To estimate dopaminergic dysfunction in patients with Parkinson disease (PD) during the premotor stage and to investigate the effect of genetic factors on the trajectories. Using longitudinal dopamine transporter single-photon emission computed tomography data from 367 sporadic PD (sPD), 72 LRRK2 (G2019S), and 39 GBA (N370S) PD patients in the Parkinson’s Progression Markers Initiative (PPMI) study, we estimated the temporal trajectories of putaminal-specific binding ratios using an integrating function between baseline values and their annual change rates. In order to test reproducibility, we computed another trajectory for sPD using positron emission tomography data of 38 sPD patients at Gangnam Severance Hospital (GSH). Temporal trajectories of sPD were compared between the groups separated by age at onset (AAO) and polygenic load for common PD risk variants, and also compared with genetic PD. sPD patients in both the PPMI and GSH cohorts showed similar onset of dopaminergic degeneration around 10 years before motor onset. Early-onset PD patients exhibited later onset of degeneration and a faster decline in dopaminergic activity during the premotor period than late-onset patients. sPD patients with high polygenic load were associated with earlier onset and slower progression of dopaminergic dysfunction. Compared to the sPD and LRRK2 PD groups, GBA PD patients exhibited faster deterioration of dopaminergic function during the premotor stage. Dopaminergic dysfunction in PD appears to start about 10 years before motor onset. Genetic factors may be contributing to the heterogeneity of dopaminergic deterioration during the premotor stage.

Dopamine transporters (DAT) are transmembrane proteins that translocate dopamine from the extracellular space into presynaptic neurons. We aimed to investigate the predictive power of DAT mRNA for DAT protein expression, measured using positron emission tomography (PET). We performed 18 F-FP-CIT PET scans in 35 healthy individuals. Binding potentials (BP ND ) from the ventral striatum, caudate nucleus, putamen, and middle frontal, orbitofrontal, cingulate, parietal, and temporal cortices were measured. DAT gene expression data were obtained from the freely available Allen Human Brain Atlas derived from six healthy donors. The auto-correlation of PETderived BP ND s for DAT was intermediate (mean ρ 2 = .66) with ρ 2 ranging from .0811 to 1. However, the auto-correlation of mRNA expression was weak across the probes with a mean ρ 2 of .09-.23. Cross-correlations between PET-derived BP ND s and mRNA expression were weak with a mean ρ 2 ranging from 0 to .22 across the probes. In conclusion, we observed weak associations between DAT mRNA expression and DAT availability in human brains. Therefore, DAT mRNA mapping may have only limited predictive power for DAT availability in humans. However, the difference in distribution of DAT mRNA and DAT protein may influence this limitation.

The brain plays a major role in controlling the desire to eat. This meta-analysis aimed to assess the association between dopamine receptor (DR) availability and dopamine transporter (DAT) availability, measured using positron emission tomography, and obesity. We performed a systematic search of MEDLINE (from inception to November 2020) and EMBASE (from inception to November 2020) for articles published in English using the keywords "dopamine receptor," "dopamine transporter," "obesity," and "neuroimaging." Body mass index (BMI) and the corresponding binding potential (BP ND ) were extracted from figures in each study using Engauge Digitizer, version 12.1, and plotted for radiopharmaceuticals and regions of interest (ROIs). Five studies involving 119 subjects with DR and five studies including 421 subjects with DAT were eligible for inclusion in this study. In overweight or obese subjects with BMI of 25 kg/m 2 or higher, DR availability from 11 C-Racloprie was negatively associated with BMI. However, DR availability from 11 C-PHNO was positively associated with BMI.DAT ratio was calculated after dividing DAT availabilities of overweight/obese BMI with mean DAT availabilities of normal BMI. The association between DAT ratio and BMI was not significant regardless of radiopharmaceuticals. In conclusion, dopamine plays a main role in the reward system with regard to obesity. Overweight and obese subjects had negative association between DR availability from 11 C-Raclopride and BMI. However, the association of DR availability with BMI was dependent on radiopharmaceuticals. DAT availability did not show the significant relationship with BMI regardless of radiopharmaceuticals.

We investigated the association between SLC6A3 gene polymorphisms and changes in dopamine transporter (DAT) availability after glucose loading in humans. An intravenous injection of 18F‐FP‐CIT was administered after infusion of glucose or placebo, and the emission data were acquired over 90 min in 38 healthy male participants. DAT availability expressed in terms of binding potential (BPND) was recorded. The 40‐bp variable number of tandem repeats (VNTR) in the 3′ untranslated region and two single nucleotide polymorphisms (SNPs), rs2652511 and rs2937639, in the SLC6A3 gene were genotyped. Among the 38 participants, those with a VNTR other than 10R/10R (n = 7) were excluded. The alleles of the two SNPs (rs2652511 and rs2937639) appeared to be inherited together in two fixed combinations (C–G or T–A) in 29 of 31 individuals. The BPND in the ventral striatum (VST), caudate nucleus, and putamen was not significantly different after glucose or placebo loading according to genotype. However, BPNDs from the caudate nucleus and putamen of all participants with rs2652511 CT/rs2937639 AG (n = 6) were higher after glucose loading. In conclusion, the SLC6A3 gene polymorphism is associated with the changes in DAT availability after glucose loading. DAT availability after glucose or placebo loading in the VST, caudate nucleus, and putamen did not differ according to the SLC6A3 genotype.

In Table 1 of this article, the value in the column headed "GSH cohort: Controls (n = 71)" and in the row "CN, more affected" was incorrectly stated as "6.84 ± 1.24", where the correct value is "5.84 ± 1.24". In the column headed "GSH cohort: Controls (n = 71)" and in the row "PUT, more affected", the value was incorrectly stated as "9.07 ± 1.27", where the correct value is "8.07 ± 1.27". The original article has been corrected.