Skip to main content

Serum short-chain fatty acids and its correlation with motor and non-motor symptoms in Parkinson’s disease patients

Abstract

Background

Parkinson’s disease (PD) is associated with enteric nervous system dysfunction and gut microbiota dysbiosis. Short-chain fatty acids (SCFAs), derived from gut microbiota, are supposed to anticipate PD pathogenesis via the pathway of spinal cord and vagal nerve or the circulatory system. However, the serum concentration of SCFAs in PD patients is poorly known. This study aims to investigate the exact level of SCFAs in PD patients and its correlation with Parkinson’s symptoms.

Methods

50 PD patients and 50 healthy controls were recruited, and their demographic and clinical characteristics were collected. The serum concentration of SCFAs was detected using a gas chromatography-mass spectrometer. SCFAs were compared between PD and control groups. The correlation between serum SCFAs and Parkinson’s symptoms and the potential effects of medications on the serum SCFAs was analyzed.

Results

Serum propionic acid, butyric acid and caproic acid were lower, while heptanoic acid was higher in PD patients than in control subjects. However, only the serum level of propionic acid was correlated with Unified Parkinson’s Disease Rating Scale (UPDRs) part III score (R = -0.365, P = 0.009), Mini-mental State Examination (MMSE) score (R = -0.416, P = 0.003), and Hamilton Depression Scale (HAMD) score (R = 0.306, P = 0.03). There was no correlation between other serum SCFAs and motor complications. The use of trihexyphenidyl or tizanidine increased the serum concentration of propionic acid.

Conclusions

Serum SCFAs are altered in PD patients, and the decrease of serum propionic acid level is correlated with motor symptoms, cognitive ability and non-depressed state. Thus, the gut microbial-derived SCFAs potentially affect Parkinson’s symptoms through the blood circulation. Propionic acid supplementation might ameliorate motor and non-motor symptoms of PD patients, although clinical trials are needed to test this hypothesis.

Peer Review reports

Background

Short-chain fatty acids (SCFAs) are saturated aliphatic organic acids with one to six carbons, and produced by gut microbiota through fermentation of dietary fiber [1]. After being produced and absorbed in the gut, SCFAs are transported to the liver, and some of them enter the systemic blood circulation system [2]. The gut microbiota is disturbed in patients of Parkinson’s disease (PD), which is associated with motor or non-motor symptoms [3,4,5,6,7,8]. SCFAs or metabolites from gut microbiota are also changed in the feces or serum of PD patients [9, 10]. SCFAs have extensive physical effects on cellular energy metabolism, cholesterol biosynthesis, anti-inflammatory, and immune system regulation [11]. However, it remains debated whether SCFAs modulate the function of central nervous system through interacting with gastrointestinal, vagal nerves and spinal cord or directly acting on brain cells [12, 13]. Recently, activation of free fatty acid receptor 3 (FFAR3) was reported to attenuate the motor deficits and dopaminergic neuronal loss in a 6-hydroxydopamine-induced PD mouse model [14]. In this study, we aimed to determine the concentration of SCFAs in the serum of PD patients and investigate the relationship between serum SCFAs and Parkinson’s symptoms.

Methods

Subjects

PD patients were recruited from Taizhou Hospital of Zhejiang Province from July, 2020 to January, 2021. The inclusion criteria were: (1) agreement to participate in the research; (2) aged between 60 and 75 years; (3) diagnosis of PD according to Diagnostic criteria of Parkinson’s disease in China (2016 edition) [15]; (4) no use of antibiotics for three months; (5) no use of omega-3, probiotics for two weeks; (6) no use of lipid-lowering medicine for one month. The healthy controls were recruited from Health Management Center at Taizhou Hospital of Zhejiang Province from July, 2020 to January, 2021 and met the above inclusion criteria except diagnosis of PD.

Exclusion criteria were: (1) secondary parkinsonism, atypical parkinsonism, Alzheimer’s disease, cerebrovascular disease or other central nervous system diseases; (2) celiac disease; (3) chronic pancreatitis; (4) history of gastrointestinal surgery; (5) inflammatory bowel disease; and (6) history of cancer within three years.

Clinical evaluation

All participants provided demographics of age, sex, smoke, alcohol consumption, hypertension, diabetes mellitus, liver function index, lipid profiles, and medical history. For all patients, the following data were recorded: disease duration from onset to study, Hoehn-Yahr stage, motor symptom related-Unified Parkinson’s Disease Rating Scale (UPDRS) part III score, motor complications (end-of-dose phenomenon, dyskinesia, and freezing), non-motor symptoms (cognitive impairment, anxiety, depression, paresthesia, dysautonomia, sleep disorders and rapid-eye-movement sleep behavior disorder (RBD)), medication usage and Levodopa-equivalent daily dose (LEDD). The UPDRS scores were rated during the on state. Motor complications and paresthesia were evaluated by clinical face-to-face interviews with patients. Sleep disorders was evaluated according to Parkinson disease sleep scale. Autonomic function was evaluated using the Scale for Outcomes in Parkinson’s Disease-Autonomic Dysfunction. Patients that often had symptoms related to postural changes, urinary dysfunction caused by non-primary or secondary causes of the urinary system, or constipation, were considered to have dysautonomia. The cognitive state was evaluated using Mini-mental State Examination (MMSE) scale. The anxiety and depression states were evaluated using Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD), respectively. RBD was diagnosed with REM Sleep Behavior Disorder Questionnaire Hong Kong (RBDQ-HK). All the assessments were performed by two experienced physicians specialized in movement disorders (Suzhi Liu and Yajing Wang, with 21 and 10 years of work experience on Parkinson’s Disease, respectively). LEDD was calculated according to the reference [16].

Collection of serum samples

Blood was sampled into serum-separating tubes by professional nurses in the morning after overnight fasting. After 30 min at the room temperature, the blood samples were centrifuged at 4000 rpm for 5 min at 4 °C. The serum was collected into a 1.5 mL Eppendorf tube and stored at −80 °C until assay.

Determination of SCFAs concentrations

Concentrations of serum SCFAs (including heptanoic acid) were analyzed using a gas chromatography-mass spectrometer (GC-MS). Serum samples were deproteinized by phosphoric acid and extracted with ether. After being centrifuged at 4000 rpm for 10 min, the supernatant was collected and injected to GC-MS. An Agilent 7890B-7000D GC-MS was fitted with a capillary column HP-INNOWAX 25 m × 0.20 mm × 0.4 μm. The injector temperature was 240 °C, and the carrier gas flow rate was set to 1 mL/min. The ion source and transmission line temperatures were 200 and 250 °C, respectively. The electron bombarding voltage was 70 eV, and single ion monitoring was applied.

Statistical analysis

Continuous variables were expressed as mean ± Std.Error and compared using student’s t-test if data were normally distributed or otherwise, expressed as median (IQR) and analyzed using Mann-Whitney U test. The Kolmogorov-Smirnov test was used to evaluate the normality of the distribution of the variables. Categorical variables were expressed as number (%) and compared by χ2 test or Fisher’s exact test. A correlation was investigated using Spearman nonparametric correlation analysis method. Statistical significance values were set at α ≤ 0.05 (two-sided), and correlation magnitude levels were defined as weak (<0.3), moderate (0.3–0.59), and strong (≥0.6). Data analysis was conducted using SPSS software, version 16.0 (SPSS Inc.). Multiple testing of the nine SCFAs between PD and controls was then corrected by Bonferroni method. The potential confounding variables predictive of SCFAs was determined by multiple linear regression using stepwise method.

Results

Clinical and laboratory characteristics of participants

50 PD patients and 50 normal controls were recruited in this study. The main clinical characteristics and laboratory results are described in Table 1. No difference was observed in age and gender between PD patients and controls (P = 0.450 and 0.675 respectively). No difference existed in the smoke and alcohol consumption rate between PD patients and controls (P = 0.269 and 0.359 respectively). PD and controls exhibited the same rate of hypertension and diabetes mellitus comorbidity (both P = 1.000). The two groups exhibited no difference in laboratory characteristics, including bilirubin, creatinine, total cholesterol, triglycerides, or high-density lipoprotein cholesterol. However, low-density lipoprotein cholesterol in PD group was significantly lower than in the control group (mean ± Std.Error, PD vs. control, 2.91 ± 0.09 vs 2.47 ± 0.12, P = 0.005) (Table 1).

Table 1 Clinical and laboratory characteristics of the participants

PD patients had a disease duration of 51.5(48) (median (IQR)) months and the Hoehn-Yahr stage was 2.5(0.5). The UPDRS part III score was 42.82 ± 2.20 (mean ± Std.Error) and MMSE score was 20.24 ± 0.73 (mean ± Std.Error). In addition, patients with longer disease duration appeared to have higher UPDRS part III score (R = 0.494, P = 0.000) (Supplementary Fig. 1). The prevalence of motor complications such as end-of-dose phenomenon, dyskinesia, and freezing was described. The prevalence of non-motor symptoms, including anxiety, depression, paresthesia, dysautonomia, sleep disorders and RBD in PD patients was calculated. The usage of anti-Parkinson’s agent was also calculated and LEDD is 555.5 ± 38.9 mg (Table 1).

The levels of serum SCFAs in PD patients and healthy controls

The levels of serum propionic, butyric and caproic acids were significantly lower in PD group than in control group after correction by Bonferroni method (P < 0.0056 (0.05/9)). Heptanoic acids were significantly higher in PD patients than controls (P < 0.0056). No differences were observed between the two groups in pentanoic acid (P = 0.008), isobutyric acid (P = 0.019), acetic acid (P = 0.072), isovaleric acid (P = 0.450), or isocaproic acid (P = 0.937) (Fig. 1).

Fig. 1
figure 1

Comparison of peripheral serum concentrations of acetic acid, propionic acid, butyric acid, and isobutyric acid (A) as well as isovaleric acid, pentanoic acid, isocaproic acid, caproic acid, and heptanoic acid (B) between PD and control participants. Green represents control (n = 50) and red is PD patients (n = 50). *, P < 0.0056 (0.05/9)

Although demographic variables of PD and control group were similar, multiple linear regression using stepwise method was applied to determine the explanatory variables predictive of SCFAs. The model showed that: 1) PD (P < 0.000), smoke (P = 0.022), triglycerides (P = 0.019) and creatinine (P = 0.047) were significant predictors for the decrease of propionic acid, accounting for 39.6% (P = 0.000) of the variance indicated by the adjusted R-squared; 2) PD (P < 0.000), alcohol consumption (P < 0.001) and low-density lipoprotein cholesterol (P = 0.044) were significant predictors for the decrease of butyric acid, accounting for 42.3% (P = 0.000) of the variance; 3) PD (P = 0.005), alcohol consumption (P = 0.005) and high-density lipoprotein cholesterol (P = 0.038) were significant predictors for the decrease of caproic acid, accounting for 15.2% (P = 0.000) of the variance; 4) PD (P = 0.021) was also a significant predictor for the increase of heptanoic acid, accounting for 4.3% (P = 0.021) of the variance.

Correlation of serum SCFAs with motor symptoms in PD

In order to evaluate the relationship between SCFAs and PD progression, the contents of SCFAs in different Hoehn-Yahr stages were analyzed. As there were only 2 and 1 patient in stage 1 and 4 respectively, patients at stage 1 and stage 2, and patients at stage 3 and stage 4 were pooled in the analysis. Between these two pooled groups of patients (stage 1–2 vs. 3–4), the levels of serum SCFAs were not significantly different (p > 0.05). The motor symptom in PD was evaluated by UPDRS part III. We observed that only the serum level of propionic acid was correlated with UPDRS part III scores (Supplementary Table 1). The correlation coefficient of propionic acid with UPDRS part III scores was −0.365, and P-value was 0.009, indicating moderate correlation magnitude levels (Fig. 2A).

Fig. 2
figure 2

Correlation between serum propionic acid and Parkinson’s symptoms. A Serum propionic acid is significantly negatively correlated with UPDRS part III score in PD patients (n = 50), R = -0.365, P = 0.009. B Scatterplots show a negative correlation between propionic acid concentration and MMSE score, R = -0.416, P = 0.003. (C) Scatterplots show a positive correlation between propionic acid concentration and HAMD score, R = 0.306, P = 0.03. Spearman nonparametric correlation test was used in the correlation analysis. MMSE, Mini-mental State Examination. HAMD, Hamilton Depression Scale. UPDRS, Unified Parkinson’s Disease Rating Scale

Correlation of serum SCFAs with non-motor symptoms and motor complications in PD

After we observed a correlation between serum SCFA and motor symptoms in PD patients, we analyzed the potential relation between SCFAs and non-motor symptoms. We observed that only the level of serum propionic acid was correlated with cognitive impairment and depression. As shown in Fig. 2, B and C, serum propionic acid was negatively correlated with MMSE score (R = -0.416, P = 0.003), but positively correlated with HAMD score (R = 0.306, P = 0.03). Notably, HAMD score was negatively correlated with MMSE score (R = -0.375, P = 0.007) (Supplementary Fig. 2), implying that depressed patients exhibit cognitive impairment or vice versa.

No correlation was observed between SCFAs and motor complications. The serum SCFAs concentration was comparable in patients with and without motor complications (data not shown).

Relation of anti-Parkinson medication to serum SCFAs

All PD patients were administered with anti-PD drug. To investigate the influence of anti-Parkinson medication on peripheral blood SCFAs, the serum SCFAs concentration was compared between people who received a specific anti-Parkinson medication and those who did not receive it. The results showed that the serum SCFAs appeared not to be influenced by levodopa administration as correlation between SCFAs and LEDD was not observed. However, serum propionic acid concentration was significantly higher in PD patients taking trihexyphenidyl (n = 14) (P = 0.028) (Fig. 3A) or tizanidine (n = 16) (P = 0.032) (Fig. 3B). Other anti-Parkinson medication’s influence on serum SCFAs concentration was not observed.

Fig. 3
figure 3

The effect of anti-Parkinson medications on peripheral venous concentrations of SCFAs in PD patients. A Serum propionic acid concentration was higher in PD patients taking trihexyphenidyl (n = 14) (P = 0.028). B Serum propionic acid concentration was higher in PD patients taking tizanidine (n = 16) (P = 0.032)

Discussion

Gastrointestinal symptoms often appear prior to motor symptoms. Current researches have demonstrated intestinal microbial disturbances in PD patients [3, 17]. The disorder of intestinal flora is not only related to Parkinson’s motor phenotype [7], but also to its non-motor symptoms [5, 6, 8]. It is hypothesized that intestinal flora and their metabolites play a direct role in the pathogenesis of PD [17, 18]. SCFAs are derived from gut microbial metabolism and alter brain function not only through gastrointestinal, spinal cord, and vagal nerves but also by directly interacting with brain cells after circulating in the blood system [12, 13, 19, 20]. However, the exact concentration of SCFAs in the blood of PD patients and its association with Parkinson’s symptoms are unclear. To the best of our knowledge, this study is the first research to examine SCFAs in serum specimens from PD patients. Moreover, we investigated the correlation of serum SCFAs with motor or non-motor symptoms, as well as motor complications of PD.

The results indicated that clinical and laboratory characteristics remained relatively consistent between PD patients and healthy controls. We observed that levels of propionic acid, butyric acid and caproic acid decreased, while, the level of heptanoic acid increased in PD patients compared with healthy controls. The differences in propionic acid and butyric acid are in line with the alterations of gut microbes in PD. In the feces of PD patients, the abundance of Prevotella, which produces propionate, decreases [21], and the abundance of putative-butyrate–producing bacteria, such as Faecalibacterium, Prausnitzii, Blautia, Coprococcus, Roseburia, and Eubacterium, also decreases [1, 4, 9,10,11]. Thus, the decreased propionic acid and butyric acid in the serum might arise from alteration of intestinal flora. Numerous studies have identified increased abundance of putative-acetate-producing bacteria, such as Bifidobacterium, Lactobacillus, Clostridium clusters, and Akkermansia muciniphila, reduced abundance of Prevotella, Bacteroides, Blautia, Clostridium spp., and Ruminococcus [1, 11] in Parkinson’s disease. The overall equilibrium of putative-acetate-producing bacteria might result in the same amount of metabolized acetic acid and subsequent serum acetic acid in Parkinson patients as controls in present study.

Nutrient intake was supposed to regulate the generation of SCFAs and modify PD pathogenesis [22, 23]. It was reported that PD patients had a high intake of dietary fiber, which is the source of SCFAs [24]. In our study, we observed that most types of SCFAs decrease in PD patients. Thus, the abnormal serum SCFAs is mainly due to the intestinal microbial disorders instead of nutrient patterns of PD patients.

We observed that propionic acid in the serum was moderately negatively correlated with UPDRS part III score of PD patients, which is consistent with a previous study [25]. Although propionic acid might act on FFAR3 in the gut and ameliorate motor deficits and dopaminergic neuron loss in 6-hydroxydopamine-induced PD mice [14], it is possible that the circulating propionic acid directly protects neurons in the brain. An in vitro experiment showed that treatment with propionic acid prevented dopaminergic neurons from the neurotoxicity of rotenone and enhanced the outgrowth of neurites [26]. Moreover, as an HDAC inhibitor [11], propionic acid might also inhibit the neuroinflammatory activation and attenuate the damage of blood-brain-barrier [27], the two characteristic pathological changes in PD brain [28].

Moreover, propionic acid in the serum was negatively correlated with MMSE score, suggesting that lower serum propionic acid is linked to normal cognitive ability in PD. It was reported that the abundance of genus Ruminococcus, a putative propionic acid producer, was decreased significantly in the moderate cognitive impairment group than that of a normal cognitive group in PD patients. In addition, its abundance negatively correlated with cognition ability in PD [8]. It has been shown that patients with propionic acidemia often display cognitive deficits [29]. A chronic subcutaneous injection of propionic acid induces cognitive dysfunction in adult rats [30]. Moreover, the serum level of propionic acid was positively correlated with HAMD score, which suggests that propionic acid affects the depression status of PD patients. Family Ruminococcaceae and fecal propionic acid decrease in depressed mice compared to control mice [31], however, intraperitoneal injection of propionic acid at a low dose could inhibit the social motivation of rats [32]. Thus, the administration route should be considered and evaluated if propionic acid is supplemented to PD patients [33].

The microbial metabolism affects the pharmacokinetics of neuromodulatory drugs, and on the other way around, medication can alter gut microbiota composition and SCFAs production [34, 35]. Indeed, administration of anti-PD drugs, trihexyphenidyl and tizanidine, increased the serum concentration of propionic acid. However, since the gut microbiota was not identified in this study, a clear relationship between anti-PD therapy, gut microbiota, and SCFAs could not be clarified. As described above, serum propionic acid is related to cognitive impairment and depression in PD, however, treatments of trihexyphenidyl or tizanidine were not correlated with MMSE or HAMD scores (data not shown).

It is similar with previous studies [36,37,38,39] that the levels low-density lipoprotein cholesterol decreases in PD patients. We observed that low-density lipoprotein cholesterol was weakly correlated with acetic, propionic, and butyric acids in the serum of all participants including PD patients and healthy controls; however, in the separate PD group, the serum level of low-density lipoprotein cholesterol was not correlated with any SCFAs, UPDRS part III score, non-motor symptoms or motor complications (data not shown). Thus, low-density lipoprotein cholesterol dose not interfere with the effects of serum SCFAs levels on Parkinson’s symptoms, although it mignt interact with SCFAs.

There are three limitations in this study: 1, the sample size was small, which limited the number of patients with Hoehn-Yahr stages 1 and 4; and 2, the fecal microbiota and SCFAs were not analyzed, which made it unclear how the gut bacteria affect serum SCFAs; and 3, serum SCFAs in drug naive PD patients and their correlation with Parkinson’s symptoms were not investigated. A more comprehensive study is still needed.

Conclusions

In summary, our study provides additional evidence for the alteration of serum SCFAs in PD patients. We further observed that the serum level of propionic acid was decreased and associated with both motor and non-motor symptoms (cognitive dysfunction and depression status of PD patients). In addition, we found that some anti-Parkinson medications such as trihexyphenidyl and tizanidine affected serum propionic acid. Our study supports that the gut bacteria-derived SCFAs could act on brain cells through circulating in the blood stream. Clinical trials are needed to investigate whether the supplement of propionic acid could improve the motor symptoms and mental functions of PD patients.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

FFAR:

Free Fatty Acid Receptor

GC-MS:

Gas Chromatography-Mass Spectrometer

HAMA:

Hamilton Anxiety Scale

HAMD:

Hamilton Depression Scale

HDAC:

Histone Deacetylase

IQR:

Interquartile Range

LEDD:

Levodopa-Equivalent Daily Dose

MAOI:

Monoamine Oxidase Inhibitor

MMSE:

Mini-mental State Examination

PD:

Parkinson’s disease

RBD:

Rapid eye movement Sleep Behavior Disorder

RBDQ-HK:

Rapid eye movement Sleep Behavior Disorder Questionnaire Hong Kong

SCFAs:

Short-chain fatty acids

UPDRs:

Unified Parkinson’s Disease Rating Scale

References

  1. 1.

    Koh A, De Vadder F, Kovatcheva-Datchary P, Bäckhed F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell. 2016;165(6):1332–45. https://doi.org/10.1016/j.cell.2016.05.041.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Morrison DJ, Preston T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes. 2016;7(3):189–200. https://doi.org/10.1080/19490976.2015.1134082.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Yang D, Zhao D, Ali Shah SZ, Wu W, Lai M, Zhang X, et al. The role of the gut microbiota in the pathogenesis of Parkinson's disease. Front Neurol. 2019;10:1155. https://doi.org/10.3389/fneur.2019.01155.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Keshavarzian A, Green SJ, Engen PA, Voigt RM, Naqib A, Forsyth CB, et al. Colonic bacterial composition in Parkinson's disease. Mov Disord. 2015;30(10):1351–60. https://doi.org/10.1002/mds.26307.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Qian Y, Yang X, Xu S, Wu C, Song Y, Qin N, et al. Alteration of the fecal microbiota in Chinese patients with Parkinson’s disease. Brain Behav Immun. 2018;70:194–202. https://doi.org/10.1016/j.bbi.2018.02.016.

    Article  PubMed  Google Scholar 

  6. 6.

    Baldini F, Hertel J, Sandt E, Thinnes CC, Neuberger-Castillo L, Pavelka L, et al. Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions. BMC Biol. 2020;18:1:62. https://doi.org/10.1186/s12915-020-00775-7.

    CAS  Article  Google Scholar 

  7. 7.

    Scheperjans F, Aho V, Pereira P, Koskinen K, Paulin L, Pekkonen E, et al. Gut microbiota are related to Parkinson's disease and clinical phenotype. Mov Disord. 2015;30(3):350–8.

    Article  Google Scholar 

  8. 8.

    Ren T, Gao Y, Qiu Y, Jiang S, Zhang Q, Zhang J, et al. Gut Microbiota Altered in Mild Cognitive Impairment Compared With Normal Cognition in Sporadic Parkinson's Disease. Front Neurol. 2020;11:137. https://doi.org/10.3389/fneur.2020.00137.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Unger MM, Spiegel J, Dillmann KU, Grundmann D, Philippeit H, Bürmann J, et al. Short chain fatty acids and gut microbiota differ between patients with Parkinson's disease and age-matched controls. Parkinsonism Relat Disord. 2016;32:66–72. https://doi.org/10.1016/j.parkreldis.2016.08.019.

    Article  PubMed  Google Scholar 

  10. 10.

    Cirstea MS, Yu AC, Golz E, Sundvick K, Kliger D, Radisavljevic N, et al. Microbiota Composition and Metabolism Are Associated With Gut Function in Parkinson's Disease. Mov Disord. 2020;35(7):1208–17. https://doi.org/10.1002/mds.28052.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Fernández J, Redondo-Blanco S, Gutiérrez-del-Río I, Miguélez EM, Villar CJ, Lombó F. Colon microbiota fermentation of dietary prebiotics towards short-chain fatty acids and their roles as anti-inflammatory and antitumour agents: A review. J Funct Foods. 2016;25:511–22. https://doi.org/10.1016/j.jff.2016.06.032.

    CAS  Article  Google Scholar 

  12. 12.

    Sampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, et al. Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson's Disease. Cell. 2016;167(6):1469–80.e12. https://doi.org/10.1016/j.cell.2016.11.018.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Mulak A. A controversy on the role of short-chain fatty acids in the pathogenesis of Parkinson's disease. Mov Disord. 2018;33(3):398–401. https://doi.org/10.1002/mds.27304.

    Article  PubMed  Google Scholar 

  14. 14.

    Hou YF, Shan C, Zhuang SY, Zhuang QQ, Ghosh A, Zhu KC, et al. Gut microbiota-derived propionate mediates the neuroprotective effect of osteocalcin in a mouse model of Parkinson's disease. Microbiome. 2021;9(1):34. https://doi.org/10.1186/s40168-020-00988-6.

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Group of Parkinson's Disease and Movement Disorders NBoCMAPCoPsDaMDoNBoCPA. Diagnostic criteria of Parkinson's disease in China (2016 edition). Chin J Neurol. 2016;49(4):268–71. https://doi.org/10.3760/cma.j.issn.1006-7876.2016.04.002.

    Article  Google Scholar 

  16. 16.

    Tomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE. Systematic review of levodopa dose equivalency reporting in Parkinson's disease. Mov Disord. 2010;25(15):2649–53. https://doi.org/10.1002/mds.23429.

    Article  PubMed  Google Scholar 

  17. 17.

    Nair AT, Ramachandran V, Joghee NM, Antony S, Ramalingam G. Gut Microbiota Dysfunction as Reliable Non-invasive Early Diagnostic Biomarkers in the Pathophysiology of Parkinson&rsquo;s Disease: A Critical Review. J Neurogastroenterol Motil. 2018;24(1):30–42. https://doi.org/10.5056/jnm17105.

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Chapelet G, Leclair-Visonneau L, Clairembault T, Neunlist M, Derkinderen P. Can the gut be the missing piece in uncovering PD pathogenesis? Parkinsonism Relat Disord. 2019;59:26–31. https://doi.org/10.1016/j.parkreldis.2018.11.014.

    Article  PubMed  Google Scholar 

  19. 19.

    Yano Jessica M, Yu K, Donaldson Gregory P, Shastri Gauri G, Ann P, Ma L, et al. Indigenous Bacteria from the Gut Microbiota Regulate Host Serotonin Biosynthesis. Cell. 2015;161(2):264–76. https://doi.org/10.1016/j.cell.2015.02.047.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Martin CR, Osadchiy V, Kalani A, Mayer EA. The Brain-Gut-Microbiome Axis. Cell Mol Gastroenterol Hepatol. 2018;6(2):133–48. https://doi.org/10.1016/j.jcmgh.2018.04.003.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Chen T, Long W, Zhang C, Liu S, Zhao L, Hamaker BR. Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota. Sci Rep. 2017;7(1):2594. https://doi.org/10.1038/s41598-017-02995-4.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Powers KM, Smith-Weller T, Franklin GM, Longstreth WT, Swanson PD, Checkoway H. Parkinson’s disease risks associated with dietary iron, manganese, and other nutrient intakes. Neurology. 2003;60(11):1761–6. https://doi.org/10.1212/01.Wnl.0000068021.13945.7f.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Palavra NC, Lubomski M, Flood VM, Davis RL, Sue CM. Increased Added Sugar Consumption Is Common in Parkinson's Disease. Frontiers. Nutrition. 2021;8:207. https://doi.org/10.3389/fnut.2021.628845.

    CAS  Article  Google Scholar 

  24. 24.

    Baert F, Matthys C, Mellaerts R, Lemaître D, Vlaemynck G, Foulon V. Dietary Intake of Parkinson's Disease Patients. Front Nutr. 2020;7:105. https://doi.org/10.3389/fnut.2020.00105.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Shin C, Lim Y, Lim H, Ahn TB. Plasma Short-Chain Fatty Acids in Patients With Parkinson's Disease. Mov Disord. 2020;35(6):1021–7. https://doi.org/10.1002/mds.28016.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Ostendorf F, Metzdorf J, Gold R, Haghikia A, Tönges L. Propionic Acid and Fasudil as Treatment Against Rotenone Toxicity in an In Vitro Model of Parkinson's Disease. Molecules. 2020;25(11). https://doi.org/10.3390/molecules25112502.

  27. 27.

    Hoyles L, Snelling T, Umlai UK, Nicholson JK, Carding SR, Glen RC, et al. Microbiome-host systems interactions: protective effects of propionate upon the blood-brain barrier. Microbiome. 2018;6(1):55. https://doi.org/10.1186/s40168-018-0439-y.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Guan J, Pavlovic D, Dalkie N, Waldvogel HJ, O'Carroll SJ, Green CR, et al. Vascular degeneration in Parkinson's disease. Brain Pathol. 2013;23(2):154–64. https://doi.org/10.1111/j.1750-3639.2012.00628.x.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Pena L, Burton BK. Survey of health status and complications among propionic acidemia patients. Am J Med Genet A. 2012;158a(7):1641–6. https://doi.org/10.1002/ajmg.a.35387.

    Article  PubMed  Google Scholar 

  30. 30.

    Pettenuzzo LF, Schuck PF, Fontella F, Wannmacher CM, Wyse AT, Dutra-Filho CS, et al. Ascorbic acid prevents cognitive deficits caused by chronic administration of propionic acid to rats in the water maze. Pharmacol Biochem Behav. 2002;73(3):623–9. https://doi.org/10.1016/s0091-3057(02)00856-0.

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Wu M, Tian T, Mao Q, Zou T, Zhou CJ, Xie J, et al. Associations between disordered gut microbiota and changes of neurotransmitters and short-chain fatty acids in depressed mice. Transl Psychiatry. 2020;10(1):350. https://doi.org/10.1038/s41398-020-01038-3.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Lobzhanidze G, Lordkipanidze T, Zhvania M, Japaridze N, MacFabe DF, Pochkidze N, et al. Effect of propionic acid on the morphology of the amygdala in adolescent male rats and their behavior. Micron. 2019;125:102732. https://doi.org/10.1016/j.micron.2019.102732.

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Song W, Guo X, Chen K, Chen X, Cao B, Wei Q, et al. The impact of non-motor symptoms on the Health-Related Quality of Life of Parkinson's disease patients from Southwest China. Parkinsonism Relat Disord. 2014;20(2):149–52. https://doi.org/10.1016/j.parkreldis.2013.10.005.

    Article  PubMed  Google Scholar 

  34. 34.

    Hill-Burns EM, Debelius JW, Morton JT, Wissemann WT, Lewis MR, Wallen ZD, et al. Parkinson's disease and Parkinson's disease medications have distinct signatures of the gut microbiome. Mov Disord. 2017;32(5):739–49. https://doi.org/10.1002/mds.26942.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Jameson KG, Olson CA, Kazmi SA, Hsiao EY. Toward Understanding Microbiome-Neuronal Signaling. Mol Cell. 2020;78(4):577–83. https://doi.org/10.1016/j.molcel.2020.03.006.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Wei Q, Wang H, Tian Y, Xu F, Chen X, Wang K. Reduced serum levels of triglyceride, very low density lipoprotein cholesterol and apolipoprotein B in Parkinson's disease patients. PLoS One. 2013;8(9):e75743. https://doi.org/10.1371/journal.pone.0075743.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Guo X, Song W, Chen K, Chen X, Zheng Z, Cao B, et al. The serum lipid profile of Parkinson's disease patients: a study from China. Int J Neurosci. 2015;125(11):838–44. https://doi.org/10.3109/00207454.2014.979288.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Fu X, Wang Y, He X, Li H, Liu H, Zhang X. A systematic review and meta-analysis of serum cholesterol and triglyceride levels in patients with Parkinson's disease. Lipids Health Dis. 2020;19:1:97. https://doi.org/10.1186/s12944-020-01284-w.

    CAS  Article  Google Scholar 

  39. 39.

    Saedi S, Hemmati-Dinarvand M, Barmaki H, Mokhtari Z, Musavi H, Valilo M, et al. Serum lipid profile of Parkinson's disease patients: A study from the Northwest of Iran. Caspian J Intern Med. 2021;12(2):155–61. https://doi.org/10.22088/cjim.12.2.155.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported in part by grants from Zhejiang Provincial Basic and Public Welfare Research Program (No. LGF20H090009 to Suzhi Liu, LGD20H310002 to Gang Wu); National Natural Science Foundations of China (No.81903584 to Gang Wu).

Author information

Affiliations

Authors

Contributions

1) a) conception, design; b) data acquisition; c) analysis, interpretation. 2) a) drafting the article; b) revising the article critically. 3) a) final approval of the version to be submitted. Gang Wu, 1a), 1b), 1c), 2a), 3a). Zhengli Jiang, 1b), 3a). Yaling Pu, 1b), 3a). Shiyong Chen, 1b), 3a). Tingling Wang, 1b), 1c), 2a), 3a). Yajing Wang, 1b), 3a). Shanshan Wang, 1b), 3a). Minya Jin, 1b), 3a). Yangyang Yao, 1b), 3a). Yang Liu, 2b), 3a). Shaofa Ke, 2b), 3a). Suzhi Liu, 1a), 2b), 3a). All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Shaofa Ke or Suzhi Liu.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the ethics committee of Zhejiang Province Taizhou Hospital (Number of Ethics documents: K20190536). All methods were performed in accordance with the Declaration of Helsinki. All enrolled subjects provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supp Table 1.

The correlation between serum SCFAs and UPDRS part III score. Supplementary Fig. 1. UPDRS part III score was significantly positively correlated with disease duration in PD patients (n = 50), R = 0.494, P = 0.000. Investigated by Spearman nonparametric correlation analysis method. UPDRS, Unified Parkinson’s Disease Rating Scale. Supplementary Fig. 2. MMSE score was significantly negatively correlated with HAMD score in PD patients, R = -0.375, P = 0.007. Investigated by Spearman nonparametric correlation analysis method. MMSE, Mini-mental State Examination. HAMD, Hamilton Depression Scale.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, G., Jiang, Z., Pu, Y. et al. Serum short-chain fatty acids and its correlation with motor and non-motor symptoms in Parkinson’s disease patients. BMC Neurol 22, 13 (2022). https://doi.org/10.1186/s12883-021-02544-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12883-021-02544-7

Keywords

  • Short-Chain Fatty Acids
  • Parkinson’s Disease
  • Cognitive Impairment
  • Depression
  • Propionic Acid
  • trihexyphenidyl
  • tizanidine