Plasma Metabolomic Markers of Insulin Resistance and Diabetes and Rate of Incident Parkinson’s Disease

INTRODUCTION

A link between Parkinson’s disease (PD) and diabetes was originally suggested by the observation that individuals with PD frequently have impaired glucose tolerance [1], and was later supported by the discovery that dysregulated pathways related to mitochondrial biogenesis and respiration may play a role in the pathophysiology of both diseases [2]. The results of epidemiological studies, however, have been inconsistent. A significant increase in PD risk among individuals with type 2 diabetes has been reported in a cohort in Finland [3], in registry-based studies in Denmark [4] and Taiwan [5], a record-linkage study in the UK [6], and in two cohorts in the U.S. [7, 8], but not in other large longitudinal studies [9, 10]. Although the reason for these inconsistent results is unclear, it is possible that the relationship between diabetes and PD is modified by other factors, such plasma uric acid [11].

Metabolomics studies suggest that altered plasma levels of some metabolites may be associated with insulin resistance and diabetes. For example, in a nested case-control study in the Framingham Offspring Study, elevated plasma levels of branched chain amino acids (BCAAs), including isoleucine, leucine, and valine, were be associated with incident diabetes up to 12 years prior to diagnosis [12]; these findings were replicated in subsequent analyses in the Framingham Heart Study and the Malmö Diet and Cancer Study [13]. Altered, often elevated, levels of a variety of plasma acylcarnitines, including short-, medium-, and long-chain acylcarnitines, have been linked in various investigations with both prevalent [14–16] and incident diabetes [17]. Similarly, plasma glutamate and glutamine levels have been linked with diabetes: high plasma glutamate, low plasma glutamine, and a low ratio of glutamine to glutamate have each been linked with insulin resistance and diabetes in the Framingham Heart Study and Malmö Diet and Cancer Study [13] as well as in the PREDIMED trial [18].

Aside from their associations with diabetes and insulin resistance, each of these metabolites could also affect nervous system functioning. Branched chain amino acids, for example, have been linked with other neurodegenerative diseases, including amyotrophic lateral sclerosis [19] and Huntington’s disease [20, 21]. Acylcarnitines, which are related to mitochondrial function and beta-oxidation of fatty acids, are involved in a number of functions in the brain, including not only mitochondrial function, but also lipid synthesis, altering and stabilizing membrane composition, and increasing antioxidant activity [22]. Plasma levels of glutamate are associated with smoking [23], a strong risk factor of PD [24], and plasma levels of glutamate were found to be decreased in PD cases relative to health controls in a small cross-sectional study [25]. Given the relationships between these metabolites and diabetes, the biological and epidemiological links between diabetes and PD, and the potential for each of these metabolites to influence nervous system functioning, it is plausible that these metabolites might also play a role in PD pathophysiology. To investigate this possibility, we report here a nested case-control study of the association between plasma BCAA, glutamate, glutamine, and acylcarnitine levels with incident PD.

METHODS

RESULTS

Baseline characteristics of the 698 participants included in these analyses are presented in Table 1. As expected due to matching, the age, sex, and time interval length distributions are comparable between cases and controls. The distributions of BMI and history of diabetes were also similar, but cases had fewer smoking pack-years and lower cumulative average caffeine intake than controls. The CV was less than 20% for all metabolites except for C7 carnitine (mean CV = 47.98%) and C3-DC-CH3 carnitine (mean CV = 26.80%). Cases tended to have lower values of most metabolites as compared to controls. Based on the results of paired t-tests, the mean levels of C9-carnitine (mean difference=–0.18, 95% CI: – 0.32, – 0.03; p = 0.02), C12-carnitine (mean difference=– 0.14, 95% CI: – 0.28, – 0.01; p = 0.04), and C14-carnitine (mean difference=– 0.17, 95% CI: – 0.32, – 0.02; p = 0.02) were significantly different between cases and controls (Supplementary Table 1), but these did not remain significant after adjustment for multiple testing. The distribution of the summary scores for each metabolite group followed a similar pattern; cases had lower mean values of the summary scores for each metabolite group except for the long-chain acylcarnitine scores, but these differences were not significant. To explore whether metabolite values differed between cases and controls based on length of time between blood collection and case diagnosis, Figure 1 shows the mean difference within case-control pair of log-transformed and standardized metabolite values for the BCAAs and the acylcarnitine groups; although some of these differences are nominally different from 0, none remain significant after multiple testing correction. Glutamate and glutamine, which are not included in the BCAA or acylcarnitine groupings, were also not different between cases and controls at any time point.

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Fig. 1

Mean differences (case-control) and corresponding 95% confidence intervals for within matched case-control pair difference in log-transformed and standardized metabolite values. No differences were statistically significant after adjustment for multiple testing.

In single metabolite regression analyses, no metabolite was significantly associated with PD in either the minimally adjusted model (Supplementary Table 2), adjusting for pack-year category, cumulative average caffeine intake quartile, and fasting status, or the more fully adjusted model (Table 2), which additionally adjusted for diabetes history, BMI category, and plasma uric acid level. Summary score analysis results for each metabolite group were similar. As shown in Table 3, there was no evidence of an association between any of the metabolite groups and PD. This finding was consistent across both forms of the summary score assessed.

Including an interaction between metabolite level and time interval category indicates that, for some metabolites, there is a different association between the metabolite and PD among individuals at different time points prior to PD diagnosis. Results of these models are summarized according to metabolite group in Supplementary Tables 3–6; results for glutamate and glutamine are included in the Supplementary Table 3 with the BCAAs. In these analyses, the interaction between metabolite and time interval category contributed significantly to the median quartile-based model for the following acylcarnitines: C3-DC-CH3 (p_interaction = 0.007), C12 : 1 (p_interaction = 0.03), C14 : 2 (p_interaction = 0.03), C16 (p_interaction = 0.04), C18 : 1 (p_interaction = 0.01), and C18 : 2 (p_interaction = 0.003). In the continuous models, the interaction was significant for C8 (p_interaction = 0.04), C12 : 1 (p_interaction = 0.047), C14 : 1 (p_interaction = 0.03), C14 : 2 (p_interaction = 0.01), C18 : 1 (p_interaction = 0.01), and C18 : 2 (p_interaction = 0.01) acylcarnitines. Generally, the significance of these interactions reflects a stronger association between these metabolites and PD in a specific time category. For example, the association between C18 : 2 acylcarnitine and PD is stronger among those less than 60 months prior to diagnosis (continuous model: RR = 1.79 (1.20, 2.67)) as compared to among those further from diagnosis at the time of blood collection (60–179 months: RR = 0.85 (0.68, 1.06); 180 + months: RR = 1.04 (0.73, 1.47)). Following adjustment for multiple testing, only the association from the median quartile-based model between C18 : 2 acylcarnitine and PD among those in the shortest interval category, when cases are likely to be in the prodromal period, remained significant (FDR p_trend = 0.054), suggesting that increased level of C18 : 2 acylcarnitine is associated with increased rate of developing PD. Results from models restricted to individuals in a given interval category were comparable to those obtained by incorporating the interaction between interval category and metabolite level.

In models for the association between each metabolite group summary score and PD that included an interaction term between metabolite group summary score and interval category, the interaction was significant for models of both forms of the medium-chain acylcarnitines score in the continuous model (Summary Score 1 p_interaction = 0.04; Summary Score 2 p_interaction = 0.04) and long-chain acylcarnitine Summary Score 2 in the continuous model (p_interaction = 0.03). The results of these models are summarized in Supplementary Tables 3–6 by metabolite group, and, consistent with the single metabolite results, none of the associations between metabolite summary score and PD within interval category are significant after correction for multiple testing except for the association between the long-chain acylcarnitine Summary Score 2 and PD among individuals in the shortest interval category of the quartile-based model (FDR p_trend = 0.048), likely reflecting the association between C18 : 2 acylcarnitine and PD among individuals in this interval category.

Results did not change substantially when time interval was modelled continuously, when further adjustment was made for use of diabetes medications, total caloric intake, or physical activity, or when models were more minimally adjusted for only fasting status. Exclusion of matched pairs for whom the case’s PD diagnosis occurred shortly before blood collection yielded similar results. Similarly, exclusion of subjects with diabetes at baseline did not substantially alter any findings. In exploring two sets of restricted cubic splines models with knots placed at the 10^th 50^th and 90^th percentiles of the data (k = 3 internal knots) as well as models with knots placed at the 5^th, 35^th, 65^th, and 95^th percentiles of the data (k = 4 internal knots), the results were similar to those obtained in the quartile-based analyses described above.

DISCUSSION

In this nested case-control study, we found no evidence that BCAAs, glutamate, glutamine, or acylcarnitine metabolites, all markers of insulin resistance or diabetes,[12–16] act as risk factors of PD development either individually or in groups. Exploration of effect modification of the relationship between each metabolite and PD by time between blood sample collection and PD diagnosis revealed that some metabolite levels may be altered at different times prior to PD diagnosis, but that no metabolite was consistently altered across each time interval assessed. Following multiple testing adjustment, no metabolite was significantly associated with PD in any of our analyses except for the association between C18 : 2 acylcarnitine and PD and corresponding association between the long-chain acylcarnitine Summary Score 2 and PD among those less than 60 months from diagnosis.

Recently, there have been several investigations of metabolomics in relation to PD, including some studies [45, 46] suggesting that several of the metabolites investigated here might act as a marker of PD. Our finding that C12 and C14 acylcarnitines were nominally lower in PD cases relative to controls and nominally associated with a decreased rate of PD in adjusted regression models are similar to the results from a recent retrospective case-control investigation [46], indicating that those metabolites may be of interest despite not remaining statistically significant following multiple testing correction in our analyses. On the other hand, our finding that C18 : 2 acylcarnitine was associated with an increased rate of PD among those with a short time between blood collection and diagnosis is in direct opposition to two previous retrospective case-control studies [45, 46] that found C18 : 2 acylcarnitine was decreased among PD patients relative to controls. Although our findings conflict with these reports, several differences between our study and other studies may account for the inconsistencies. First, previous studies have included only samples from patients after disease onset and may thus be influenced by metabolic changes that are a consequence of the disease processes or of behavioral changes that might occur after diagnosis. Second, our study was nested within two large and well-characterized cohort studies and is thus less vulnerable to control selection bias, which is an important concern in studies have recruited cases from hospitals, ongoing clinical trials, and other case series that do not allow direct sampling of the source population. By conducting our investigation within these cohorts, we were also able to adjust for a variety of covariates that were prospectively measured and that are not always available in other studies. Further, our study utilized plasma samples whereas other studies have investigated metabolomics in a variety of plasma, serum, and cerebrospinal fluid samples; it is possible that the relationship between a given metabolite and PD may differ based on the sampled biofluid. In particular, the literature [47] suggests that insulin resistance in the central nervous system is most relevant for PD, it is possible that these metabolites or other markers of diabetes might have different relationships with PD when measured in CSF rather than plasma.

This investigation has several key strengths. First, there are several strengths due to the study design. As a prospective study of incident PD using blood samples collected prior to PD diagnosis, we were able to assess potential risk factors for the development of PD with little risk of reverse causation explaining our findings. Further, as the study is nested within two large cohorts, we minimized the risk of selection bias when selecting controls and ensured that there were no systematic differences between cases and their matched controls with respect to blood collection and processing. Another key strength of this study was the ability to combine metabolomics measurements with prospectively collected data on a variety of covariates, which allowed for careful control for potential confounders. Lastly, we conducted thorough sensitivity analyses to assess the robustness of our findings.

There are limitations of this study. It is possible that some of the less stable metabolites in our samples may have degraded during shipment or processing and that there may have been measurement error in the metabolite profiling procedures. Given the prospective nature of the study, the blinding of lab staff to each sample’s outcome status, the simultaneous analyses of matched pairs, and previous quality control analyses using samples from the same cohorts and metabolomics platform, the effects of such measurement error or sample degradation for most metabolites are likely to be modest and nondifferential, resulting in a possible bias towards the null [28]. An additional limitation of the study is that we have only a single measurement for each participant, which may have been collected too early in the disease course to detect an association and which may not be representative of long-term levels of these metabolites. However, the metabolomics validation study referenced above also indicated that, among participants who donated two blood samples 0.8–2.3 years apart, the correlation between measurements was greater than 0.4 for more than 90% of metabolites, suggesting a single measurement may reasonably reflect longer-term levels [28]. Another limitation of this study is that, despite the large sample size compared to previous PD metabolomics investigations, we did not have a sufficient sample size to explore additional subgroups, such as PD cases with diabetes versus PD cases without diabetes, and may be underpowered to detect effect modification, as evidenced by the wide confidence intervals in some of the stratified analyses. Due to the observational nature of the investigation and use of questionnaires, it is also possible that unmeasured or residual confounding or measurement error may be biasing our results. To mitigate these biases as much as possible, we took care to adjust for several known confounders and used well-validated instruments.

In conclusion, we did not find evidence that metabolomic markers of insulin resistance and type 2 diabetes also act as markers of pre-diagnostic PD. These findings are in concordance with previous research conducted in the NHS and HPFS that did not find an association between diabetes and incident development of PD [10]. Given that an association between diabetes and PD has not been identified in these cohorts and that there were few subjects with a history of diabetes at baseline in this specific investigation, further prospective investigation of the relationship between diabetes markers, including not only metabolomic but also other biomarkers, and PD is needed in other populations in order to establish the robustness of these findings.

CONFLICTS OF INTEREST

The authors have no conflicts of interest to report but several authors have relevant financial disclosures. Dr. Molsberry has no disclosures. Dr. Bjornevik has no disclosures. Dr. Hughes has no disclosures. Dr. Zhang has no disclosures. Dr. Healy has received research support from Merck Serono, Genzyme, Novartis, Verily Life Sciences and Analysis Group. Ms. Jeanfavre reports no disclosures. Dr. Clish reports no disclosures. Dr. Schwarzschild has within the past three years received funding from the Cure Parkinson’s Trust, CBD Solutions trust, Michael J. Fox Foundation for Parkinson’s Research (MJFF), Prevail Therapeutics and nQ Medical for serving on their scientific advisory boards; from Eli Lilly and Company for serving on a trial Data Monitoring Committee; from Biotie Therapeutics, Inc./Acorda Therapeutics, MJFF, Parkinson’s Foundation and Univ. of California-San Francisco for serving on study steering committees; and from Denali Therapeutics for presenting a seminar. He has within the past three years received research funding from the NIH, Parkinson’s (Disease) Foundation, U.S. Department of Defense, RJG Foundation, Target ALS Foundation, Michael J. Fox Foundation and the Farmer Family Foundation. Dr. Ascherio has no disclosures.