Precision Medicine for Nociception, Sngception and Proprioception.

Overview

Precision medicine is defined as "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person" by the Precision Medicine Initiative. Patients have different response to different treatment modalities, and sore/pain medicine is no exception. In our experience, low-level laser (LLL), ultrasound, and prolotherapy can reduce sore /pain through different genetic pathway. Whether the therapeutic effect is controlled by the genetic variants of those sore /pain related genes or not, is still in debate. The aims of this study are (1) To set up next generation sequencing (NGS)-based approach to find genetic variants which can determine the response of sng/pain treatment modalities and the phenotype of idiopathic scoliosis. (2) To find possible metabolomics and proteomic markers of sng/pain. (3) To determine the algorithm of precision medicine for sng/pain control via the genetic markers. Investigators will recruit 80 myofascial pain participant and 80 idiopathic scoliosis participant from Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Bei-Hu Branch in 2023 and 2025. The myofascial pain participant participants will receive LLL, ultrasound, and prolotherapy, and the therapeutic effect will be recorded. The clinical trial will evaluate the Sng / pain (VAS) and muscle tone of the idiopathic scoliosis participant. The blood and urine samples from the first, the second, and the third visits will be analyzed by next generation sequencing, and mass spectrometry to find the possible biomarker in 2024 and 2025. Investigators expect to develop the individualized treatment plan by means of these biomarkers. Hopefully, the results will be widely applied in the field of sore /pain medicine.

Precision medicine is defined as "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person" by the Precision Medicine Initiative. Pain is defined as "an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage" by the International Association for the Study of Pain (IASP).Traditionally, soreness, or sng, is also included as one of the pain sensation. Recently, the investigators defined sngception as acid and/or soreness sensation in the somatosensory system, and revealed that analgesia in muscle is through substance P and Tac1. According to the clinical outcome, some patients responded to physical agents well, and some preferred injections. The genetic variants of the above-mentioned genes might be the determining factors of differential therapeutic effects. However, it took about 4-8 weeks for a patient to switch from one treatment option to another one. If the investigators can determine the optimal treatment modality by genetic biomarkers, the treatment course and total expanse will decrease a lot. The investigators hypothesize that the genetic variants of the proposed genes (TRPV1, ASIC1a, ASIC3, Tac1, COMT, TCL1A, POMC, RGS4, ASIC2, ASIC4, TRPA1, NK1R, G2A, GPR4, OGR1, TDAG8, TASK1, TASK2, TASK3, TREK1, P2X2, P2X3, P2X5, TRPV4, KCNK1, NTSR1, NTSR2, CaV3.2, Nav1.1, Piezo1, and Piezo2, Runx3 or Egr3, endothelin converting enzyme-like 1 (ECEL1), myosin heavy chain genes MYH3 and MYH8, myosin-binding protein C gene, MYBPC1, and TNNI2, TNNT3 and TPM2 that encode the muscle regulatory proteins troponin I, troponin T and beta-tropomyosin) could be the prognostic biomarkers of sng/pain treatments or proprioceptive function. 1.Specific Aims 1. To set up next generation sequencing (NGS)-based approach to find genetic variants which can determine the response of sng/pain treatment modalities and the phenotype of idiopathic scoliosis. 2. To find possible metabolomics and proteomic markers of sng/pain. 3. To determine the algorithm of precision medicine for sng/pain control via the genetic markers. 2.In the past years, our team had some achievements to justify the search of genetic variants for development of a new sore/pain treatment algorithm. 1. Analgesia of LLLT is through TRPV1. 2. Analgesia by therapeutic ultrasound is through ASIC3. 3. Dextrose injection decreased chronic muscle pain through ASIC1a. 4. Biomarker for sng/pain in fibromyalgia. 3.Study design (1) Participants from cohort A-Myofascial pain syndrome: The investigators will recruit 80 participant from National Taiwan University Hospital and its Branch hospital. B. Clinical trail design: 1. After obtaining the informed consent, the basic demographic data, including age, gender, job, education level, and past medical history of eligible participant are collected. 2. The eligible participant first received LLLT with a 685-nm wavelength and an output of 30 mW at energy densities of 8 J/cm2 at trigger point of upper trapezius muscle. The pre- and post-treatment VAS-pain and VAS-sng are collected for LLLT phenotype determination, respectively. 3. Then, they are conveniently assigned into two groups (40 subjects in each group): A. therapeutic ultrasound group; B. prolotherapy group. Group A receives therapeutic ultrasound. Group B receives hypertonic prolotherapy at perimysium of upper trapezius muscle. No other medication or physical modality was given to avoid efficacy interference in both groups. 4. The recruited participant receive evaluation before and after injection, and 2-week after injection. The primary outcome is VAS-pain and VAS-sng. The secondary outcomes are pain threshold, muscle tone, and SF-36. Venous blood and urine samples were collected at first visit and 2-week visit, respectively. The buffy coat is separated after centrifugation, and stored as well. The urine samples were aliquoted and stored at -80 ºC in a locked freezer for future analysis. 5. Rescue therapy (cross-over treatment): If the participant does not satisfy with their first round treatment and the improvement of VAS is less than 10, then they are eligible to receive the rescue therapy-the treatment in the other group. And they will return to clinic for another 2 weeks. The outcome variables will be collected in the 3rd visit as well. (2) Participants from cohort B-idiopathic scoliosis: The investigators will recruit 80 participant from National Taiwan University Hospital and its Branch hospital, and Taichung Veteran Hospital. B. Clinical Trail design: 1. After obtaining the informed consent, the basic demographic data, including age, gender, job, education level, physical activity level, and past medical history of eligible participant are collected. 2. The recruited participant receive evaluation for once only. The collected parameters include Cobb's angle, spine rotational angle, VAS-pain and VAS-sng, strength and tone of paraspinal muscle, and SF-36. Venous blood and urine samples were also collected. Data analyses will be performed as previously described. Briefly, the raw sequencing data will be aligned to the reference human genome (Feb. 2009, GRCh37/hg19) using BWA-MEM. The investigators will use Picard to perform necessary data conversion, sorting and indexing. The main variant calling process, for both single nucleotide variants and indels, will be operated with the GATK software package. Structural variants will also be identified. The investigators will apply ANNOVAR to appropriately annotate the genetic variants; this include at least gene annotation, amino acid change annotation, SIFT scores, PolyPhen2 score, dbSNP identifiers, 1000 Genome Project allele frequencies, gnomAD allele frequencies and ClinVar database with variant clinical significance. The investigators will then use IGV to view the mapping and annotation of sequences on a graphic interface. The allele frequencies of variants of interest will be compared between different groups of participants. The investigators will look for possible rare variants with very strong effects (the single-gene model) as well as relatively common variants with moderate to strong effects (the complex phenotype model).