions of the QSM values of the left thalamus and the UPDRS total scores [B=−2,628.72, 95% confidence interval (CI): −5,199.90 to −57.53; t=−2.09; P=0.04) and rigidity scores (B=−823.74, 95% CI: −1,366.68 to −280.80; t=−3.10; P=0.03), of the left SNc and H&Y stages (B=29.27, 95% CI: 7.21 to 51.32; t=2.71; P=0.01) in patients with PD (detailed in Table 4), with a graphic presentation illustrated in Figure 3. Likewise, the statistically significant moderating effect of the SI on the interactions of the QSM values of the bilateral thalamus and the UPDRS III scores (left: B=−127.77, 95% CI: −255.11 to −0.43; t=−2.05; P<0.05; right: B=−138.33, 95% CI: −276.47 to −0.19; t=−2.05; P<0.05), rigidity (left: B=−47.52, 95% CI: −83.14 to −11.90; t=−2.73; P=0.01; right: B=−48.12, 95% CI: −87.21 to −9.02; t=−2.52; P=0.02), and bradykinesia scores (left: B=−63.12, 95% CI: −125.65 to −0.58; t=−2.06; P<0.05; right: B=−69.46, 95% CI: −137.16 to −1.75; t=−2.10; P=0.04), and of the right red nucleus and rigidity (B=−10.31, 95% CI: −20.26 to −0.35; t=−2.12; P=0.04) and bradykinesia (B=−19.85, 95% CI: −36.18 to −3.51; t=−2.48; P=0.02) scores (detailed in Table 5 and Figure 4). The disordinal interactions were plotted in the figures by illustrating crossed lines and used to predict clinical performances which were significantly moderated by cigarette smoking status and the SI (Tables 4,5 and Figures 3,4).

Figure 3 The moderating effect of the interaction of cigarette smoking status (yes/no) and QSM measures in the ROIs on clinical performance in PD patients, with covariates of TIV and age. The slopes changed with the moderator variable of smoking status and exhibited significant interactions illustrated by crossed lines in disordinal forms. (A,B) For non-smokers (blue line) the relationships between the QSM values of the left thalamus and the UPDRS total scores and rigidity severity were positive (the regression line slopes upwards), whereas for smokers (red line), the relationship was negative (the regression line slopes downwards). (C) In non-smokers (blue line), the relationship between the QSM values of the left SNc and the H&Y stages was negative but for smokers (red line) the relationship was positive. QSM, the quantitative susceptibility mapping; ROI, regions of interest; PD, Parkinson’s disease; SD, standard deviation; TIV, total intracranial volume; UPDRS, Unified Parkinson Disease Rating Scale; SNc, substantia nigra pars compacta.

Figure 4 The moderating effect of the interaction of cigarette smoking index and the susceptibility measures in the ROIs on clinical performance in PD patients, with covariates of TIV and age. The slopes changed with the moderator variable of smoking index in disordinal forms. (A-H) For heavy-burdened smokers (mean SI + SD, red line) the relationship between the susceptibility values of bilateral thalami and the UPDRS III score, rigidity and bradykinesia subscores were negative (the regression line slopes downwards), whereas for light-burdened smokers (mean SI − 1SD, blue line) the relationships were positive (the regression line slopes upwards). SI, smoking index; SD, standard deviation; QSM, quantitative susceptibility mapping; ROI, regions of interest; PD, Parkinson’s disease; TIV, total intracranial volume; UPDRS III, Unified Parkinson Disease Rating Scale III.

The moderation was shown up in PD patients by a significant interaction effect. As illustrated in Figure 3A,3B, for PD non-smokers (blue line) the relationships between the QSM values of the left thalamus and the UPDRS total scores and rigidity severity were positive (the regression line slopes upwards), whereas for PD smokers (red line) the relationship was negative (the regression line slopes downwards). However, as for the relationship between the QSM values of the left SNc and H&Y stages (Figure 3C), in PD non-smokers (blue line) the relationship was negative but for smokers (red line) the relationship was positive. As illustrated in Figure 4A-4H, for heavy-burdened smokers (evidenced by mean SI + 1SD, red line) the relationships between the QSM values of the bilateral thalami and the UPDRS III scores, rigidity, and bradykinesia subscores were negative (the regression line slopes downwards), whereas for light-burdened smokers (evidenced by mean SI − 1SD, blue line) the relationships were positive (the regression line slopes upwards).

Graphically, using smoking status as the moderator variable, when iron deposition of the left thalamus was low (evidenced by the mean QSM − 1SD, as below), PD smokers showed worse performance in general movement disability and rigidity compared with PD non-smokers; conversely, when iron of the left thalamus was high (evidenced by the mean QSM + 1SD, as below), PD smokers showed better general movement disability and rigidity compared with PD non-smokers. In contrast to PD non-smokers, PD smokers stayed at a less severe disease stage when iron deposition of the left SNc was low, but at later stages when iron deposition of the left SNc was high, disease severity increased. Furthermore, using the SI as the moderator variable, we found when iron deposition of the bilateral thalamus was low, heavy-burdened PD smokers (evidenced by mean SI + 1SD, as below) had worse rigidity, bradykinesia, and general movement performance compared to those with a light smoking burden (evidenced by mean SI − 1SD, as below); in contrast, when the iron deposition of the bilateral thalamus was high, heavy-burdened PD smokers showed better aforementioned performance compared with light-burdened PD smokers. Similar outcomes were also present in the right red nucleus, with both rigidity and bradykinesia symptoms.

Discussion

To the best of our knowledge, this was the first study to examine the impacts of different smoking practices (sustained and current smoking versus non-smoking) on PD by means of quantified iron deposition. Since iron overload and resulting neurotoxicity lead to dopamine degradation and alpha-synuclein increase in the nigrostriatal region (35,36), aberrant iron deposition has long been regarded as a marker of PD (37). The QSM technique is best known as a robust, reproducible, and automated technique for imaging tissue susceptibility values from MR phase maps (38,39). Several QSM studies have demonstrated a strong link between brain iron overload and disease severity of PD (2,4,40-42). This technique allows for the qualifiable measurement and distributional depiction of the regional iron concentration for PD patients (2-5).

Numerous studies have confirmed the strong link between cigarette smoking and PD (43-45). Epidemiological evidence has shown that smokers have a lower incidence or severity of PD. The casually protective effect of cigarette smoking on the risk of PD has been repeatedly demonstrated (13,44), with a risk reduction of up to 40% (45). Nicotine is the main ingredient of cigarettes that provides neuroprotection (46,47). It acts as a presynaptic nicotinic acetylcholine receptor (nAChR) agonist that protects against PD (48,49), with a rationale based on the imbalance between nicotinic cholinergic and dopaminergic neurotransmitter systems in the nigrostriatal (19-21). Given previous human and animal studies, the putative biological mechanisms behind nicotine are outlined as follows: (I) activation of nAChRs on dopaminergic terminals to promote dopamine release and improve the efficiency of dopamine transporters in the striatum (50-55); (II) inhibition of the production of free radicals as an antioxidant (56); (III) increase in the levels of both fibroblast growth and neurotrophic factors to stimulate dopaminergic neuron survival (21,57); (IV) elevation of cytochrome P450 enzymes to increase the inactivation of neurotoxins in the brain and delay the development of PD (58); (V) decrease in misfolding and binding of protein to prevent the aggregation of toxic protein α-synuclein (59) and Aβ (60) in the brain; and (VI) mitigation of intestinal inflammation in the gut to change the composition of microbiota and subsequently decrease neuroinflammation in the brain (61). In spite of the reduced incidence of PD that has been repeatedly proposed, no definitive conclusion on smoking has yet been reached since cigarette smoking is still harmful for general health and remains a risk factor for multiple disorders. Thus, caution should be used when interpreting this association as protective, and cigarette smoking is still not recommended.

A major finding in this study was that the smoking-moderated protection for PD symptoms of rigidity, bradykinesia, and general movement performance was consistently present only when the deep brain nuclei were more affected by PD (evidenced by increased iron deposition), regardless of smoking status or smoking index as a moderator variable. In other words, it appeared difficult for cigarette smoking to exacerbate the reduction of physical movement when the PD pathology was mild, whereas cigarette smoking appeared more likely to mitigate symptoms when the PD pathology was serious. Prior to the present study, there had been no other research with similar findings, and we attribute this interesting, double-edged role of cigarette smoking to the following. First, the smoking-moderated neuroprotection for PD fluctuates, dependent on smoking duration, intensity, and recentness. The neuroprotection is present in a temporal and dose-response manner in PD (62,63). Exposure-disease relationship differs, depending on smoking duration and intensity (62,64), with distinct outcomes (10,65). Acute nicotine administration increases dopamine outputs in the striatum, which initially depresses then increases locomotor activity (66). Chronic nicotine ingestion via transdermal patch, on the other hand, improves motor performance (67) whereas transient cigarette smoking deteriorates it (68). This is due to a high levodopa sensitivity induced by nicotine, which causes a subthreshold stimulation of dopamine release (65) and positive modulators of nAChRs by enhancing striatal dopaminergic function (50). Furthermore, different levels of smoking intensity have distinct impacts on PD. In PD mice models, high doses of nicotine exacerbated 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced toxicity (69), which was supported by another rat study that found nicotinic neuroprotection was dependent on low, not high, doses by inhibiting neuronal degeneration in the brain (70). Nevertheless, an open trial employing rising doses of transdermal nicotine for the treatment of PD found the opposite results, with the majority of patients tolerating high-dose nicotine and gaining improvement in their clinical symptoms (71). These findings can be explained by a direct action of nicotine, which is mediated by nAChR-pathways (72), or by a D3 receptor upregulation that promotes dopamine use in the central neurological system (73). The nicotinic therapeutic effect is closely connected to the formulations and dosages (68,74-76); the nicotine formulations include patch, gum, administration, nasal spray, and inhaler, and the doses fluctuate widely depending on the tolerance levels of the study participants (70,77). Thus, the reason for these differential results of studies may be related to the variance of smoking/nicotine intake, duration, and intensity among those studies (50). Second, cigarette smoking itself produces distinct effects on different PD motor symptoms. With respect to motor symptoms, some researchers found that the protective effect of smoking worked only on patients dominated by postural instability gait difficulties (PIGD) other than those caused by tremor (78), while other researchers drew the opposite conclusion (74). Likewise, in animal studies, cigarette smoking was found to be effective at managing levodopa-induced bradykinesia (79) and tremor (14). However, cigarette smoking does not benefit all PD motor manifestations. Restless legs syndrome is regarded an early sign of PD, which is aggravated by cigarette smoking (80,81). However, results from other studies using nicotine administration did not find modified motor symptoms (55,82,83), shedding doubts on a direct effect of either nicotine or smoking on PD motor symptoms (49,50). The reason for these differential results is still uncertain, but they may be related to the variance of study paradigms, including smoking habits of participants in those studies (50). Third, other non-motor symptoms are also influenced by smoking-moderated neuroprotection for PD. Those with PD who smoke more often complain of non-motor deficits, such as depression (84), memory difficulties (85), and psychiatric disorders (17,86,87). Severe depression in PD is associated with reduced individual sensation-seeking traits that are involved with mesocorticolimbic dopamine circuits, suggesting that PD, depression, and diminished sensation-seeking traits are likely to share a common neurological substrate (88). This assumption is supported by the decreased novelty seeking found in PD patients, making patients less prone to consume stimulants like cigarettes (89) and to subsequently change their smoking practices, such as duration and intensity. Memory problems usually predict the cognitive impairment of PD (90). The causal interaction between smoking and cognitive dysfunction is still unclear, but excessive iron deposition in deep brain nuclei could cause neurotoxicity, neuroinflammation, and the aggregation of Lewy bodies and abnormal build-up of alpha (α)-synuclein, leading to cognitive deficits in PD (91-94). Impulse-control disorder (ICD) is one of the most common psychiatric disorders in PD smokers, whereby smoking (19) and mutated genes (95) jointly participate in the upregulation of sensitivity in developing and maintaining behavioral symptoms. In addition, motor and non-motor symptoms co-interact in the context of smoking. For example, smoking exposure-associated negative mood may potentially worsen the motor symptoms of PD patients with a smoking history than those without (10). Tremor-dominated PD patients are more likely to be current smokers but present with fewer non-motor symptoms (78). Moreover, the transient deterioration of specific motor and non-motor symptoms has been observed in some PD patients with a smoking history (10,65). Taken together, we assumed that an unknown trigger point might exist for this exposure-disease relationship. In other words, once the pathological changes of PD reach a certain level, smoking may exert its effects in moderating neuroprotection against PD symptoms.

Another significant finding in this study by moderation analyses is that the thalamus, red nucleus, and SN were the nuclei most moderated by cigarette smoking, as evidenced by the brain iron-PD interaction. As a proxy for iron deposition, previous QSM studies have continuously demonstrated that changes in QSM values externalize excessive iron load in the nuclei, reflecting pathological changes in the PD brain (5,96-99). Notably, in this study, both smoking status and burdens moderated the interaction between the thalamic pathology and movement slowness (i.e., rigidity and bradykinesia) in PD smokers. Anatomically, the thalamus is located between the midbrain and cortices to relay and filter information for further processing (100), exhibiting different roles in PD clinical symptoms. Pathophysiologically, the thalamus plays a dual role in both cholinergic and dopaminergic systems, having the highest density of nAChRs in the brain and simultaneously serving as a central hub for the formation of the classical cortico-striato-nigro-thalamo-cortical and cerebello-thalamo-cortical pathways of PD (101-103). Many studies have found changes in the morphology and function of the thalamus in smokers relative to non-smokers (103,104), which are mainly attributed to the nicotine-induced regulation of nAChRs (105) and the dysfunction of the cortico-basal ganglia-thalamo-cortical reward circuit as well (106). The thalamus, as the region with the most accumulation of AChR, receives plenty of cholinergic inputs from the brainstem center (107). Thalamic cholinergic denervation has been repeatedly found in PD patients compared with controls (108,109), which is supported by the fact that anticholinergic drugs have been commonly used to suppress excessive movements, such as tremor symptom (10). As previously shown in a single-photon emission computerized tomography (SPECT) study, after smoking cessation, the upregulated nAChR levels of the smokers returned to normal (110), which suggested that the smoking-moderated upregulation may be temporary and delayed during the course of smoking. Consequently, the following neuroprotection is unlikely to be fixed. On this basis, the presence of thalamic neuroprotection for decreased movements of rigidity and bradykinesia rather than tremor is likely to indicate activation of the cholinergic system in the thalamus of PD patients. In addition to acetylcholine, certain components in cigarettes can irreversibly inhibit the activity of monoamine oxidases (MAOs) in the basal ganglia (111). For this reason, rasagiline is an MAO inhibitor currently used for the treatment of PD (112). Taken together, the thalamus does exert essential impacts on cigarette smoking and PD, but the causal relationships is unclear, and the outcomes are still variable. This suggests that smoking-moderated neuroprotection is the result of a joint effort involving multiple neurotransmitter systems.

Except for the thalamus, the red nucleus has been identified as one of the most significant iron deposition regions affected by PD (36,113). When using the SI as a moderator variable, the slope directions for rigidity and bradykinesia in the red nucleus were consistent with those of the bilateral thalami, suggesting a potential causal relationship between the red nucleus and the thalamus. The dentato-rubro-thalamic tract has been widely used as a deep brain stimulation target for tremor-specific suppression in PD (114), but in our moderation analysis the impaired red nucleus was found more closely correlated to rigidity and bradykinesia than tremor in PD patients. Similar to the findings in the thalamus, we considered that this was possibly due to receptor desensitization, suggesting the threshold of smoking burden indeed deserves special attention for studying the neuroprotection of cigarette smoking.

In addition, our present study showed the relationship between the iron deposition of the left SNc and the H&Y stages for PD smokers was positive; that is, cigarette smoking could not reverse progressive degradation in the left SNc. Based on our previous study, iron deposition in the SNc was found to be affected exclusively in the early stages of PD (4), which has been supported by other similar findings (115,116). The underlying mechanism is still unclear, but the potential reason is likely attributable to decreased neuromelanin in the SNc of PD, which has been shown to play an essential and exclusive role in PD (117-119). Moreover, the redistributed iron in the brain according to metabolism in the early stages of PD may be also involved (120). Supported by findings of previous animal studies, this disordinal moderating effect observed in our study suggested that nicotine-induced neuroprotection for PD was likely to act only on progressive striatal degenerative alterations, delaying and stopping the unhappened progression of PD, but not on reversing the damage to the dopaminergic system that has already occurred (20,121). Our findings suggest that cigarette smoking starts to take a protective effect by disrupting the iron redistribution process at an early stage of PD, which requires verification in future research.

This study had some limitations. First, we did not enroll past smokers into the study. The effect of previous smoking history on PD has remained unclear among different clinical studies (10,63) and, as yet, we have not found a direct link between the iron deposition in PD and that of ex-smokers; however, a temporal relationship between smoking history and PD and the potential mechanism for abnormal iron load has been highly suggested. Ex-smokers have a markedly decreased risk of developing PD compared with never smokers (43). Furthermore, the lower risk of PD among current and ex-smokers varies with smoking duration, intensity, and recentness (62). Further research needs to be conducted on the impact of different types of smoking habits on PD. Second, we used iron deposition of the major deep brain nuclei as a proxy for PD pathology, but this did not turn into iron deposition in cortical or white matter structures. The relationship between iron metabolism and changes in morphology is still uncertain, and more modals of data need to be collected in future. Third, the effect of cigarette smoking on PD varies with the smoking condition, as discussed in the paper, and longitudinal follow-up studies are necessary to better elucidate the role of cigarette smoking in PD.

Conclusions

To the best of our knowledge, this was the first study to investigate the impacts of current cigarette smoking on PD using quantified iron deposition as a surrogate biomarker for PD pathology. We concluded our major findings as follows: (I) cigarette smoking moderates the interaction between the iron content of the deep brain nuclei and rigidity, bradykinesia, and general performance of PD patients in a double-edged fashion; and (II) in the interaction between iron deposition and clinical PD symptoms, the thalamus, red nucleus, and SNc are the nuclei most affected by cigarette smoking. These findings may help understand the protective role of cigarette smoking against PD.

Acknowledgments

Funding: This work was supported by the National Natural Science Foundation of China (No. 81701647).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-21-1090/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-21-1090/coif). QG reports that this work was supported by the National Natural Science Foundation of China (No. 81701647). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethical Review Board at Zhejiang University and informed consent was provided by all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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