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CONCLUSION | PD, gait impairments | Our findings suggested that taVNS could relieve gait impairments and remodel sensorimotor integration in PD patients. The results provided insights into the neural mechanism of taVNS and a new neuromodulation method for treating gait impairments in PD patients. | PMC10651956 | |
AUTHOR CONTRIBUTIONS | Heng Zhang: Conceptualization, Data acquisition, Formal analysis, interpretation, Writing‐original draft, Writing‐review & editing. Xing‐yue Cao: Conceptualization, Data acquisition, Writing‐review & editing. Li‐na Wang, Qing Tong, Hui‐min Sun, and Cai‐ting Gan: Data acquisition, Writing‐review & editing. Ai‐di Shan: Grouping and intervention of participants. Yong‐sheng Yuan: Conceptualization, Data acquisition, Writing‐review & editing, Funding acquisition. Ke‐zhong Zhang: Conceptualization, Data acquisition, safety assessment, Writing‐review & editing, Study supervision, Funding acquisition. | PMC10651956 | ||
FUNDING INFORMATION | This work was funded by the National Natural Science Foundation of China (82271273) and the Jiangsu Social Development Project (BE2022808). | PMC10651956 | ||
CONFLICT OF INTEREST STATEMENT | Authors declare no conflict of interest. | PMC10651956 | ||
CONSENT | Written informed consent for publication was obtained from all participants. | PMC10651956 | ||
Supporting information |
Tables S1–S3
Click here for additional data file. | PMC10651956 | ||
ACKNOWLEDGMENTS | We are grateful to Deyu Ji (the engineer of Danyang Huichuang Medical Equipment Company) for his help in fNIRS data analysis and to Li Liu (the employee of the First Affiliated Hospital of Nanjing Medical University) for her help in data collection. | PMC10651956 | ||
DATA AVAILABILITY STATEMENT | The original contributions presented in the study are included in the article/ | PMC10651956 | ||
REFERENCES | PMC10651956 | |||
Objective | T2DM, CHD | CORONARY HEART DISEASE, TYPE 2 DIABETES MELLITUS | We aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk. | PMC10401778 |
Methods | diabetic, CHD, T2DM, Diabetes | SEPARATION, DIABETES | This study considered the 462 patients of the CORDIOPREV study (CHD patients) who were not diabetic at the beginning of the intervention. In total, 107 of them developed T2DM after a median follow-up of 60 months. They were diagnosed using the American Diabetes Association criteria. A novel lipidomic methodology employing liquid chromatography (LC) separation followed by HESI, and detection by mass spectrometry (MS) was used to annotate the lipids at the isomer level. The patients were then classified into a Training and a Validation Set (60–40). Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. Finally, a production model combining the clinical variables of interest, and the lipidic species was carried out. | PMC10401778 |
Results | T2DM | INSULIN SENSITIVITY | LC-tandem MS annotated 440 lipid species. From those, the RSF identified 15 lipid species with the lowest prediction error. These lipids were combined in an LR score which showed association with the development of T2DM. The LR hazard ratio per unit standard deviation was 2.87 and 1.43, in the Training and Validation Set respectively. Likewise, patients with higher LR Score values had lower insulin sensitivity ( | PMC10401778 |
Supplementary Information | The online version contains supplementary material available at 10.1186/s12933-023-01933-1. | PMC10401778 | ||
Keywords | PMC10401778 | |||
Background | T2DM, CHD, metabolic disorder | HYPERGLYCAEMIA, DIABETES MELLITUS, DYSLIPIDAEMIA, METABOLIC DISORDER | Diabetes mellitus, a metabolic disorder defined by high blood glucose levels (i.e. hyperglycaemia) [The simultaneity of T2DM with CHD raises the risk of mortality by up to 80% compared to the ratio observed across individuals without CHD [Dyslipidaemia associated with T2DM is characterized by increased concentrations of low-density lipoproteins (LDL) cholesterol particles, low levels of high-density lipoproteins (HDL) cholesterol, and high plasma triglycerides [In this study, we carried out a highly-sensitive lipidomic protocol capable of defining the compounds at such a level of detail [ | PMC10401778 |
Methods | PMC10401778 | |||
Study subjects | T2DM, CHD, Diabetes | EVENT, CORONARY HEART DISEASE, DIABETES | The current work was conducted within the framework of the Coronary Diet Intervention with Olive Oil and Cardiovascular Prevention Study (CORDIOPREV; Clinical trials.gov. Identifier: NCT00924937). This is an ongoing prospective, randomized, open, controlled trial with 1002 patients. The patients received conventional treatment for CHD and had their last coronary event took place over 6 months before joining the study. The volunteers followed one of two different dietary models, a Mediterranean or a low-fat diet, for 7 years, in addition to their conventional treatment for coronary heart disease [The patients were recruited principally at the Reina Sofia University Hospital (Cordoba, Spain), with contributions from other health centres in Cordoba and Jaen, between November 2009 and February 2012. The eligibility criteria, design, and methods of the CORDIOPREV clinical trial were already reported [Our study included 462 patients from the CORDIOPREV study (N = 1002). These patients had not been diagnosed with T2DM at the beginning of the study according to specifications from the American Diabetes Association (ADA) T2DM diagnosis criteria [These patients were followed up for a median of 60 months and 107 developed T2DM (incident-DIAB), according to the ADA T2DM criteria [ | PMC10401778 |
Study experimental design | The study design has been previously described [ | PMC10401778 | ||
Lipidomic analysis | The protocol to carry out the lipidomic analysis is described elsewhere [ | PMC10401778 | ||
Random survival forest | The lipidomic data were normalized using log transformation and scaled in multiples of 1 standard deviation (SD). We then performed a random classification of patients into two different datasets: a Training Set with 274 patients (60% of the total), in which the variables were selected using RSF [ | PMC10401778 | ||
Lipidomic Risk score building | T2DM | REGRESSION | We performed a Cox proportional hazards regression analysis with the 15 lipids selected by RSF in the Training set to determine the potential use of these lipids as an independent predictor of T2DM development. Next, a LR Score was built by multiplying the coefficients obtained for every lipid in the previous step (the Cox analysis) by its plasma concentration. This LR Score was built into both the Training and the Validation set. Furthermore, patients were classified according to the score generated to carry out a second Cox proportional hazards regression with each one of those variables, adjusted by diet, age, gender, body mass index (BMI), HDL, TGs, and statin intensity treatment. Finally, the predictive capacity of this score was evaluated by classifying the same population with different cut-off points. | PMC10401778 |
Statistical analysis | We used RStudio [ | PMC10401778 | ||
Results | PMC10401778 | |||
Baseline characteristics of the participants | T2DM | TYPE 2 DIABETES MELLITUS, INSULIN SENSITIVITY | The baseline characteristics of the subjects in the study are shown in Table Baseline characteristics of the population for type 2 diabetes mellitus incidence studyMeans values ± S.E.MIncident-DIAB: non-diabetic patients at baseline who developed T2DM after a median follow-up of 60 months; non-DIAB: non-diabetic patients at baseline who did not develop T2DM after a median follow-up of 60 months; BMI: body mass index; HbA1c: glycated hemoglobin A1c; ISI: insulin sensitivity index; IGI: insulinogenic indexOne-way ANOVA P-values | PMC10401778 |
Random survival forest | T2DM, diabetes | REGRESSION, TYPE 2 DIABETES, TYPE 2 DIABETES MELLITUS, DIABETES | A stepwise RSF was carried out in the Training Set (60% of patients) to select the lipids with greater predictive power for T2DM development. Thus, the 15 lipids that produced the lowest prediction error, out of a total of 440 lipids variables originally tested, were included in the final model (Fig. Selection of the best model by Random Survival Forest. Selection in the Training set of lipid species with a higher predictive power for type 2 diabetes, by applying an Random Survival Forest in combination with a backward selection procedureSelection of lipids included in the model with the lowest prediction error in the Training SetPE: phosphatidylethanolamine; PG: phosphatidylglycerol; PI, phosphatidyl inositol; PC: phosphatidylcholine; TG: triacylglycerols; LPC: lysophosphatidylcholine; PS: phosphatidylserineTo evaluate the relationship of these lipids with the development of diabetes, an individual Cox proportional regression model was made for each of the 15 selected lipids. In total, 8 of the 15 lipids were directly associated with T2DM development, while 7 of them were inversely associated with T2DM development. It is important to note that different members of the PC, PE, phosphatidyl glycerol (PG), and phosphatidyl inositol (PI) families were associated with both the development and non-development of T2DM (Table Association between lipids selected and type 2 diabetes mellitus development in the Training Set, per standard deviation increaseModel 1 was unadjusted and Model 2 was adjusted by age, gender, diet, body mass index, high density lipoproteins-cholesterol, plasma triacylglycerols, and statin intensity treatmentHR: hazard ratio; CI: confidence interval. LPC: lysophosphatidylcholine; PC: phosphatidylcholine; PE: phosphatidylethanolamine; PG: phosphatidylglycerol; PI, phosphatidyl inositol; PS: phosphatidylserine; TG: triacylglycerolsMoreover, the model with the 15 lipids produced a C Index of 0.714; but when the clinical variables (i.e., diet, age, gender, BMI, HDL, TGs, and statin intensity treatment) were added into the model, the C Index increased to 0.757, while the clinical variables, taken separately, showed a C Index of only 0.618 (Table Parameters of the different modelsReceiver operating characteristic curves of the model including the clinical variables separately and the model including the 15 lipids and the clinical variables. The clinical variables included: age, gender, diet, body mass index, high density lipoproteins-cholesterol, plasma triacylglycerols, and statin intensity treatment. AUC: area under the curveWe then tested the predictive power of the RSF model with the selected lipids in the Validation Set. The C Index was 0.703 for lipids, which rose to 0.755 when the clinical parameters were included, which had previously yielded 0.653 when they were tested separately. ROC curves of the selected lipids yielded an AUC of 0.742, which increased to 0.799 when the clinical variables were included, while the clinical variables taken separately yielded an AUC of only 0.659 (Fig. | PMC10401778 |
Results from the score based on the lipidomic profile | T2DM | REGRESSION | A LR Score was built to assess the relationship between the lipidomic profile and T2DM development (see Materials and Methods). To achieve this, the coefficients obtained for each of the 15 lipids in the Cox proportional hazards regression were multiplied by the lipid concentrations in plasma for each subject (Table Next, the prediction power of the score created was evaluated by categorizing patients according to the LR Score by ascending tertiles, quartiles, and the median, in both the Training and the Validation set.In the Training set (Fig. Disease-free survival by Cox proportional hazards regression analysis according to lipid species score in the Training Set. Patients from the Training set were categorized according to the Lipidomic Risk score by tertiles, quartiles, and median (in ascending order). *This model was adjusted for age, gender, diet, body mass index, high density lipoproteins-cholesterol, plasma triacylglycerols, and statin intensity treatment. The hazard ratio (HR) between groups was calculated. CI: confidence intervalWe also analysed the LR Score in the validation set (Fig. Disease-free survival by Cox proportional hazards regression analysis according to lipid species score in the Validation Set. Patients from the validation set were categorized according to the Lipidomic Risk score by tertiles, quartiles, and median (in ascending order). *This model was adjusted for age, gender, diet, body mass index, high density lipoproteins-cholesterol, plasma triacylglycerols, and statin intensity treatment. The hazard ratio (HR) between groups was calculated. CI: confidence interval | PMC10401778 |
Relationship between Lipidomic Risk score and insulin resistance and beta-cell functionality indexes | INSULIN RESISTANCE | We also studied the relationship between LR Score, insulin resistance, and beta-cell function as assessed by validated indexes during the follow-up (Fig. Relationship between lipid profile and insulin resistance and beta-cell functionality indexes according to the ascending tertiles of the Lipidomic Risk score. Patients were categorized according to the Lipidomic Risk score by ascending tertiles. ANOVA for repeated measures p-values adjusted by age, gender, diet, body mass index, high density lipoproteins-cholesterol, and plasma triacylglycerols. Global p-values: P(time): time effect; P(tertile): tertile of the Lipidomic Risk score effect; P(interaction): time by tertiles of the Lipidomic Risk score interaction | PMC10401778 | |
Discussion | acute myocardial infarction, dyslipidaemia, CHD, T2DM, diabetes | ACUTE MYOCARDIAL INFARCTION, DISEASE, EVENT, SECONDARY, CORONARY HEART DISEASE, DIABETES | Despite the determining role of dyslipidaemia in T2DM, the molecular mechanisms and the involvement of the specific lipid species behind this role are not yet well understood [T2DM is currently the most prevalent form of diabetes, affecting around 380 million people worldwide, and accounting for 90% of all cases. It is also on the rise, mainly due to the prevalence of sedentary lifestyles and inadequate diets [Current predictive models in T2DM research combine classic biomarkers and risk factors, including serum parameters, anthropometric characteristics, and factors related to lifestyle. On the FINDRISC questionnaire [This study showed that the predictive capacity of the clinical variables was significantly improved by the addition of 15 lipid species, selected by RSF from a total of 440 determined by our experimental approach. To the best of our knowledge, this is the first time that a lipidomic study has been carried out in a risk population of CHD patients to predict diabetes incidence. Nevertheless, a previous study in a non-CVD population also observed an improvement in the prediction capacity of their model when lipids were added to conventional risk factors [Unlike the study by Razquin et al., which described a lipid profile based only on lipid classes associated with T2DM incidence, our study shows that we need to analyse individual lipid species to accurately differentiate the directionality of the association with T2DM. We identified four members of the PE lipid family, of which two, PE(16:0_18:1), and PE(O-20:0/18:0), were associated with the development of T2DM, whereas the other two, PE(16:1_18:1) and PE(18:0_18:2), were protective against T2DM. Moreover, while the relationship of PE with T2DM risk has been previously reported, the specific species and isomers have not been described previously [Similarly, two compounds from the PC family were identified by the RSF as associated with T2DM in opposite ways, one protecting and another promoting the disease. PC(P-16:0/18:1) is linked with a protective role against the disease, while PC(P-16:1/18:0) is associated with diabetes development. PC is the only phospholipid essential for the assembly, secretion, and regulation of lipoproteins such as LDL and HDL [In contrast, isomers identified within the TGs, phosphatidylserines (PSs), and LPC are unidirectional. Among these three families, LPC is the only one with a protective role in preventing diabetes development. LPC is a hydrolysis product derived from the catalysis of phosphatidylcholine by phospholipase AFinally, it is important to mention the limitations of this study. Firstly, this research is based on a long-term, closely controlled dietary intervention, which, despite ensuring the quality of the study, may not reflect the level of compliance in a free-living population.The second limitation is that the incidence of T2DM was not the primary endpoint of the CORDIOPREV trial, although it was a secondary objective of this study. Indeed, our study has the limitation that the incident-DIAB group has higher baseline glucose levels and an unbalanced number of men and women included as participants. In fact, this population was included in the CORDIOPREV study without any type of selection, therefore representing the sexual dimorphism existent in CHD, and any attempt to balance the number of men and women may introduce a bias. Moreover, the study included patients with coronary heart disease, which limits our findings to people with these characteristics and precludes its generalization to healthy individuals. Although diabetes prediction is extremely important since patients with acute myocardial infarction and T2DM have a considerably higher risk of developing a new cardiovascular event than those without T2DM [ | PMC10401778 |
Conclusion | T2DM | INSULIN RESISTANCE, INSULIN SENSITIVITY | Overall, this study has shown the potential of highly sensitive lipidomics in identifying patients at risk of developing T2DM. In addition, the lipid species identified as associated with T2DM development, combined with clinical variables, have provided a new, highly sensitive model to be used in clinical practice. The findings also suggest that the risk of T2DM development is associated with a specific lipidomic profile which is characterized by lower peripheral insulin sensitivity and higher hepatic insulin resistance. Finally, these results also indicate that we need to look closely at isomers to understand the role of this specific compound in T2DM development since isomers of the same class of lipids are associated with different outcomes. | PMC10401778 |
Acknowledgements | The CIBEROBN is an initiative of the Instituto de Salud Carlos III, Madrid, Spain. We would like to thank the Córdoba branch of the Biobank of the Sistema Sanitario Público de Andalucía (Andalusia, Spain) for providing the biological human samples. We would also like to thank the EASP (Escuela Andaluza de Salud Publica), Granada, Spain, which performed the randomization process for this study. Parts of the figure were drawn using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License ( | PMC10401778 | ||
Author contributions | JLM, AC, PPM, JDL, MBS: conceptualization. JLM, FE, ALB, MCS, FPC, AVG, MMO, JFAD: methodology. AVG, MMO, JFAD, FE, APAL: formal analysis. AVG, MMO, FE, FPC: investigation and data curation. JLM, AC, ALB, MCS, FPC: resources. AVG, MMO, JFAD, APAL: writing—original draft. FPC, MMM, FE, PPM, JDl, MBS, AC, JLM: writing—review and editing. MM-O, MMM: visualization. JLM, AC, PPM, JDL: supervision. JLM, AC, MBS, MMO: funding acquisition. JLM, AC, MBS: project administration. | PMC10401778 | ||
Funding | The CIBEROBN is an initiative of the Instituto de Salud Carlos III, Madrid, Spain. The CORDIOPREV study is supported by Ministerio de Ciencia e Innovación, Spain (AGL2012/39615, PCIN-2016-084, and PID2019-104362RB-I00 to J L-M; AGL2015-67896-P to J L-M and AC); Grants AGL2012/39615, PCIN-2016-084, AGL2015-67896-P and PID2019-104362RB-I00 funded by MCIN/AEI/1.0.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”; Instituto de Salud Carlos III (PIE14/00005, PIE14/00031, ICI20/00069 to JL-M, CP14/00114, DTS19/00007, PI19/00299 and PI22/00925 to AC); Fundación Patrimonio Comunal Olivarero, Junta de Andalucía (Consejería de Salud, Consejería de Agricultura y Pesca, Consejería de Innovación, Ciencia y Empresa), Diputaciones de Jaén y Córdoba, Centro de Excelencia en Investigación sobre Aceite de Oliva y Salud and Ministerio de Medio Ambiente, Medio Rural y Marino, Gobierno de España; Consejería de Innovación, Ciencia y Empresa, Junta de Andalucía (PY20-00256 to J L-M); Consejería de Salud y Familias, Junta de Andalucía (PI-0055-2021 to AC and MM-O) and co-funded by the European Union. This project has also received funding from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement No 847468. Antonio Camargo is supported by an ISCIII research contract (Programa Miguel-Servet CP14/00114 and CPII19/00007) and Servicio Andaluz de Salud-Junta de Andalucia (Nicolas Monardes Programme Contract C1-0001-2022). | PMC10401778 | ||
Availability of data and materials | Collaborations with the Cordioprev Study are open to Biomedical Institutions, always after an accepted proposal for scientific work. Depending on the nature of the collaboration, electronic data, hard copy data, or biological samples should be provided. All collaborations will be made after a collaboration agreement. Terms of the collaboration agreement will be specific for each collaboration, and the extent of the shared documentation (ie, deidentified participant data, data dictionary, biological samples, hard copy, or other specified data sets) will be also specifically set on the light of each work. | PMC10401778 | ||
Declarations | PMC10401778 | |||
Ethics approval and consent to participate | The current work was conducted within the framework of the Coronary Diet Intervention with Olive Oil and Cardiovascular Prevention Study (CORDIOPREV; Clinical trials.gov. Identifier: NCT00924937). The trial protocol was approved by the Reina Sofia University Hospital Ethics Committee, following the Helsinki declaration and good clinical practices. All the patients gave their written informed consent to participate in the study. The experimental protocol conformed to international ethical standards. | PMC10401778 | ||
Consent for publication | Not applicable. | PMC10401778 | ||
Competing interests | The authors declare no competing financial interests. | PMC10401778 | ||
References | PMC10401778 | |||
1. Introduction | advanced-stage, Parkinson’s disease, ’s disease, PD | Current pharmacotherapy has limited efficacy and/or intolerable side effects in late-stage Parkinson’s disease (LsPD) patients whose daily life depends primarily on caregivers and palliative care. Clinical metrics inadequately gauge efficacy in LsPD patients. We explored if a DParkinson’s disease (PD) is characterized clinically by motor and non-motor symptoms. Despite research advances related to disease-modifying therapy, symptomatic treatment using the dopamine precursor levodopa remains the therapeutic cornerstone [As PD patients advance to LsPD, there is an increasing family and caregiver burden and higher healthcare costs compared to early- and advanced-stage patients [Dopamine receptors were first differentiated into two pharmacological classes, DPromisingly, there is compelling neurobiological and pharmacological evidence for the potential of DThe accessibility to the orally available D | PMC10216182 | |
2. Methods | PMC10216182 | |||
2.1. Study Design, Subjects, and Randomization | PSH | DISEASE, RECRUITMENT | This study was conducted at PennStateHealth (PSH) in compliance with the Declaration of Helsinki and guidelines for Good Clinical Practice issued by the International Conference on Harmonization. It was reviewed and approved by the US Food and Drug Administration and PSH Institutional Review Board. All participants and caregivers provided signed informed consent. Details of subject recruitment, inclusion and exclusion criteria, baseline medical, protocol information, and safety data were published in a previous report [All LsPD subjects had disease duration >15 y and Hoehn and Yahr (HY) stages ≥IV, either “on” or “off” levodopa. Our criteria adapted the terminology of Coelho and Ferreira [Eligible participants were randomized to PF-2562 (Sequence A) or levodopa (Sequence B) during Test Period 1 using a 1:1 random allocation sequence and then crossed over to the other drug during Test Period 2 ( | PMC10216182 |
2.2. Study Compound Choice | The initial pilot study focused on establishing the safety and tolerability of a D | PMC10216182 | ||
2.3. Quantitative Data and Metrics | We included five standard quantitative scales [As detailed in our previous report [ | PMC10216182 | ||
2.4. Qualitative Interviews | ADVERSE EFFECTS | Qualitative data collection was chosen to capture broad, nuanced experiences, observations, and perspectives of caregivers regarding potential efficacy and/or side effects. Semi-structured caregiver interviews (30–60 min) were conducted by a trained qualitative research assistant at the end of Day 3. Responses were audio-recorded and transcribed verbatim. Interviews explored caregiver-perceived patient response to study drug (if any) and adverse effects compared to patient baseline status. The interview guide used open-ended questions to elicit first general observations from caregivers and then probed specific domains of motor, alertness, cognition, and sleep. | PMC10216182 | |
2.5. Convergent Mixed Methods Design | Convergent mixed methods designs collect both quantitative and qualitative data for a ‘domain’ and then compare/contrast the conclusions from each dataset (‘merging’) to reach a comprehensive conclusion [ | PMC10216182 | ||
3. Results | PMC10216182 | |||
3.1. Participants | dehydration | DISEASE, PATHOPHYSIOLOGY, DEHYDRATION, KIDNEY DYSFUNCTION, AUTONOMIC DYSFUNCTION | Six subjects met the inclusion criteria (demographics in Of the six patients who were randomized, one (subject 6, disease duration 19 y) withdrew after the first arm because of blood pressure fluctuations the clinical team felt were related to the interaction of the test drug with baseline dehydration, related kidney dysfunction, and autonomic dysfunction [Key narrative phrases from caregiver interviews qualitatively described the patient’s baseline functional status (Subject 8 had the longest disease duration (32 y). All drugs, including levodopa, had caused intolerable side effects, and thus, this patient had not been treated with any Parkinsonian drugs for three years prior to study enrollment. On most days, he was in unarousable “deep sleep”, but able to reflexively suck/swallow if his mouth was stimulated with a straw or food in a more “awake” state. Because of his atypical background and long survival without dopaminergic medication, we highlight his response to treatment in subsequent sections since it may provide unique insight into LsPD pathophysiology. | PMC10216182 |
3.2. Quantitative Results | Standardized scales assessing motor function, alertness, cognition, and sleep did not detect a clear pattern of differences between levodopa and PF-2562 ( | PMC10216182 | ||
3.3. Qualitative Caregiver Interview | napping, restlessness | SAID | Blinded analysis of the transcripts revealed significant variability in patients’ baseline functional status (Qualitative analyses also suggested PF-2562 may improve facial expression and sleep to varying degrees, although analysis of sleep was challenging due to highly variable caregiver descriptions (e.g., the judgment of sleep quality based on different aspects such as breathing, apneas, duration, depth of napping, restlessness, vocalizations). All caregivers commented that some environmental factors may have impacted results. For example, caregiver-1 said: “Subject 8 responded dramatically to levodopa but not PF-2562 (see | PMC10216182 |
3.4. Mixed Methods Results | Integration of quantitative and qualitative data suggested a convergent finding that caregivers favored PF-2562 in four of five patients who completed the study (Caregivers were consistent in their quantitative observations, whereas clinician impressions displayed substantial variability and diverged from caregiver impressions in two of five patients. The rater-dependent standard metrics detected no differences and were not contributory to the overall results. | PMC10216182 | ||
4. Discussion | LsPD patients have many unmet needs, and supportive and palliative care has increasingly been recognized as the best options, e.g., reviews [ | PMC10216182 | ||
4.2. Unresolved Mechanisms in These Findings | As noted earlier, in NHP models [The compound we used, PF-2562, is one of a series of non-catechol DIntriguingly, dihydrexidine, like its 2-methyl analog [The two most obvious are what are the optimal ligand properties in terms of intrinsic activity at canonical and non-canonical pathways to provide the highest therapeutic index. For example, it has been argued that the lack of β-arrestin activity will decrease desensitization due to chronic administration, yet it is also possible the receptor occupation needed to obtain antiparkinson effects will be too low in vivo to trigger these mechanisms. In that case, the non-canonical signaling of β-arrestin (or other unstudied pathways) may be very important.The availability of newer generation D | PMC10216182 | ||
5. Conclusions and Future Directions | PD | EVENT, HOLIDAY | The current authors [As a first-of-its-kind, the current study is limited by its relatively small sample size. As experience with DIt is also important to address the one subject who had a profound positive response to levodopa. Subject 8 previously had been essentially unresponsive to all treatment for years, suggesting it was not a random event. The dramatic improvement during the levodopa week might represent a re-sensitization to levodopa after a three-year “drug holiday”, but this seems unlikely since there was no effect when the family resumed levodopa. Although highly speculative, another hypothesis is that the two-day PF-2562 period “primed” dopamine circuitry (e.g., by improving sleep structure) to respond more normally to even small amounts of dopamine from a levodopa challenge six days later. Coupled with the very consistent beneficial responses of the other four patients, the hypothesis is a high priority for further testing, as there will be a growing number of LsPD patients with better palliative care strategies, which may increase the life-span, but not the health span of PD patients.We have noted the limitations of this study above, but it is important to put them in context. There was no prior experience in the literature for interventional studies in LsPD. Thus, necessarily, the design [ | PMC10216182 |
Author Contributions | The trial design was initiated by the authors from Penn State and finalized in collaboration with the Pfizer authors, as listed below. The contribution of each author is assigned by the following scale: Research project: 1A—Conception, 1B—Organization, 1C—Execution; Data and Statistical analysis: 2A:—Design, 2B—Execution, 2C—Review and critique; and Manuscript preparation: 3A—Writing the first draft, 3B Review and critique. The specific contributions were: M.M.L.: 1A, 1B, 1C, 2A, 2C, 3A, 3B; L.J.V.S.: 1A, 1B, 1C, 2A, 2B, 2C, 3A, 3B; S.D.J.: 1C, 2C, 3B; J.G.H.: 1B, 1C, 3B; P.J.E.: 1B, 1C; 3B; J.F.-M.: 1B; 3B; L.K.: 2A, 2B, 2C, 3B; Y.Y.: 2C, 3B; B.L.S.: 1B, 1C, 2B, 2C, 3B; N.L.: 1C; S.D.: 2A, 2C; D.L.G.: 1A, 2A, 2C, 3B; X.H.: 1A, 1B, 1C, 2A, 2C, 3A, 3B; R.B.M.: 1A, 1B, 2C, 3A, 3B. All authors have read and agreed to the published version of the manuscript. | PMC10216182 | ||
Institutional Review Board Statement | PSH | SECONDARY, DISEASE | This study was conducted at PennStateHealth (PSH) in compliance with the Declaration of Helsinki and guidelines for Good Clinical Practice issued by the International Conference on Harmonization. The protocol was reviewed and approved by the Institutional Review Board of the Penn State College of Medicine (protocol code 9437, approved 07/16/2018). The study’s IND was approved by the US Food and Drug Administration and registered in clinicaltrials.gov (PF 06412562 in Subjects With Advanced Stage Parkinson’s Disease), where the primary and secondary outcomes noted above were listed. | PMC10216182 |
Informed Consent Statement | Informed consent was obtained from all subjects involved in the study. Written informed consent also was obtained from participants to publish potential results from the research study. | PMC10216182 | ||
Data Availability Statement | Additional details are available in supplemental data at | PMC10216182 | ||
Conflicts of Interest | Gray and Duvvuri were employees and shareholders of Pfizer, Inc. at the time of study design and initiation. Huang and Mailman have a potential conflict of interest (COI) due to existing patents related due to the discovery or use of D | PMC10216182 | ||
Appendix A | PMC10216182 | |||
Appendix A.2. Details of Qualitative Analysis | As noted in the text, a conventional content analysis approach that included data transformation was used to evaluate the data [ | PMC10216182 | ||
Appendix A.3. Importance of Caregiver Perspectives | Most clinical trials rely upon informed clinician judgment based on validated instruments and (when available) imaging/molecular/biochemical markers, but no validated standard scales exist for LsPD [Caregiver observations also were more consistent and less variable than experienced physicians. This is not surprising since caregivers were intimately familiar with nuanced baseline patient behaviors, were able to provide insight and context for typical/atypical behavioral observations, and were with participants 24/7 during the study. It also is noteworthy that blinded caregivers consistently identified the levodopa week as not being remarkably different from home. This gives credence to the caregivers’ observations and objectivity. | PMC10216182 | ||
1. Introduction | low back pain, pain, Low back pain, LBP, lumbar dysfunction | BURNETT | Low back pain (LBP) is known to affect cyclists. This study aimed to describe perceived lumbar dysfunction and compare the pain sensation in recreational cyclists who practice road and mountain biking. Forty males were randomly assigned to carry out a 3-h road cycling (RC) and mountain biking (MTB) time trial (TT) at submaximal intensity. LBP and pain pressure threshold (PPT) were measured before and after the TT. A significant increment at the LBP was found after RC TT (Cycling is a very practiced sport at a competitive and recreational level [Its practice is associated with the appearance of overloads and sports injuries, and perceived discomfort and pain [Several risk factors are related to low back pain in cyclists, including muscle activation asymmetries. For example, Burnett et al., 2004 [Previously published works have evaluated LBP after cycling, but they refer only to road cyclists [To date, comparative studies on the prevalence of LBP among MTB and RC are yet to be found, as well as their possible interaction. Thus, understanding these potential differences may benefit the cycling communities to aid in identifying prevention programming to reduce its prevalence as well as the incidence of injury and care to this population. Therefore, this work aims to describe perceived lumbar dysfunction and compare the pain sensation in recreational cyclists who practice road and mountain biking. | PMC10001301 |
2. Materials and Methods | PMC10001301 | |||
2.1. Participants | PATHOLOGY | Eligible participants were aged from 18 to 55 years, amateur males, from mixed cycling modalities, with experience in the practice of cycling greater than three years, not having received specific treatment in the musculature evaluated during the last four weeks, or subject to some treatment in the present, as well as not having pathology diagnosed in the lumbar region. Participants were ineligible if they had undertaken strenuous exercise in the previous 48 h, had taken analgesics before the data collection, did not complete the bike ride due to loss in the course, or had a mechanical breakdown that made continuity not possible. Participants were blinded to the research hypothesis. In addition, the research analyst established the randomization and had no direct contact with the participants. | PMC10001301 | |
2.2. Design | The study is a crossover randomized controlled trial, conducted according to the Declaration of Helsinki, and was approved by the local University Ethics Committee (2016/UEM18). All the participants were volunteers, informed about the study protocol, and provided written consent before the measurements. Furthermore, in agreement with the latest version of the Declaration of Helsinki, the study was registered at | PMC10001301 | ||
2.3. Methodology | low back pain, functional disability, pain, FD, LBP | SECONDARY | The sample size was calculated based on a pilot study and the 2 points of clinical relevance established by Ostelo et al. [For recruiting, a non-probabilistic sampling of chain or network selection (“snowball”) was used, through which key participants were identified and added to the sample. They were asked if they knew other people who can provide more extensive data, and once contacted, they were also included [To ensure that exercise intensity was comparable between both situations, the intensity along the TT was established between 60 and 82% of the maximal heart rate (HR), corresponding to the cardiovascular zones 1, 2 and 3 of a total of 5, which are the predominant zones for cyclists in prolonged submaximal efforts, equivalent to an effort of light to moderate intensity [The prespecified primary outcome measure was low back pain perception (LBPP) using a 0 to 10 numeric pain rating scale (NPRS). This is an 11-point scale ranging from 0 (no pain) to 10 (worst imaginable pain) that has been demonstrated to be valid, reliable, and appropriate for use in clinical practice and also with cyclists [Prespecified secondary outcome measures were the pain pressure threshold (PPT) and functional disability (FD) due to LBP measured by the Roland–Morris questionnaire (RMQ) and RPE. PPT was measured from 0 to 10 kg/cmFD was measured only once, at the end of the first assessment day, by using the Spanish version of RMQ [ | PMC10001301 |
2.4. Statistical Analysis | ’s Q | SECONDARY | Statistical testing was conducted using statistical software SPSS v.21 (IBM Corp., Armonk, NY, USA). First, descriptive statistics composed median and interquartile ranges to report quantitative variables and frequency and percentage to describe the qualitative variables. Second, Shapiro–Wilks test was performed to determine normal distribution. Third, Cochran’s Q (Q) was used to analyze differences in the prevalence of FD evaluated from the RMQ. Finally, a non-parametric Wilcoxon signed-rank test was used to determine differences between measurement moments (pre-intervention and post-treatment) and within, between, and within–between groups on the primary and secondary outcomes. Significance level was set at | PMC10001301 |
3. Results | In total, 70 participants were screened for study enrollment (Forty ( | PMC10001301 | ||
3.1. Baseline Data | The participants completed the RC TT in 201 ± 17.6 min, with an average speed of 28 ± 2.2 km/h. The duration of the MTB TT was 182 ± 23.8 min at 18.9 ± 2.3 km/h. The average intensity as a percentage of the estimated MHR was 69.8 ± 5.48 % and 68.1 ± 8%, respectively, and the global RPE was 13.9 ± 1.5 and 13.2 ± 1.6, respectively.Considering the crossover design, the absence of differences between the baseline results of LBBP and PPT was checked to ensure a sufficient washout effect. No significant differences were observed between interventions; neither in LBBP (median 1 = 0 (P | PMC10001301 | ||
3.2. Functional Disability | Concerning FD, 22 (55%) participants showed a score of 0; 9 (23%) participants, 1; 3 (8%) participants, 2; 2 (5%) participants, 3; 1 (3%) participant, 5; and 3 (8%) participants, 6. Differences in the answers were found between the items evaluated (Q (23) = 104.3, | PMC10001301 | ||
3.3. Low Back Pain Perception | There were no found differences in LBBP between conditions at the beginning (Z = −1.12; | PMC10001301 | ||
3.4. Pain Pressure Threshold | There were no differences found in PPT between conditions at the beginning (Z = −1.31; | PMC10001301 | ||
4. Discussion | LBP, pain | This study aimed to describe and analyze the evolution of LBPP in recreational cyclists who practice road and mountain biking. Increased detected LBBP was greater than 2 points, which is considered clinically relevant [Regarding PPT, participants reported a decrease after completing the TT, but this change was statistically significant only for the RC condition. Although the difference in MTB was not statistically significant, the trend is like RC, which, besides the ES found, indicates a change in the response pattern. Since cyclists seem to be acclimatized to some degree of discomfort even in healthy conditions, so they continue to participate regardless of pain [The present study also showed how six participants presented three or more FDs. According to Monticone et al. (2012) [We have yet to find published comparative studies on the evolution of LBPP among mountain and road cyclists. Therefore, we cannot compare our results with those of other authors. Nevertheless, the hypothesis of this study expected to find differences in the change produced on LBP between the practice of RC and MTB, since both modalities present differences in posture [Moreover, Dahlquist et al., 2015 [Another hypothesis that can be explored in the future is the role of the myofascial system. It interpenetrates and surrounds all organs, muscles, bones, and nerve fibers, creating a unique environment for body systems’ functioning [Some limitations should be considered in the present study. First, to evaluate the differences between modalities, there were no specific road and mountain cyclists, which does not allow us to assess whether the cyclists’ muscles have different adaptations typical of each modality. Second, to evaluate the PPT, it was necessary to calculate the average of the results observed on the left and right sides. This could mask possible differences between the evolution of the left and right sides. As regards strengths, this is the first study that compares the development of LBP among mountain and road cyclists. As an intra-subject study, it allows the outcomes (LBP and the PPT) to be more stable. Both interventions were spaced a week apart to eliminate the influence of the previous intervention. Our data indicated no carryover effect between the baseline results of LBBP and PPT.Moreover, the ecological design of our research, which used real situations, such as own bicycles with own bike fitting of the participants and the interventions being carried out outdoors, led to a greater external validity. Finally, the intensity training session was adjusted through an individualized HR range, and each participant completed the TT alone to avoid the effect of restricting effort due to the shielding effect of any other front cyclist. Another adopted procedure to ensure the control of interventions was the recordings of RPE. Our results indicated no differences among interventions, so we can assume that intensity was similar in both interventions. | PMC10001301 | |
5. Conclusions | low back pain, lumbar dysfunction | Our results suggest that recreational cyclists present some degree of lumbar dysfunction, which could indicate injury risk. Furthermore, our findings highlight that low back pain perception increases with cycling in recreational cyclists. Nevertheless, this increase is independent of the modality of cycling practiced and appears to be more related to the traits of the cyclist (intrinsic factor) than the modality practiced (extrinsic factor). Furthermore, these findings may contribute to identifying prevention programming to reduce lumbar dysfunction prevalence and the incidence of injury to this population. Again, such information may help, as encouraging physical activity through cycling is promoted. | PMC10001301 | |
Author Contributions | Conceptualization, G.G.-M. and I.D.-V.; methodology, G.G.-M. and I.D.-V.; software, I.D.-V.; validation, C.A.M. and I.D.-V.; formal analysis, I.D.-V.; investigation, G.G.-M., I.D.-V., C.A.M. and J.J.M.-M.; resources, C.A.M. and J.J.M.-M.; data curation, I.D.-V.; writing—original draft preparation, G.G.-M. and I.D.-V.; writing—review and editing, G.G.-M., I.D.-V. and C.A.M.; visualization, G.G.-M.; supervision, I.D.-V. and C.A.M.; project administration, J.J.M.-M. and I.D.-V. All authors have read and agreed to the published version of the manuscript. | PMC10001301 | ||
Institutional Review Board Statement | The study was conducted following the Declaration of Helsinki, was registered at | PMC10001301 | ||
Informed Consent Statement | Informed consent was obtained from all subjects involved in the study to publish this paper. | PMC10001301 | ||
Data Availability Statement | The raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request. | PMC10001301 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC10001301 | ||
References | Pain, Low back pain | Participant’s flow chart of the study design.Low back pain perception.Pain Pressure Threshold.Frequency and percentage of the affirmative responses to each item of the Roland–Morris questionnaire.Note: n YES = frequency of the affirmative responses to each item of the Roland–Morris questionnaire, % YES = percentage of the affirmative responses to each item of the Roland–Morris questionnaire.Descriptive results of Low Back Pain Perception.Results expressed median (interquartile range).Descriptive results of Pain Pressure Threshold.Results expressed median (interquartile range). | PMC10001301 | |
1. Introduction | DCM, coronary artery diseases, systolic dysfunction, cardiac disorders | DILATED CARDIOMYOPATHY, LEFT VENTRICULAR DILATATION, CARDIAC DISORDERS, CORONARY ARTERY DISEASES, REGRESSION, HEART FAILURE, SYSTOLIC DYSFUNCTION | Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Therefore, screening and early diagnosis of potential DCM patients is beneficial. Electrocardiogram (ECG) can be an inexpensive and easily available screening tool. We aimed to construct a simple screening model for DCM based on electrocardiogram. In this retrospective observational study, we consecutively enrolled 117 DCM patients between July 1, 2016 and July 1, 2021 as the DCM group, while 117 patients hospitalized in the same period with normal echocardiography and ECG were selected as the non-DCM group. Patients were randomly assigned to the training and validation sets at 8:2. ECG parameters of left ventricular related leads were exacted. Logistic regression was performed to evaluate screening indicators of ECG parameters and a nomogram was conducted. The screening ability of the model was evaluated using receiver operating characteristic analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis. Screening indicators included in the nomogram were the amplitude of S wave in V1 and V3 leads, the amplitude of R wave in aVF and V6 leads, and PR interval. The nomogram performed satisfactory discrimination in the training (area under the receiver operating characteristic curve = 0.904) and validation (area under the receiver operating characteristic curve = 0.878) sets and good calibration (Hosmer–Lemeshow Dilated cardiomyopathy (DCM) is a group of cardiac disorders characterized by left ventricular dilatation and systolic dysfunction without severe sufficiently coronary artery diseases or abnormal load conditions, which is one of the most common causes of heart failure.With the rapidly developing cardiovascular imaging technologies, particularly the widespread use of cardiovascular magnetic resonance, we can identify tissue characterization and deepen the phenotype of DCM. | PMC9907988 |
2. Methods | PMC9907988 | |||
2.1. Study population | DCM | Between July 1, 2016 and July 1, 2021, we retrospectively consecutively enrolled 117 DCM patients admitted to the first Hospital of Jilin University as the DCM group, while 117 patients hospitalized in the same period with normal echocardiography and ECG were selected as the non-DCM group. The diagnosis of DCM was based on symptoms, laboratory, and echocardiography. The inclusion criteria for the DCM group were as follows: age ≥ 18 years; Patients underwent 12-lead ECG and echocardiography and the relevant results were stored completely after admission; ECGs were in sinus rhythm; Patients were diagnosed as DCM by echocardiography following the diagnosed criteria that echocardiography showed left ventricular end-diastolic diameter (LVEDD) > 5.0 cm (female) or 5.5cm (male) and left ventricular ejection fraction (LVEF) < 45% according to the guideline protocol of diagnosis of DCM from the British Society of Echocardiography. | PMC9907988 | |
2.2. Baseline characteristics and ECG data collection | Baseline characteristics included age, gender, body mass index, brain natriuretic peptide level, echocardiographic data, New York heart association class, patterns of ECG, and symptoms and medications. ECG recorded with paper speed = 25 mm/s and calibration = 10 mm/mV. GE Muse ECG data management system (GE Healthcare, Chicago, IL) was used to measure 12-lead ECG parameters. QRS complex parameters were measured using amplitude-based methods. | PMC9907988 | ||
2.3. Statistical analysis | non-DCM | REGRESSION | Statistical analyses were performed with IBM SPSS version 26.0 (SPSS Inc., Chicago, IL) and R version 4.1.3. The patients were randomly divided into a training dataset (80%) and a validation dataset (20%) using the “caret” package in R 4.1.3. The training dataset was used to construct the nomogram and the validation dataset was used for external validation. Continuous variables were reported as means ± standard deviations (SDs) or medians (minimum value, maximum value) depending on the data distribution. Categorical variables were reported as frequencies (proportions). Differences between DCM and non-DCM groups were tested by independent sample t-test or Mann–Whitney–Wilcoxon test for continuous variables and chi-square test or Fisher’s exact test for categorical variables. Univariate analyses of ECG variables were performed to identify potential indicators screening DCM. Forward stepwise logistic regression was performed to determine the indicators. Multivariate binary logistic regression analysis with indicators was conducted by the “glm2” package and used to construct the final screening model, which was performed as a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate discrimination and screening ability of the model. The optimal cutoff value was determined using Youden index. Hosmer-Lemeshow test was conducted by the “generalhoslem” package. The decision curve analysis was conducted by the “ggDCA” package and applied to evaluate the clinical effects under the optimal cutoff value of the validation dataset. A 2-sided | PMC9907988 |
3. Results | PMC9907988 | |||
3.1. Baseline characteristics and univariate analyses of ECG parameters | DCM, non-DCM | ATRIOVENTRICULAR BLOCK, LEFT BRANCH BUNDLE BLOCK, LVH, RIGHT BRANCH BUNDLE BLOCK, RBBB, LBBB, DILATED CARDIOMYOPATHY, LEFT VENTRICULAR HYPERTROPHY | A total of 234 patients including 117 DCM patients and 117 non-DCM patients were enrolled in our study. The baseline characteristics are shown in Table Baseline characteristics.ACEI = angiotensin-converting enzyme inhibitors, ARB = angiotensin II receptor blocker, ARNI = angiotensin receptor-neprilysin inhibitor, AVB = atrioventricular block, BMI = body mass index, BNP = brain natriuretic peptide, DCM = dilated cardiomyopathy, ECG = electrocardiogram, LAD = left atrial diameter, LBBB = left bundle branch block, LVEDD = left ventricular end-diastolic diameter, LVEF = left ventricular ejection fraction, LVH = left ventricular hypertrophy, MRA = mineralocorticoid receptor antagonist, NYHA = New York heart association, RBBB = right bundle branch block.The training dataset included 94 patients in the DCM group and 97 patients in the non-DCM group. All parameters automatically output by ECG data management system were checked manually and confirmed without error. Univariate analyses identified a number of indicators, including PR interval (173ms vs 156ms, The validation dataset included 23 patients in the DCM group and 20 patients in the non-DCM group. Univariate analyses identified a number of indicators, including QRS axis (−3.0°vs 49.5°, Electrocardiogram parameters and univariate analyses.DCM = dilated cardiomyopathy, QTc = corrected QT interval according to heart rate, RA = the amplitude of R wave, SA = the amplitude of S wave. | PMC9907988 |
3.2. Multivariate binary logistic regression and nomogram construction | RA, dyspnea | REGRESSION, LBBB, DILATED CARDIOMYOPATHY | ECG parameters in the training dataset which were found statistically significant in univariate analyses were included in multivariate binary logistic regression using forward LN stepwise regression. After multivariate analysis, V1SA (odds ratio [OR]: 4.145, 95% confidence interval [CI] [2.286, 7.516], Independent screening indicators for DCM in the multivariate logistic analysis.CI = confidence interval, DCM = dilated cardiomyopathy, RA = the amplitude of R wave, SA = amplitude of S wave.The nomogram of the screening model.The use of nomogram consists of 3 simple steps. First, the point value of each variable is read on the point scale. Next, add up all the points obtained in the first step as the total point. Finally, the probability of DCM corresponding to the total point of the specific patient is read on the predicted value scale. For example, a patient with dyspnea was admitted to the cardiovascular department of our hospital, and the ECG was examined (paper speed = 25 mm/s and calibration = 10 mm/mV) immediately after admission and showed LBBB, as shown in Figure Admission ECG of a patient with dyspnea. ECG = electrocardiogram. | PMC9907988 |
3.3. Model validation | Figure The ROC curve of the training dataset. ROC = receiver operating characteristic.The ROC curve of the validation dataset. ROC = receiver operating characteristic.Calibration curve of the training dataset.Calibration curve of the validation dataset.Decision-curve analysis of the validation dataset. | PMC9907988 | ||
4. Discussion | sudden cardiac death, anterolateral T-wave, first-degree | SUDDEN CARDIAC DEATH, CONGESTIVE HEART FAILURE, CARDIOVASCULAR DISEASE, CARDIOMYOPATHY | Our study constructed a simple screening model for DCM entirely was based on ECG parameters and identified V1SA, V3SA, aVFRA, V6RA, and PR interval as independent screening indicators. ECG, as the first step of the diagnosis of cardiovascular disease, still plays a powerful role in the evaluation of DCM patients. It’s estimated that 80% of patients with DCM have an abnormal ECG.Historically, many electro cardiologists and clinicians have continuously explored the relationship between ECG and cardiomyopathy. As early as 40 years ago, Goldberger proposed to predict ECG triad in patients with congestive heart failure, including high precordial QRS wave voltage, relatively low limb lead voltage, and poor precordial R wave progression.Our model is applicable to a wide range of potential DCM patients, especially for first-degree relatives of DCM patients. Familial DCM accounts for approximately 20% - 50% of DCM,In recent years, the analysis of predictors of ECG for the diagnosis and risk assessment of DCM has been also emerging in endlessly. For example, anterolateral lead T-wave inversion may be a predictor of heart transplantation in patients with DCM, and V2 lead S-wave, III lead R-wave, and anterolateral T-wave inversion may be a predictor of sudden cardiac death in patients with DCM. | PMC9907988 |
4.1. Limitations | heart failure, supraventricular or ventricular arrhythmias | HEART FAILURE, COLLAPSE | There are some limitations in our study. First, our study is a retrospective single-center study. The conclusions of our study, especially the cutoff value of the index, need to be further verified in larger datasets. Secondly, the DCM patients we included were mainly hospitalized for collapse of heart failure. This group may have limited benefit and cannot represent all the DCM patients. Thirdly, the patients included in our study were in sinus rhythm, thus the conclusions cannot apply to DCM patients with supraventricular or ventricular arrhythmias. Finally, it is not sufficient to exclude the possibility of DCM only based on our algorithm in clinical, yet further need to investigate other etiology of heart failure practically. | PMC9907988 |
5. Conclusion | Our study developed a simple screening model for DCM patients only based on ECG parameters which demonstrated satisfactory performance, and our model is warranted to be validated in more diverse populations. | PMC9907988 | ||
Abbreviations: | Zhang Q | DILATED CARDIOMYOPATHY | area under the receiver operating characteristic curveconfidence intervaldilated cardiomyopathyelectrocardiogramleft bundle branch blockleft ventricularleft ventricular end-diastolic diameterleft ventricular ejection fractionleft ventricular hypertrophyodds ratiocorrected QT interval according to heart ratethe amplitude of the S waveThe datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.The authors have no conflicts of interest to disclose.How to cite this article: Wang X, Zhang Q, Yang N, Wang X, Zhang Z. Simple screening model based on electrocardiogram for patients with dilated cardiomyopathy. | PMC9907988 |
References | PMC9907988 | |||
Background | migraine | MIGRAINE | We aimed to explore whether erenumab, a monoclonal antibody targeting the calcitonin gene-related peptide receptor, could exert a central effect on brain network function in migraine, and investigate the persistence of such an effect following treatment discontinuation. | PMC10576673 |
Methods | migraine, RS | MIGRAINE | This was a randomized, double-blind, placebo-controlled, multicenter trial with a crossover design performed in adult episodic migraine patients with previous treatment failure. Patients were randomized (1:1) to 12 weeks of erenumab 140 mg or placebo, followed by a 12-week crossover. Resting state (RS) functional connectivity (FC) changes of brain networks involved in migraine were investigated using a seed-based correlation approach. | PMC10576673 |
Results | migraine, RS | MIGRAINE, BRAIN | Sixty-one patients were randomized to treatment. In each treatment sequence, 27 patients completed the visit at week 12. Forty-four enrolled patients, 22 in each treatment sequence, completed the study procedures with no major protocol violations. We observed a carry-over effect of erenumab during the placebo treatment and therefore data analysis was performed as a parallel comparison of erenumab vs placebo of the first 12 weeks of treatment. From baseline to week 12, compared to placebo, patients receiving erenumab showed RS FC changes within the cerebellar, thalamic and periaqueductal gray matter networks, significantly associated with clinical improvement. Compared to non-responders, patients achieving a 50% reduction in migraine days had distinct patterns of thalamic and visual network RS FC. Brain RS FC changes reversed when erenumab was stopped. A lower baseline RS FC of the pontine network identified patients responding to erenumab | PMC10576673 |
Conclusion | migraine, RS | MIGRAINE, PATHOPHYSIOLOGY | Erenumab modulates RS FC of networks involved in migraine pathophysiology. In line with clinical response, erenumab-induced brain RS FC changes tend to reverse when treatment is stopped. | PMC10576673 |
Supplementary Information | The online version contains supplementary material available at 10.1007/s00415-023-11879-9. | PMC10576673 | ||
Keywords | PMC10576673 |
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