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Author contributions | P.H. | P.H., Y.H. and C.Z., conceptualized the research question, designed the study. Y.D., Y.C., P.J. and J.C. performed the statistical data analysis, and wrote the article. Y.C., J.Z., and Y.Z. interpreted the results. S.E., Y.Z., and Z.L. revised the article for important intellectual content. All authors reviewed the manuscript. | PMC10439129 | |
Funding | No competing financial interests exist. The authors had financial support for the submitted work as specified in the funding section, but the funding institutions did not have any role in the writing of the article or the decision to submit it for publication; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work. This article does not include details, images, or videosrelating to an individual person, requiring specific consent. This work was supported by the National Natural Science Foundation of China (NO. 82030048, 8210219, 82001818, and 82102052), Key Research and Development Program of Zhejiang Province (NO. 2019C03077), Natural Science Foundation of Zhejiang Province (NO. LQ20H180009, Y16H180019, LQ20H180011 and LQ19H180004), Zhejiang Science and Technology Project (LQ21H180007). | PMC10439129 | ||
Data availability | The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. | PMC10439129 | ||
Competing interests | The authors declare no competing interests. | PMC10439129 | ||
References | PMC10439129 | |||
Key Contribution | temporomandibular disorders, headaches, TMDs, masticatory muscle pains, masticatory muscle pain, orofacial pain, headache, myogenous TMD, tenderness, capitis, OVAS | TEMPOROMANDIBULAR DISORDERS, DISORDERS | These authors contributed equally to this work.This study aimed to evaluate the efficacy of botulinum toxin type A (BoNT/A) in patients with temporomandibular disorders (TMDs) associated with masticatory muscle pain (MMP) and headaches. This randomized, double-blind, placebo-controlled pilot study is the first clinical trial to evaluate both disorders simultaneously. Twenty-one patients with myogenous TMD were randomly assigned to two groups. The experimental and control groups received injections of either BoNT/A or saline into the sites showing tenderness after palpation of a total of 16 muscle areas, including each masseter, a temporalis, splenius capitis, sternocleidomastoid, and trapezius muscle. During each visit, the clinical effects, based on the intensity of orofacial pain (OVAS), headache (HVAS), number of tender points (TPs), maximum mouth opening (MMO), and headache frequency (HF), were evaluated at four time points, namely, pre-injection and 4, 8, and 12 weeks after the injection, in both groups. Friedman and Mann–Whitney tests were used for the analyses. In the experimental group, the reductions in OVAS, TP, HVAS, and HF showed significant differences over time, excluding MMO, whereas there was no significant difference in any of the variables in the control group. In addition, the decline in TPs was significantly different between the experimental and control groups at all time points, especially after 4 and 12 weeks, compared to that during pre-injection. In conclusion, treatment with BoNT/A was relatively effective for masticatory muscle pain caused by TMDs and headache compared to the saline placebo.Significant values were observed over time following the administration of Botulinum toxin injections, and the number of tender points decreased significantly. Based on the findings of our study, Botulinum toxin injection can be suggested as a partially effective treatment for patients with masticatory muscle pains caused by temporomandibular disorders and headache. | PMC10610636 |
1. Introduction | myofascial pain, masticatory myalgia, TMD, traumatic injuries, TMDs, dystonia, masticatory muscle disorders, strabismus, pain, musculoskeletal disorders, parafunction, muscle hyperfunction, hyperhidrosis, blepharospasm | MYOFASCIAL PAIN, TMJ, DYSTONIA, STRABISMUS, MUSCULOSKELETAL DISORDERS, HEMIFACIAL SPASM, TEMPOROMANDIBULAR DISORDERS, BLEPHAROSPASM | Temporomandibular disorders (TMDs) refer to a range of conditions affecting the masticatory muscles or the temporomandibular joint (TMJ) and are the second most common musculoskeletal disorders causing pain and functional impairment [TMDs are considered to have a multifactorial etiology, commonly associated with muscle hyperfunction, oral parafunction, traumatic injuries, hormonal influences, and articular changes within the joint [It has been reported that approximately 50% of all TMDs comprise masticatory myalgia or painful masticatory muscle disorders [Since the US Food and Drug Administration first approved Botulinum toxin type A (BoNT/A) for the treatment of strabismus and blepharospasm in 1989, it has been approved for the treatment of hemifacial spasm, cervical dystonia, and hyperhidrosis and for cosmetic treatment [However, several double-blind randomized clinical trials (RCTs) failed to indicate significant differences with respect to myofascial pain in TMD between patients treated with BoNT/A and a saline placebo [ | PMC10610636 |
2. Results | PMC10610636 | |||
2.1. Participants | A total of 21 patients (19 women and 2 men; age range of 21–53 years) clinically diagnosed with MMP were included in the study. They were randomly divided into two groups and received BoNT/A or saline injections. There were no significant differences in sex and age between the groups; thus, the homogeneity of the general characteristics between the groups was confirmed. There were no dropouts from either group during the 12-week study period. In addition, there were no reports of side effects during this study. The participants’ demographic characteristics are presented in | PMC10610636 | ||
2.2. Differences between Groups | PMC10610636 | |||
2.2.1. Comparison of Changes over Time within Groups | According to the Friedman test, which was used to verify whether the change over time was significant in the two groups, no significant difference in OVAS, TPs, MMO, HVAS, or HF was observed in the control group, whereas in the experimental group, except for MMO, OVAS ( | PMC10610636 | ||
2.2.2. Comparison of Changes between Groups | A Mann–Whitney U test was conducted to verify whether there was a significant difference between the groups in the amount of change after 4, 8, and 12 weeks of pre-injection. Consequently, the changes in OVAS, MMO, HVAS, and HF did not show significant differences between the experimental and control groups at all time points. In contrast, TPs showed a significant difference between the groups in terms of the amount of change after 4 weeks ( | PMC10610636 | ||
3. Discussion | TMD, Headache, migraine headaches, headaches, pain, orofacial pain, head and neck pain, headache, migraine | TENSION-TYPE HEADACHES, MIGRAINE HEADACHES, MIGRAINE | Instead of conservative treatments, such as physical therapy, the use of non-steroidal analgesic agents, and the local injection of anesthetics and steroids, BoNT/A is being increasingly used as an adjuvant treatment for head and neck pain, such as tension-type headaches and migraine headaches [In this study, OVAS and TPs were compared to evaluate the change in the intensity of orofacial pain. Our findings showed a significant reduction in pain intensity over time in the experimental group compared to the control group, as demonstrated by a decrease in the OVAS over time and significant intergroup variation as well as time effects for TPs. Similarly, a double-blind RCT conducted by Lindern et al. [Furthermore, in our study, no time effects for changes in MMO were observed in either group (exp; In addition, we investigated the effect of BoNT treatment on headache by assessing HVAS and HF. Both HVAS (As mentioned above, several studies have only mentioned the pain relief effect of BoNT/A on the masticatory muscles of patients with TMD; however, there has been a lack of prospective, randomized, double-blind approaches to assessing the effect of BoNT/A on both MMP and headache among patients with TMD. Therefore, our study aimed to simultaneously evaluate the effect of BoNT/A in treating both MMP and headache, and the following factors were analyzed: OVAS, TPs, MMO, HVAS, and HF.This study has several limitations. The major limitation of this study was its small sample size. Second, this study was carried out on patients with general headaches without classifying the headaches into detailed diagnoses, such as migraine or tension-type headaches. Despite these limitations, this is the first prospective study to evaluate the therapeutic effects of BoNT/A on both MMP and headache among patients with TMD. Moreover, we adopted a double-blind RCT design to collect evidence-based data on the use of BoNT/A among TMD patients with MMP and headache, enhancing data reliability. Considering the outcomes derived from this pilot study, further studies with larger sample sizes are warranted. Furthermore, it is necessary to conduct additional RCTs and prospective studies to distinctly classify headache diagnoses according to the criteria of the Headache Classification Committee of the International Headache Society. This approach will ultimately allow for a more optimized evaluation of BoNT’s clinical effects on headaches. | PMC10610636 |
4. Conclusions | TMD, headache | According to our study results, after BoNT/A injection, significant values were observed in the experimental group over time, and TPs decreased. Therefore, BoNT/A injection can be suggested as a relatively effective treatment for patients with TMD presenting with MMP and headache compared to saline placebo. | PMC10610636 | |
5. Materials and Methods | PMC10610636 | |||
5.1. Subjects | joint-related disease, TMD, Orofacial Pain, TMDs, pain, headache, capitis, arthralgia | AMYOTROPHIC LATERAL SCLEROSIS, FIBROMYALGIA, MOTOR NEUROPATHY, MYASTHENIA GRAVIS, LAMBERT SYNDROME, DISEASES | The clinical trial included 21 patients (2 men and 19 women; age range of 21–53 years) with chief complaints of MMP with TMDs and headache who were enrolled at the Department of Orofacial Pain and Medicine, Dental University of Yonsei, Seoul, Republic of Korea. At their first visit, patients were screened to determine whether they qualified to participate in the trial, and clinical values were measured prior to injection. To exclude the patients with arthralgia, diagnostic criteria (DC/TMD) were applied, and the following clinical criteria for TMD were satisfied: (1) history of pain in the masticatory muscle reported for ≥5 days in the previous 30 days; (2) pain induced by the clinician’s palpation of the masticatory muscles (total of 16 sites; 2 points in the masseter muscle and 3 points in the temporal muscle, and 1 point in the splenius capitis, sternocleidomastoid muscle, and trapezius muscle, each assessed bilaterally). The exclusion criteria were as follows: (1) patients whose pain was caused by a disc or joint-related disease; (2) patients with a history of muscle-related diseases that affect neuromuscular function, such as myasthenia gravis, Eaton–Lambert syndrome, amyotrophic lateral sclerosis, and motor neuropathy; (3) patients who had received an injection of BoNT within a year from the study or had been administered a myofascial trigger point injection, such as lidocaine, procaine, or bupivacaine, within a month from the study based on screening; (4) patients diagnosed with or treated for fibromyalgia; and (5) women who were pregnant, lactating, or of childbearing potential. All participants understood the content of the study and voluntarily signed a consent form. This study was approved by the Institutional Review Board of the Yonsei University Dental Hospital (IRB No. 2-2019-0061) on 14 November 2019. | PMC10610636 |
5.2. Study Design | orofacial pain, TMD, headache pain, headaches | This was a prospective, randomized, double-blind, placebo-controlled clinical trial. Clinicians checked the demographic information, medical history, and medication history of participants. Participants completed questionnaires regarding TMD and headache pain for measurement and assessment of clinical values. The intensity of orofacial pain was assessed using a VAS, and participants indicated the frequency and intensity of various types of headaches experienced during the past 30 days. Additionally, the clinicians measured the number of tender points and the range of maximum mouth opening in millimeters (mm). Accordingly, diagnoses were clinically determined. Participants who met the selection criteria and did not meet the exclusion criteria were randomly assigned to either the test group ( | PMC10610636 | |
Author Contributions | Conceptualization, A.H.K. and S.T.K.; methodology, A.H.K. and S.T.K.; formal analysis, S.R.K.; investigation, M.C. and S.T.K.; data curation, S.R.K. and M.C.; writing—original draft preparation, S.R.K.; writing—review and editing, S.R.K. and S.T.K.; supervision, S.T.K. All authors have read and agreed to the published version of the manuscript. | PMC10610636 | ||
Institutional Review Board Statement | The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Yonsei University Dental Hospital (Approval No. 2-2019-0061, date of approval; 14 November 2019). | PMC10610636 | ||
Informed Consent Statement | Informed consent was obtained from all subjects involved in the study. | PMC10610636 | ||
Data Availability Statement | The data presented in this study are available in this article. | PMC10610636 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC10610636 | ||
References | orofacial pain, headache | (Homogeneity of general characteristics between the experimental and control groups.Exp, experimental group; Con, control group.Differences in variables in the experimental and control groups according to time.Exp, experimental group; Con, control group; OVAS, the intensity of orofacial pain; TPs, the number of tender points; MMO, maximum mouth opening (mm); HVAS, the intensity of headache; HF, headache frequency per month. An asterisk (*) indicates statistical significance (Differences in changes in variables according to group.Exp, experimental group; Con, control group; OVAS, the intensity of orofacial pain; TPs, the number of tender points; MMO, maximum mouth opening (mm); HVAS, the intensity of headache; HF, headache frequency per month; An asterisk (*) indicates statistical significance ( | PMC10610636 | |
Subject terms | disability, pain | ADVERSE EVENTS, RECRUITMENT, CORTEX, CHRONIC LOW BACK PAIN | Chronic low back pain (CLBP) is a disabling condition worldwide. In CLBP, neuroimaging studies demonstrate abnormal activities in cortical areas responsible for pain modulation, emotional, and sensory components of pain experience [i.e., pregenual and dorsal anterior cingulate cortex (pgACC, dACC), and somatosensory cortex (SSC), respectively]. This pilot study, conducted in a university setting, evaluated the feasibility, safety, and acceptability of a novel electroencephalography-based infraslow-neurofeedback (EEG ISF-NF) technique for retraining activities in pgACC, dACC and SSC and explored its effects on pain and disability. Participants with CLBP (n = 60), recruited between July’20 to March’21, received 12 sessions of either: ISF-NF targeting pgACC, dACC + SSC, a ratio of pgACC*2/dACC + SSC, or Placebo-NF. Descriptive statistics demonstrated that ISF-NF training is feasible [recruitment rate (7 participants/month), dropouts (25%; 20–27%), and adherence (80%; 73–88%)], safe (no adverse events reported), and was moderate to highly acceptable [Mean ± SD: 7.8 ± 2.0 (pgACC), 7.5 ± 2.7 (dACC + SCC), 8.2 ± 1.9 (Ratio), and 7.7 ± 1.5 (Placebo)]. ISF-NF targeting pgACC demonstrated the most favourable clinical outcomes, with a higher proportion of participants exhibiting a clinically meaningful reduction in pain severity [53%; MD (95% CI): − 1.9 (− 2.7, − 1.0)], interference [80%; MD (95% CI): − 2.3 (− 3.5, − 1.2)], and disability [73%; MD (95% CI): − 4.5 (− 6.1, − 2.9)] at 1-month follow-up. ISF-NF training is a feasible, safe, and an acceptable treatment approach for CLBP. | PMC9860016 |
Introduction | pain | CHRONIC LOW BACK PAIN | Chronic low back pain (CLBP) is a significant and disabling health condition affecting individuals, the wider community, and the healthcare systemResting-state cortical activity alterations have been demonstrated in individuals with CLBPElectroencephalography (EEG) based Neurofeedback (NF) is a brain-computer interface biofeedback technique that facilitates an individual’s ability to self-control their real-time cortical activity of the targeted brain regions and reinforces learning through operant conditioningTherefore, this investigation explored the feasibility and safety of a novel source localised EEG-based ISF-NF training technique, targeting the pgACC and dACC + SSC regions for treating CLBP. The specific objectives of this study were to.to assess the feasibility, safety, and acceptability of the targeted EEG ISF-NF training in people with CLBP,to explore the immediate, intermediate, and short-term trends of the effects of the targeted ISF-NF training on pain and function.to explore the EEG changes [current density (CD) and functional connectivity (FC)] in the ISF band at the targeted brain regions following training.We hypothesize that pgACC up-training will improve pain modulation through activation of descending pain inhibitory controls; while down-training medial (dACC) and lateral (SSC) pathways will disrupt pain-provoking activity; but that most benefit may be obtained by normalizing the pain provoking/pain inhibitory balance (up-training pgACC, and down-training dACC and SSC pathway simultaneously by ratio training).A study is therefore set up to test these hypotheses and compare these three groups versus placebo-NF. The study results will support brain-computer interface training as a treatment tool for improving clinical outcomes in people with CLBP. This study will provide central tendency and variability data of clinical outcomes for estimating sample size for a full RCT. | PMC9860016 |
Methods | PMC9860016 | |||
Trial registration and ethical approval | Prospectively registered in Australian and New Zealand Clinical Trials Registry ( | PMC9860016 | ||
Study design | SECONDARY, CORTEX | Double-blind, randomized, placebo-controlled feasibility study with four parallel intervention arms (Fig. Study design and timelines. ISF-NF: infraslow frequency neurofeedback, pgACC: pregenual anterior cingulate cortex, dACC: dorsal anterior cingulate cortex, SSC: primary somatosensory cortex. Study assessment sessions were conducted at the Department of Surgical Sciences laboratory, Dunedin School of Medicine, Dunedin hospital, and the treatment sessions were conducted at the School of Physiotherapy laboratory, University of Otago, Dunedin, New Zealand. Feasibility measures were assessed throughout the study period. Treatment acceptability and satisfaction was assessed immediately post-intervention. Primary and secondary outcome measures and mechanistic outcome measure (EEG) were collected at baseline, immediately post-intervention, and at 1-week and 1-month post-intervention. | PMC9860016 | |
Randomization | pain | A research administrator, not involved in treatment/assessment, randomized, and assigned participants using a computerized open-access randomization software program without applying any restrictions (on a 1:1:1:1 basis) to either:Group-1: ISF-NF up-training pgACC, i.e., modulate the descending pain inhibitory pathwayGroup-2: ISF-NF down-training dACC + SSC, i.e., modulate the medial and lateral pathwayGroup-3: ISF-NF concurrently up-training pgACC and down-training dACC + SSC, i.e., Ratio [(2xpgACC)/(dACC + SSC)], i.e., normalize the balance between the descending inhibitory and ascending pain provoking pathwaysGroup-4: Placebo-NFThe randomisation schedule was concealed in sequentially numbered, sealed opaque envelopes and provided to participants at baseline. | PMC9860016 | |
Blinding | Participants and outcome assessor were blinded. The success of blinding was assessed after completion of intervention using the question, “What type of treatment do you believe that you/the participant received respectively?” The confidence in their judgement was assessed on an 11-point NRS | PMC9860016 | ||
Participants and eligibility criteria | All participants were voluntarily recruited from the community through advertisement flyers. All participants provided written informed consent prior to study enrolment. Participants were screened for eligibility and enrolled by a musculoskeletal physiotherapy researcher. | PMC9860016 | ||
Inclusion and exclusion criteria | Morris Disability, disability, pain | CENTRAL SENSITIZATION | Ages of 18 to 75 years, pain in the lower back region for ≥ 3 months, a score of ≥ 4 on an 11-point NPRS in 4 weeks prior to enrolment, a disability score of ≥ 5 on Roland–Morris Disability Questionnaire (RMDQ)At baseline assessment, all participants completed questionnaires to capture demographics, and clinical characteristics of CLBP, including the presence of central sensitivity (Central Sensitization Inventory) | PMC9860016 |
Sample size | As this was a pilot study to determine the feasibility of a future fully powered RCT, sample size calculation was not performed. Based on statistical advice, a sample of 60 participants (15/group) was considered enough to determine feasibility issues and obtain treatment estimates for designing the full trial. | PMC9860016 | ||
Intervention | Source localised EEG ISF-NF was administered three times a week (30 min/session) for four consecutive weeks (12 sessions) by the researcher (physiotherapy background) experienced in delivering neuromodulation techniques. Treatment was delivered using a 21-channel DC coupled amplifier and BrainAvatar™ sLORETA software version 4.7.5 for Discovery manufactured by BrainMaster Technologies Inc., Bedford, OH, USADuring each session, the Comby EEG lead cap with 19 (Ag/AgCl) electrodes positioned according to the International 10–20 system was fixed to individual’s scalp (Fig. EEG ISF-NF intervention set-up. | PMC9860016 | ||
ISF-NF treatment groups | chronic pain, pain | CHRONIC PAIN | It has been demonstrated that chronic pain can be considered as an imbalance between pain input and pain suppression. Our protocols are derived from this theory utilizing source localization neurofeedback targeting the ratio between pgACC, SSC, and dACC. For the current study, we developed EEG-NF training programs to up-train (i.e., increase CD) ISF activity at pgACC (Group 1), and down-train (i.e., decrease CD) ISF activity simultaneously at dACC and SSC (Group 2). For Group 3, a program was developed to concurrently up-train ISF activity at pgACC (× 2) and down-train ISF activity at dACC + SSC, to reinforce ratio between these regions to be > 1, as below:For all groups, the reward threshold was adjusted in real-time between 60 and 80%, i.e., for 60–80% of time, sound feedback was delivered by system when participant's brain activity meets desired infraslow magnitude. | PMC9860016 |
Placebo-NF group | To create identical auditory feedback to ISF-NF groups, participants in placebo-NF group listened to a random set of pre-recorded sound files (n = 12), sourced from a database of recorded audio files (using audacity software) of healthy participants that underwent EEG source localised ISF-NF training (targeting ratio between pgACC and dACC + SSC). All other conditions were kept same as ISF-NF groups. | PMC9860016 | ||
Outcome measures | PMC9860016 | |||
Feasibility measures | RECRUITMENT | Included recruitment rate (number of participants recruited per month), proportion of participants recruited from total number screened (expressed as a percentage), adherence to intervention (measured as number of treatment sessions attended by each participant expressed as a percentage of the total number of sessions), and dropout rates (measured as the number of participants who dropped out in each group, expressed as a percentage of the total number of participants enrolled in the study). | PMC9860016 | |
Clinical measures | Pain | Brief Pain Inventory (BPI) | PMC9860016 | |
Electroencephalogram | Resting-state eyes-closed EEG (~ 10 min) was obtained using SynAmps-RT Amplifier (Compumedics-Neuroscan). Sixty-four electrodes were placed in 10–10 International placement and impedances were checked to remain below 5kΩ. Data were resampled (128 Hz), band-pass filtered (0.005–0.2 Hz), plotted in EEGLAB and ICoN software for careful inspection and manual artefact rejection. SLORETA source localisation software was used to estimate intracerebral electrical sources that generates scalp-recorded activity. We calculated average fourier cross-spectral matrices for three ISF bands: ISF1 (low:0.01–0.04 Hz), ISF2 (mid:0.05–0.07) and ISF3 (high:0.08–0.10). Log-transformed CDs and lagged phase coherence (FC) were calculated for and between targeted ROIs (pgACC, dACC, and left and right SSC) respectively. | PMC9860016 | ||
Data analysis | primary pain, pain | Data were analysed using SPSS_v27.0. As this was a feasibility study, tests for significance to compare clinical and EEG measures between study groups were not performed, but descriptive statistics were applied. Feasibility outcomes are reported based on recommendations.Clinical outcomes were analyzed based on intention-to-treat principle and as per the originally assigned groups. Last observation carried forward methodology was used to impute missing data. Mean ± SDs and Mean differences (95% CI), were calculated from baseline to each interim and primary endpoint (T3) for all clinical and experimental pain measures, and descriptively compared between groups.Percentage change to baseline was calculated for primary pain (BPI) and functional (RMDQ) measures as below (e.g., for T3):A ≥ 30% decrease was considered as meaningful clinical important difference (MCID). Proportion of participants with changes ≥ MCID were calculated and descriptively compared between groups.Similarly, EEG measures (CD and FC) were also analysed descriptively and compared between groups. | PMC9860016 | |
Protocol changes | Following changes were made to the registered protocol based on the ethical review and the peer reviewer comments. | PMC9860016 | ||
Results | PMC9860016 | |||
Participants | CORTEX | Sixty participants were enrolled and randomised equally into four treatment groups (Fig. Flow of participants through the study phases. pgACC: pregenual anterior cingulate cortex, dACC: dorsal anterior cingulate cortex, SSC: primary somatosensory cortex, ITT: intention to treat.Demographics and clinical characteristics of participants. | PMC9860016 | |
Feasibility | PMC9860016 | |||
Recruitment | MAY, RECRUITMENT | The total recruitment period was 9 months (July 2020 to March 2021), with the last follow up completed in May 2021. This feasibility trial was stopped in May 2021 as the desired sample size was reached (n = 60) and all follow ups completed. Our average recruitment rate was seven participants per month. The proportion of participants recruited (n = 60) from the total number of participants screened (n = 252) was 24%, which was greater than our a priori criteria of 20% (Fig. | PMC9860016 | |
Dropouts | Of the total participants enrolled (n = 60), we lost 8 participants following baseline assessment session (Fig. | PMC9860016 | ||
Treatment adherence and engagement | NRS | The average treatment adherence rate for all groups was 80%. The individual treatment adherence scores were 88%, 73%, 76%, and 81% for pgACC, dACC + SSC, Ratio, and Placebo groups respectively.The NRS scores for mood, motivation, and treatment engagement were comparable between treatment groups. The Median (95% CI) for the pgACC, dACC + SSC, Ratio, and Placebo group for mood were 7.2 (6.0,7.8), 7.7 (5.3,8.3), 7.0 (6.0, 7.9), and 7.0 (6.9, 9.0) respectively; for motivation were 6.7 (5.6,7.5), 7.1 (5.2,7.8), 7.0 (5.3,8.3), and 7.0 (6.5,9.0) respectively; and for treatment engagement were 7.8 (5.7,8.3), 7.3 (5.0,8.3), 7.7 (6.8,8.8), and 7.7 (7.0,7.9) respectively. Overall, irrespective of the treatment group, participants reported moderate to high levels of mood, motivation, and engagement during NF training sessions. | PMC9860016 | |
Integrity of blinding | Participant blinding was deemed successful as the treatment group was not revealed to them in any way. In total, 51% of participants incorrectly predicted the treatment group or responded, “don’t know”. The remaining 49% of participants, although correctly predicted their treatment groups, based their decision primarily on guesswork or symptom assessment, and their confidence for correctly predicting the group was not greater than merely chance [Mean ± SD (48% ± 19%)]. Outcome assessor blinding was highly successful, with correct prediction being only 18%, and 58% of responses being “don’t know”. | PMC9860016 | ||
Adverse effects | ADVERSE EFFECTS, ADVERSE EFFECTS, CORTEX | No serious adverse effects were reported. Several transient low intensity (< 3 on NRS) negative side effects, rated to be related to ISF-NF treatment, were reported by a few participants (Fig. Adverse effects reported by participants during the neurofeedback treatment sessions. pgACC: pregenual anterior cingulate cortex, dACC: dorsal anterior cingulate cortex, SSC: primary somatosensory cortex. | PMC9860016 | |
Acceptability and satisfaction | SD | All participants, irrespective of treatment group, reported moderate to high levels of acceptability with Mean ± SD of 7.8 ± 2.0 (pgACC), 7.5 ± 2.7 (dACC + SCC), 8.2 ± 1.9 (Ratio), and 7.7 ± 1.5 (Placebo), respectively. Further, moderate to high levels of satisfaction were also reported with Mean ± SD of 5.7 ± 2.9 (pgACC), 7.3 ± 2.5 (dACC + SCC), 7.5 ± 2.4 (Ratio), and 7.0 ± 1.5 (Placebo), respectively. Summary of qualitative feedback to open-ended questions is presented in Supplementary Table | PMC9860016 | |
Pain | pain | All treatment groups demonstrated a favourable change in pain measures at all timepoints (Table | PMC9860016 | |
Function | disability | The RMDQ scores demonstrated a trend of reduction in disability in all treatment groups (Table | PMC9860016 | |
Global perceived effect | At 1-month follow-up, the proportion of participants who perceived meaningful (≥ + 2) global effect was higher in pgACC (67%) and dACC + SSC (64%) group, when compared to ratio (46%) and sham (46%) group. | PMC9860016 | ||
QST | Due to high variability in measures, no differences in trends were observed in any of QST variables at 1-month follow-up (Table | PMC9860016 | ||
EEG measures | CORTEX | Descriptive data for CD and FC measures at all time points are presented in Supplementary Tables Heatmaps for mean percentage change to baseline in functional connectivity in the infraslow frequency bands. pgACC: pregenual anterior cingulate cortex, dACC: dorsal anterior cingulate cortex, SSC: primary somatosensory cortex, ISF1: Infraslow frequency- low band (0.01–0.04 Hz), ISF2: Infraslow frequency- mid band (0.05–0.07 Hz), ISF3: Infraslow frequency- high band (0.08–0.10 Hz), S1L: Primary Somatosensory cortex left, S1R: Primary Somatosensory cortex right, T0: baseline, T1: immediately post-treatment, T2: 1 week follow up, T3: 1 month follow up, <->: functional Connectivity between regions. Higher values represent increase in the functional connectivity compared to baseline. | PMC9860016 | |
Discussion | chronic pain, disability, pain | CHRONIC PAIN, SECONDARY, RECRUITMENT | Re-training cortical activity through real-time EEG-based source localised ISF-NF is a novel approach for treatment of chronic painOur results demonstrate that a fully powered trial to test efficacy of EEG-based ISF-NF training for treatment of CLBP is feasible. A sizable number of individuals with CLBP were interested and willing to participate in EEG-NF intervention. Our recruitment rate was comparable to other CLBP intervention studiesStudy findings also confirms safety of EEG-based ISF-NF training for treatment of CLBP. Our study used an extensive DESS scaleAlthough treatment was acceptable and participants were moderate-to-highly satisfied, some important recommendations were made. A few participants indicated that they were unsure of mental strategies to use and would have benefited from some examples. While some studiesExploratory findings on clinical measures demonstrated a decreasing trend in pain and disability in all treatment groups. Our results are comparable to the previous NF studies in chronic pain conditions, who also demonstrated significant reductions in pain and disability following trainingOur pilot study results showed that ISF-NF treatment targeting pgACC had the highest proportion of participants who exhibited sustained clinically meaningful decrease in both pain and disability and had improved global perceived effect at 1-month follow-up. EEG FC measures also demonstrated highest magnitude of changes in the pgACC group, demonstrating the specificity of EEG-NF training. Further, in a recent secondary analysis of our data, we also demonstrate that the ISF-NF uptraining the pgACC increases the effective connectivity from the pgACC to SSC and this is correlated with greater reductions in pain severityThe greatest clinical effects demonstrated in the pgACC uptraining group could also be attributed to the simplicity of the protocol and ease in learning (i.e., uptraining CD of a single brain region). Previous NF studies in chronic pain conditions have demonstrated that complex training protocols (e.g., adding beta down training to the protocol of uptraining of sensorimotor rhythm and down training of theta) reduced training effectiveness, and increasing the number of training sessions increased pain reduction for such protocolsWe also observed a time course effect in several clinical measures, where the pain and function continued to improve over time. These findings have also been observed previously | PMC9860016 |
Limitations | LBP, disability, pain | The primary limitations of this pilot feasibility study were small sample size and descriptive comparisons to infer trends in clinical and EEG outcomes. However, these limitations reflect the purpose of our feasibility study, which was to provide estimates of clinical outcome measures (pain and disability) to support sample size calculation for use in fully powered trial. Based on results of this study, a future fully powered trial will be conducted to evaluate efficacy of ISF-NF training. The future RCT could tests the effect modifiers of LBP subgroups on clinical outcomes. Another limitation is that the XYZ coordinates for the 3 selected areas were based on a neurosynth meta-analysis of pain. While this is should be optimal for the group, this may be suboptimal for the individual. For example, the selection of the pgACC XYZ coordinates was based on the neurosynth meta-analysis for pain, yet there may may be more optimal ROIs to target for the individual patient. Similarly, the connectivity between the SSC and salience network is topographic, i.e. based on which part of the body is in pain | PMC9860016 | |
Conclusions | disability, pain | The ISF-NF training is feasible, safe, and an acceptable treatment approach for CLBP. A positive trend of the effect of ISF-NF treatment was observed on pain and disability outcomes in all groups. In particular, the pgACC uptraining group experienced favourable outcomes and perceived the intervention to be highly effective. However, a fully powered RCT is needed to evaluate the clinical efficacy of ISF-NF training in people with CLBP. | PMC9860016 | |
Supplementary Information | The online version contains supplementary material available at 10.1038/s41598-023-28344-2. | PMC9860016 | ||
Author contributions | Conceptualization: D.B.A., D.D.R., R.M., and S.V.; Methodology: D.B.A., D.D.R., R.M., and S.V.; N.F. protocol programming: M.S.; Data curation: JM, FO, D.B.A.; Writing—original draft preparation: D.B.A., D.D.R., and R.M.; writing—review and editing: D.B.A., D.D.R., R.M., S.V., M.S., J.M., and F.O. All authors have critically read and agreed to the final version of the submitted manuscript. | PMC9860016 | ||
Funding | BRAIN | This work was funded by the Otago Medical School Trust (Dean’s Bequest) Funding and Brain Health Research Centre (through a Philanthropist). The funding sources were not involved in the study design; the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. | PMC9860016 | |
Data availability | Datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request. | PMC9860016 | ||
Competing interests | DDR | MS is the owner of Neurofeedback Therapy Services of New York. He helped with the programming of the neurofeedback protocols tested in the current study. The cortical areas to be targeted, its co-ordinates and the desired training protocol (uptraining/downtraining) were provided by the principal investigators (DDR and DBA). All other authors (DBA, RM, JM, FO, SV, DDR) declare no competing interests. | PMC9860016 | |
References | PMC9860016 | |||
1. Introduction | Obesity, T2D, cardiovascular disease | OBESITY, TYPE 2 DIABETES, CARDIOVASCULAR DISEASE | Liver-expressed antimicrobial peptide-2 (LEAP-2) is associated with caloric intake and glucose metabolism. Obesity raises type 2 diabetes (T2D) and cardiovascular disease risk [LEAP-2 is expressed from the small intestine and liver [ | PMC9918887 |
2. Methods | obesity, Appetite | OBESITY, BLOOD, TOLERANCE | Participants: Twenty-five females were randomized into LCD (Cardiorespiratory Fitness: A cycle ergometer with indirect calorimetry (Carefusion, Vmax CART, Yorba Linda, CA, USA) was used to determine the cardiorespiratory fitness (VOAnthropometrics: Participants were instructed to fast for approximately 4 h prior to body composition assessment. A digital scale was first used to measure body weight to the nearest 0.01 kg. Height was then measured with a stadiometer. Body fat and fat-free mass (FFM) was determined using the BodPod (BodPod, Cosmed, CA, USA). Metabolic Control: Participants were instructed to refrain from caffeine and alcohol, as well as strenuous exercise, 48 h prior to clinical testing. Participants were also asked to abstain from taking any medications or dietary supplements 24 h prior to reporting to the Clinical Research Unit. The last training bout was performed approximately 24 h before post-intervention metabolic testing.Oral Glucose Tolerance Test: Participants arrived at the Clinical Research Unit after an overnight fast and underwent a 180 min 75 g oral glucose tolerance test (OGTT). Blood samples were obtained from an antecubital vein at 0, 30, and 60 of the OGTT for the determination of the LEAP-2 concentrations. Additional samples were collected at 0, 30, 60, 90, 120, and 180 min for glucose, insulin, C-peptide, and free fatty acids (FFA). The incremental area under the curve (iAUC) was calculated via the trapezoidal method. Post-prandial LEAP-2 stimulation was calculated as the percent change from fasting to the peak value after the glucose ingestion. The LEAP-2 to AG ratio was also calculated at 0 min and iAUC. Appetite Assessment: A 100 mm visual analog scale (VAS) was used at 0 and 120 min of the OGTT to assess the desire to eat salty, sweet, savory, and fatty foods. Subjects were instructed to indicate their perceived feeling by marking a single vertical line on the scale.Low-Calorie Diet: The 2-week diet was based on preoperative recommendations for adults with obesity undergoing bariatric surgery, and details of the protocol have been previously reported [Interval Exercise and Low-Calorie Diet: Women randomized to undergo exercise performed 12 sessions of interval exercise (INT) over the 13-day intervention, with one rest day around day seven. All exercise sessions were supervised and consisted of cycling for 3 min intervals of 50% and 90% of heart rate peak (HRpeak) for a total of 60 min. After each exercise session, a mixed-meal shake (EnsureBiochemical Analysis: Plasma samples were immediately centrifuged for 10 min at 15,000× Statistical Analysis: Analysis was performed using SPSS version 27 (IBM 26th Edition). Non-normally distributed variables were log transformed. Baseline statistics were analyzed with independent two-tailed | PMC9918887 |
3. Results | ± | BLOOD | Participant Characteristics: LCD and LCD+INT reduced caloric intake over the intervention by design, and there was no difference between groups (−752.1 ± 230.4 vs. −422.7 ± 203.3 kcals/day; Blood Lipids and Glucose Metabolism: LCD and LCD+INT reduced cholesterol (Circulating LEAP-2: Both LCD and LCD+INT decreased fasting LEAP-2 (−3.4 ± 2.1 vs. −1.4 ± 1.4 ng/dL; Visual Analog Scale: LCD or LCD+INT tended to reduce fasted desire for sweets (17.0 ± 11.6 vs. 7.3 ± 6.9; Correlations: Higher circulating LEAP-2 | PMC9918887 |
4. Discussion | obesity, T2D, fat loss, hypoglycemia, caloric deficit | OBESITY, HYPOGLYCEMIA, CVD | The main observation of this work is, short-term caloric restriction with or without exercise decreased fasting LEAP-2 in women with obesity. However, when examining the impact of glucose ingestion on LEAP-2, it was observed that LCD raised LEAP-2 more than LCD+INT. The observed reductions in fasting LEAP-2 and rise in glucose-stimulated LEAP-2 after LCD, but not LCD+INT, is contrary to our hypothesis that exercise would raise LEAP-2 during an LCD. We anticipated such a blunted decline and rise in LEAP-2 given the literature suggests exercise may reduce appetite, as well as improve glucose regulation. Consistent with prior literature though, our work confirms that of Holm et al. [There are potential explanations for why LEAP-2 was altered post-treatment. Reductions in body weight and/or fat have been linked to lower LEAP-2 [We also recognize that the reduced carbohydrate oxidation during the OGTT in LCD post-treatment from our prior work may hint at glycogen storage vs. oxidation being a factor regulating LEAP-2 during the post-prandial period. Indeed, during energy deficit LEAP-2 has been reported to decline in effort to raise blood glucose for the prevention of hypoglycemia via a potential influence of glucagon mediated hepatic glucose production [LEAP-2 has gained interest as a potential pharmaceutical drug given exogenous administration suppresses hunger and decreases food intake [It is important to consider limitations in the current work. The energy deficit of the current investigation was over the course of 2 weeks and the results may change with a longer caloric deficit. LCD lost more body fat than LCD+INT and this could impact our outcomes. Why LCD lost slightly more weight/fat is beyond the scope of this analysis but could be due to either estimating energy expenditure from exercise and refeeding an absolute amount of calories post-exercise or changes in sedentary behavior. Either way, we covaried for fat loss, and this did not impact LEAP-2. This confirms that, despite both groups losing weight, and LCD inducing approximately 0.8 kg more fat loss than LCD+INT, there was no influence on LEAP-2. Testing was performed within 24 h from the last exercise session, and LEAP-2 kinetics during the immediate to longer post-exercise period (e.g., 3 to 72 h) is unknown. LEAP-2 were collected at 0, 30, and 60 min of the OGTT and it is possible we missed the exact effect of circulating and changes in LEAP-2 on the VAS measurement. However, insulin, ghrelin, GLP-1 and other appetite hormones often peak or nadir in this same time period [In conclusion, 2 weeks of a LCD, with and without interval training lowered fasting LEAP-2. However, LCD raised glucose-stimulated LEAP-2 compared with LCD+INT. This suggests diet and exercise may influence post-prandial LEAP-2, while energy deficit is an important regulator of fasting LEAP-2. In either case, lower LEAP-2 was related to reduced desires for sweet and salty foods post-intervention only. Together, these observations suggest LEAP-2 may play a role in calorically dense food choices and glucose regulation. Thus, future work is needed to understand how LEAP-2 may impact food intake and plasma glucose following lifestyle therapy to prevent/treat obesity, T2D, and CVD. | PMC9918887 |
Author Contributions | Conceptualization and work design by S.K.M. Data collection and/or analysis by S.K.M. and T.J.R. Statistical analysis by T.J.R. and S.K.M. All authors have read and agreed to the published version of the manuscript. | PMC9918887 | ||
Institutional Review Board Statement | The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the University of Virginia Ethics Committee (IRB-HSR #18316). | PMC9918887 | ||
Informed Consent Statement | All participants gave their informed consent before they participated in the study. | PMC9918887 | ||
Data Availability Statement | These data have not been made publicly available. However, the corresponding author (S.K.M.) can provide further information on the data upon reasonable request. | PMC9918887 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC9918887 | ||
References | INSULIN RESISTANCE | Circulating LEAP-2 before and after treatments. Note: Raw data shown as mean ± SEM, log transformed for analysis. (Correlations of LEAP-2 to Appetite, Acyl-Ghrelin, and Free Fatty Acids. Note: LOG indicates variables transformed for analysis, (Body composition, Fitness, and Dietary Intake.Note: Data expressed as mean ± SEM. VOBlood Lipids and Glucose Metabolism.Note: Data expressed as mean ± SEM. LDL: low-density lipoprotein; HDL: high-density lipoprotein; iAUC: incremental area under the curve; HOMA-IR: Homeostatic model of assessment for insulin resistance; change scores (Δ) calculated Post minus Pre. T: Perceived Appetite.Note: Raw data shown as mean ± SEM. VAS: visual analog scale in millimeters; change scores (Δ) calculated Post minus Pre. T: time effect, G × T: group × time effect. | PMC9918887 | |
1. Introduction | overweight or obesity, T2DM, prediabetes, diabetes | PREDIABETES, EVENTS, TYPE 2 DIABETES MELLITUS, INSULIN SENSITIVITY, DIABETES | These two authors contributed equally to this work.The global prevalence of type 2 diabetes mellitus (T2DM) has surged in recent decades, and the identification of differential glycemic responders can aid tailored treatment for the prevention of prediabetes and T2DM. A mixed meal tolerance test (MMTT) based on regular foods offers the potential to uncover differential responders in dynamical postprandial events. We aimed to fit a simple mathematical model on dynamic postprandial glucose data from repeated MMTTs among participants with elevated T2DM risk to identify response clusters and investigate their association with T2DM risk factors and gut microbiota. Data were used from a 12-week multi-center dietary intervention trial involving high-risk T2DM adults, comparing high- versus low-glycemic index foods within a Mediterranean diet context (MEDGICarb). Model-based analysis of MMTTs from 155 participants (81 females and 74 males) revealed two distinct plasma glucose response clusters that were associated with baseline gut microbiota. Cluster A, inversely associated with HbA1c and waist circumference and directly with insulin sensitivity, exhibited a contrasting profile to cluster B. Findings imply that a standardized breakfast MMTT using regular foods could effectively distinguish non-diabetic individuals at varying risk levels for T2DM using a simple mechanistic model.The global prevalence of type 2 diabetes mellitus (T2DM) is expected to reach 783.2 million (12.2%) in 2045 [Recent studies have revealed large inter-individual variations in plasma glucose after corresponding meals and found that gut microbiota and food structure are important determinants of the differential response [Clustering of dynamic features of the postprandial response could aid in identifying differences in glucose variability to the same dietary intake and provide a simple way of categorizing individuals according to diabetes risk. Such clusters may be targets for tailored diet and lifestyle interventions to prevent prediabetes or T2DM. Differential responders were identified after consuming an oral glucose tolerance test (OGTT) [Therefore, we aimed to investigate if a simple mechanistic model of glucose regulation could be applied to describe postprandial glucose concentrations after a standardized MMTT based on regular foods and whether clusters of differential responders could be identified from such a model. Furthermore, we investigated if differential response clusters are associated differently with risk factors of T2DM. We further investigated if baseline gut microbiota was associated with the response clusters and if clusters remained after dietary intervention with low or high glycemic index. The methodology was applied to data from non-diabetic men and women from Sweden, Italy, and the USA with overweight or obesity participating in interventions with high or low glycemic index (GI) Mediterranean diets [ | PMC10609681 |
2. Materials and Methods | PMC10609681 | |||
2.1. Clinical Trial and Dietary Intervention | T2D, T2DM | TYPE 2 DIABETES, INSULIN SENSITIVITY | Data from the MEDGI-Carb trial were used in the present study because participants were at risk of developing T2DM, and the intervention tested the effect of a high vs. low GI diet within the context of a healthy eating pattern, i.e., a Mediterranean diet. By including individuals with elevated risk of T2D and using data from a dietary intervention with large contrasts in GI, we had the chance to evaluate the possibility of identifying glycemic response clusters across a wide range of likely postprandial glucose responses and to assess their stability during the intervention.The MEDGI-Carb trial was an international multi-center randomized, controlled, parallel-group, 15-week dietary trial, including a 3-week baseline period followed by a 12-week controlled dietary intervention in adults at elevated risk of developing type 2 diabetes (give their age range, BMI range, OR provide a small table with their features (also, the other risk factors, up to you)). During the 12-week intervention period, participants consumed a Mediterranean-style, controlled, isocaloric, weight-maintenance diet. Furthermore, the participants were instructed to consume either a low-GI or high-GI diet with intervention-specific foods. All participants were instructed to consume the same amount of digestible carbohydrates (270 g/d) and dietary fiber (35 g/d). Modulation of daily energy intake was achieved by adjusting intakes of proteins and fat. Half of the daily carbohydrate intake was identical in the two groups, including vegetables and fruit. The other half consisted of carbohydrates with GI < 55 and >70 in the low and high GI groups, respectively. The intervention-specific carbohydrates were distributed throughout the day, with 26% at breakfast, 30% at lunch, and 44% at dinner. Markers of glucose homeostasis were obtained during standardized testing days by completion of an eight-hour MMTT, an OGTT, and 6 days of 24 h CGM at baseline and post-testing. Furthermore, blood samples were drawn to measure HbA1c, insulin, glucose, high-density lipoprotein, triglycerides, blood pressure, and anthropometrics. Insulin sensitivity indices such as the quantitative insulin sensitivity check index (QUICKI), Stumvoll, and Matsuda were calculated using data from the OGTTs [ | PMC10609681 |
2.2. Mixed Meal Tolerance Tests | BLOOD | Breakfast and lunch MMTT were performed at baseline, mid-testing (USA only), and post-intervention. Prior to all testing days, participants were instructed not to eat or drink anything (except a small amount of water) from 10:00 p.m., the evening before the visit. Fasting blood samples were collected at the time point (TP) −15 min and TP −5 following 15 min of rest. The test meal was consumed at TP 0 in two parts; the participants had 7.5 min to consume the first part of the meal and 7.5 min to consume the last part to control the pace of the meal consumption. The participants were allowed to drink 8 ounces of water (approx. 2.4 dL) during the meal. The test meals were strictly standardized across all three centers. All participants were served the same portion size, i.e., kilocalories, regardless of energy requirement for practical reasons. The food composition and nutrients of the standardized meals are provided in Blood samples were collected at TP 15 after the test meal and then at TP 30, TP 45, TP 60, TP 90, TP 120, TP 180, and TP 240. A standardized lunch meal was served at TP 240, again with 7.5 min to consume the first half of the meal and 7.5 min to consume the second part. The blood sampling continued by the same pattern as after the breakfast meal ( | PMC10609681 | |
2.3. Oral Glucose Tolerance Test | BLOOD | Participants completed OGTT at baseline, mid-testing (USA only), and post-intervention. Fasting blood samples were collected at TP −15 after 15 min of rest and at TP −5. At TP 0, participants were instructed to consume a test beverage containing 75 g glucose dissolved in water within 5 min. No additional fluids were permitted during the test. Blood samples were collected at TP 60 and TP 120 ( | PMC10609681 | |
2.4. Fecal Microbiota | LYSIS | During pre- and post-intervention study days, participants were asked to collect fecal samples using a stool sampling collection kit. Samples were taken using an EasySampler Stool Collector and a sample tube with a spoon lid. The sample was protected by being placed in yet another tube and stored immediately in the freezer (−20 °C). The samples were transported in a cooling box with an ice pack to the clinic within 72 h after the sample was collected. At the clinic, the sample is transferred to −80 °C within 24 h. The samples were analyzed with the 16S rRNA gene amplicon sequencing method, where DNA was extracted and purified from fecal samples using the QIAamp FAST DNA Stool mini kit from Qiagen, Venlo, The Netherlands. The DNA extraction followed the protocol from the manufacturer with one exception, where lysis of the bacterial cell walls used a mechanical lysis step (bead beating 2 × 1 min in a Precellys Evolution using 0.1 mm zirconium/silica beads). Once the DNA was extracted and purified from the sample, polymerase chain reaction (PCR) was used to amplify the V3-V4 region of the gene encoding 16S rRNA. This gene exists in all bacteria and is normally used for the taxonomic classification of bacteria since parts of the 16S gene vary in sequence composition between different bacteria. Sample-specific barcodes and Illumina adapters were then attached to the PCR amplicons to enable the pooling of samples. The library of PCR amplicons was then sequenced on the Illumina NovaSeq 6000 platform at Novogene, Singapore. The bioinformatics analysis to handle the generated sequence data used QIIME2 via the dada2 pipeline. The sequences were first demultiplexed, i.e., separated according to the sample-specific barcode in the specific sample. Then, quality control and filtration of the sequence data was performed to remove sequences with poor quality. Finally, a taxonomic classification of the sequences was performed. The gut microbiota analysis and subsequent data processing and analysis were described in detail by Iversen and Dicksved [Species that were known from the literature to associate with glucose regulation were selected to assess association with response clusters. The selected species were “ | PMC10609681 | |
2.5. Mechanistic Model of Glucose Regulation | A modified version of the minimal glucose model [The dynamics of the model are described using compartments that represent mechanisms in the glucose–insulin system, and the exchange rates between compartments are described using rate constants. The model assumes that the ingested glucose is delayed by the digestive system and transferred to the bloodstream, where insulin acts to let the glucose be absorbed by the muscle tissue or the liver and converted to glycogen. Furthermore, the model assumes that the glucose can be discarded through the urine via the kidneys and that the pancreas produces insulin at a given rate in response to the current glucose concentration. Notably, the model assumes a linear relationship between insulin secretion and glucose, which is often not the case due to the effects of incretin hormones, for example, but the rationale is to obtain a more parsimonious model with easily grouped parameters. A particularly simple solution for the model glucose concentration can be formulated if the gastrointestinal absorption is assumed to rise very quickly and fall slowly and the ingested breakfast meal is modeled as a momentary impulse at the first measurement in time [Although the parameters of the reduced model have no one-to-one correspondence to specific mechanisms in the body, they convey the general quality of the glucose control.The sinusoidal frequency (The model was originally shown to fit OGTT data well, despite mechanisms such as the role of adrenal cortical and medullary function in glucose economy were not accounted for [ | PMC10609681 | ||
2.6. Statistical Analyses | The parameters of the model were estimated within the nonlinear mixed effects model framework [The individual parameters The association between response clusters and gut microbiota was investigated using ANOVA on selected genera (explained in the fecal microbiota section) that were reported to associate with glycemic regulation. Additionally, we measured the Pearson correlation between estimated model parameters that separate response clusters and t-tests to assess the significance of the correlation. The intervention effect on gut microbiota composition was investigated using log fold change in the baseline and 12 wk. on all species using random forest analysis within a repeated double cross-validation framework [The parameter estimation software Monolix was used to simultaneously estimate the random and fixed effects (Monolix 2021R2, Lixoft SAS, a Simulations Plus company, Lancaster, CA, USA). Covariates were imposed to reduce the variance not reflecting blood glucose control. Age and site were imposed as covariates on the baseline (Here, | PMC10609681 | ||
3. Results | In total, 155 individuals completed the two MMTTs and OGTTs (baseline and wk. 12) and were included in the analyses. Calculations on fecal microbiota were based on 130 individuals who provided two fecal samples within the participants that performed the two MMTT and OGTTs (baseline and wk. 12) ( | PMC10609681 | ||
Postprandial MMTT Glucose Responses | diabetic, prediabetes | PREDIABETES | Individual parameters of the kinetic model (baseline, amplitude, damping, and frequency) from Equation (1) were estimated using the postprandial MMTT glucose response at baseline and wk. 12. The parameters were successfully estimated with RSE < 43% in all cases, which indicated certainty in the estimates. Variation among individuals resulted only in the parameters amplitude Two plasma glucose concentration profile clusters (A and B) were successfully identified (RSE < 33%), which were well separated in the amplitude and frequency parameters but not in the baseline parameter, although the cluster membership was estimated in the baseline parameter as well (The individuals in cluster A had, in general, a higher frequency The clusters at baseline were associated with known diabetic risk markers such as HbA1c (Importantly, the clusters also associated differently with conditions reflecting clinical cut-offs for differential glucose control, i.e., prediabetes (fasting HbA1c Most of the subjects classified as normoglycemic also belonged to cluster A, and most of the subjects classified as “impaired” or “diabetic” belonged to cluster B (The same analysis as described using the breakfast MMTT response before the intervention (at baseline) was made using the breakfast MMTT response post-intervention, where similar cluster memberships were identified (Some of the individuals changed cluster from pre- to post-trial (~26% change in each cluster), but there was no significant difference between clusters (Cohen’s kappa = 0.42, moderate stability between clusters (95%CI 0.27–0.56)). The change in parameters from baseline to wk. 12 (change = baseline − wk. 12) did not correlate significantly with the change in risk markers.Interestingly, the identified plasma glucose response clusters at baseline were associated with the gut microbiota genera | PMC10609681 |
4. Discussion | T2D, T2DM, nausea | EVENTS, INSULIN SENSITIVITY | To dissect glucose data into features representing postprandial events, we used a model with only four parameters to identify clusters from standardized breakfast meal tolerance test responses that strongly related to T2DM risk factors. Although the model did not capture all systematic variation in the data, it was flexible enough to allow the identification of differential glycemic response clusters after mixed meal tests that were differentially associated with risk factors of T2D and gut microbiota. The results suggest that a standardized breakfast meal could provide meaningful data to predict risk factors of T2DM from dynamic glucose response measurements.Plasma glucose response clusters were mostly separated in the amplitude and frequency parameters and not in the baseline glucose parameter (The breakfast MMTT clusters did not associate with the study site nor with treatment, which suggests that using these as covariates successfully captured their variance in the data. However, the low GI group improved more than the high GI group in their estimated parameters (decreased average baseline, decreased average amplitude, and increased average frequency) after a 12-week intervention, which suggests that the low GI diet aided in improving the glycemic control of the participants [Our data suggest that a standardized breakfast MMTT based on regular foods may be an alternative to an OGTT, especially among patients with a high risk of nausea, such as pregnant women or bariatric surgery patients [Previous studies have shown that gut microbiota is associated with postprandial glucose response [Our study has several strengths, including the large sample size with participants from three countries (Italy, USA, and Sweden), which reduces the chances that treatment effects or found clusters would be confounded by the cohort. In addition, the MMTT was robustly designed with participants carefully monitored during the test days and strictly standardized meal composition across the three centers to reduce the risk that the differences in response would be due to differences in intake. Furthermore, the mechanistic model gave interpretable clusters using only four identifiable parameters, which were estimated using a mixture of lognormal distributions. Although a mixture of distributions is rarely used, it proved useful in estimating the likelihood of cluster membership. However, although the method enabled investigating glucose control from the dynamical response, it should be noted that all descriptive variance was not captured using the model (e.g., slow undershoot).Limitations included the fact that all participants were at risk of developing T2DM. Hence, although OGTT and MMTT responses were described using the same model to estimate insulin sensitivity [The MMTT used in the present trial was based on a Mediterranean diet. However, different diets and foods may have different effects on gastric emptying time and blood glucose response, which should be taken into consideration when designing future studies [ | PMC10609681 |
5. Conclusions | T2D, T2DM | We used a simple model to successfully describe glucose response to a standardized breakfast MMTT based on common foods and identified two response clusters that were associated differently with T2DM risk markers and gut microbiota. Future studies should investigate if such clusters can be identified by an algorithmic self-sampling tool for the classification of differential T2D risk profiles based on standardized breakfast MMTT in a home setting using continuous glucose monitoring and whether tailored diet and lifestyle advice may lower T2D risk. | PMC10609681 | |
Supplementary Materials | The following supporting information can be downloaded at Click here for additional data file. | PMC10609681 | ||
Author Contributions | Conceptualization, R.L. and V.S.; methodology, V.S. and M.W.; software, V.S.; validation, V.S.; formal analysis, V.S.; investigation, V.S. and T.H.; resources, R.L., G.R. and W.W.C.; data curation, R.L., G.R., R.E.B. and W.W.C.; microbiota analyses, J.D., E.A.P. and V.S.; writing—original draft preparation, T.H. and V.S.; writing—review and editing, T.H. and V.S.; feedback on manuscript, R.L., M.W., C.B., M.J., G.R., J.D., E.A.P., A.E., M.V., R.G., G.C., R.E.B., G.R. and W.W.C., visualization, V.S. and T.H.; supervision, R.L., M.W., M.J., C.B., W.W.C. and G.R.; project administration, R.L.; funding acquisition, G.R., R.L. and W.W.C. All authors have read and agreed to the published version of the manuscript. | PMC10609681 | ||
Institutional Review Board Statement | RECRUITMENT | The study was conducted in accordance with the Declaration of Helsinki and approved by The Regional Ethical Review Board, Gothenburg, Sweden (DNR 663-17, approved: 14 December 2017), the institutional review board of Federico II University (n. 175/17, approved: 25 October 2017), and the biomedical institutional review board at Perdue University (n. 1610018310, approved: 9 January 2018). The trial was registered in the public trial registry clinicaltrials.gov as NCT03410719 prior to initiating participant recruitment. | PMC10609681 | |
Informed Consent Statement | Written informed consent was obtained from all participants involved in the study and was obtained prior to any collection of data or sampling. | PMC10609681 | ||
Data Availability Statement | The data presented in this study are available upon reasonable request from the corresponding author. | PMC10609681 | ||
Conflicts of Interest | G.R. is a member of the Scientific Advisory Board of the Nutrition Foundation of Italy and the Istituto Nutrizionale Carapelli Foundation; he is a member of the Health and Wellbeing Advisory Board of the Barilla G&R. Fratelli Company and Consultant for a Metabolic Health Masterclass sponsored by Nestlè. R.B. is currently employed by ADM, but the research presented in this paper was conducted in a former role and has no connection with ADM. V.S., T.H., C.B., M.W., M.J., M.V., R.G., J.D., E.P., A.E., W.W.C., and R.L. report no conflict of interest. | PMC10609681 | ||
References | diabetic”, diabetic, diabetes | IMPAIRED GLUCOSE TOLERANCE, DIABETES | Example dynamics generated from the model in Equation (1). The blue curve is characterized by a fast biphasic response to the MMTT, thus having high frequency (Model fit to postprandial breakfast MMTT response at baseline of 16 randomly selected representative subjects. Here, points represent measurements, and lines represent the fitted model values.Joint parameter distribution obtained by fitting the model in Equation (1) to the postprandial breakfast MMTT data. The blue and red colors represent clusters A and B, respectively. The diagonal represents histograms of the parameter distribution (color-coded by transparent cluster color), and the off-diagonal represents pairwise joint distributions. Note that overlapping colors appear brown.Baseline postprandial breakfast MMTT response color-coded by the clusters.Baseline joint distribution of diabetes risk markers, which had significant associations with clusters. The diagonal represents histograms of the parameter distribution (color-coded by transparent cluster color), and the off-diagonal represents pairwise joint distributions. Note that overlapping colors appear brown.Baseline joint parameter distribution obtained by fitting the model in Equation (1) to the postprandial MMTT data. The different markers (dots, triangles, and asterisk) represent subjects classified as “normoglycemic”, “impaired” glucose control, or “diabetic”, respectively. Here, we used the OGTT measurement after 2 h and classified normal glycemic regulation as <7.7 mmol/L, impaired glucose tolerance in the range of 7.8–11.0 mmol/L, and diabetic ≥ 11.1 mmol/L. The diagonal represents histograms of the parameter distribution (color-coded by transparent cluster color), and the off-diagonal represents pairwise joint distributions. Note that overlapping colors appear brown.Week 12 joint parameter distribution obtained by fitting the model in Equation (1) to the postprandial breakfast MMTT data. The blue and red colors represent clusters A and B, respectively. The diagonal represents histograms of the parameter distribution (color-coded by transparent cluster color), and the off-diagonal represents pairwise joint distributions. Note that overlapping colors appear brown.Food composition and nutrients of standardized meals.* Edible amount.Baseline characteristics of the subpopulations analyzed in MMTT, OGTT, and fecal microbiota. There was no significant difference between treatment groups. | PMC10609681 |
Background | anxiety | Full list of collaborator names appear in Child anxiety before general anaesthesia and surgery is common. Midazolam is a commonly used premedication to address this. Melatonin is an alternative anxiolytic, however trials evaluating its efficacy in children have delivered conflicting results. | PMC10797512 | |
Methods | This multicentre, double-blind randomised trial was performed in 20 UK NHS Trusts. A sample size of 624 was required to declare noninferiority of melatonin. Anxious children, awaiting day case elective surgery under general anaesthesia, were randomly assigned 1:1 to midazolam or melatonin premedication (0.5 mg kg | PMC10797512 | ||
Results | The trial was stopped prematurely ( | PMC10797512 | ||
Conclusion | anxiety | Melatonin was less effective than midazolam at reducing preoperative anxiety in children, although the early termination of the trial increases the likelihood of bias. | PMC10797512 | |
Clinical trial registration | ISRCTN registry: ISRCTN18296119. | PMC10797512 | ||
Keywords | Handling Editor: Rupert Pearse
| PMC10797512 |
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