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Untracht GR, Dikaios N, Durrani AK, Bapir M, Sarunic MV, Sampson DD, Heiss C, Sampson DM. Pilot study of optical coherence tomography angiography-derived microvascular metrics in hands and feet of healthy and diabetic people. Sci Rep 2023; 13:1122. [PMID: 36670141 PMCID: PMC9853488 DOI: 10.1038/s41598-022-26871-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/21/2022] [Indexed: 01/22/2023] Open
Abstract
Optical coherence tomography angiography (OCTA) is a non-invasive, high-resolution imaging modality with growing application in dermatology and microvascular assessment. Accepted reference values for OCTA-derived microvascular parameters in skin do not yet exist but need to be established to drive OCTA into the clinic. In this pilot study, we assess a range of OCTA microvascular metrics at rest and after post-occlusive reactive hyperaemia (PORH) in the hands and feet of 52 healthy people and 11 people with well-controlled type 2 diabetes mellitus (T2DM). We calculate each metric, measure test-retest repeatability, and evaluate correlation with demographic risk factors. Our study delivers extremity-specific, age-dependent reference values and coefficients of repeatability of nine microvascular metrics at baseline and at the maximum of PORH. Significant differences are not seen for age-dependent microvascular metrics in hand, but they are present for several metrics in the foot. Significant differences are observed between hand and foot, both at baseline and maximum PORH, for most of the microvascular metrics with generally higher values in the hand. Despite a large variability over a range of individuals, as is expected based on heterogeneous ageing phenotypes of the population, the test-retest repeatability is 3.5% to 18% of the mean value for all metrics, which highlights the opportunities for OCTA-based studies in larger cohorts, for longitudinal monitoring, and for assessing the efficacy of interventions. Additionally, branchpoint density in the hand and foot and changes in vessel diameter in response to PORH stood out as good discriminators between healthy and T2DM groups, which indicates their potential value as biomarkers. This study, building on our previous work, represents a further step towards standardised OCTA in clinical practice and research.
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Affiliation(s)
- Gavrielle R Untracht
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, 6009, Australia.,School of Biosciences and Medicine, The University of Surrey, Guildford, GU27XH, UK
| | - Nikolaos Dikaios
- Mathematics Research Centre, Academy of Athens, Athens, 10679, Greece
| | - Abdullah K Durrani
- School of Biosciences and Medicine, The University of Surrey, Guildford, GU27XH, UK.,School of Physics, Advanced Technology Institute, The University of Surrey, Guildford, GU27XH, UK
| | - Mariam Bapir
- School of Biosciences and Medicine, The University of Surrey, Guildford, GU27XH, UK
| | - Marinko V Sarunic
- Institute of Ophthalmology, University College London, London, EC1V 2PD, UK.,Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - David D Sampson
- School of Biosciences and Medicine, The University of Surrey, Guildford, GU27XH, UK.,School of Physics, Advanced Technology Institute, The University of Surrey, Guildford, GU27XH, UK
| | - Christian Heiss
- School of Biosciences and Medicine, The University of Surrey, Guildford, GU27XH, UK.,East Surrey Hospital, Surrey and Sussex Healthcare NHS Trust, Redhill, RH15RH, UK
| | - Danuta M Sampson
- School of Biosciences and Medicine, The University of Surrey, Guildford, GU27XH, UK. .,Institute of Ophthalmology, University College London, London, EC1V 2PD, UK.
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Liu Z, Zhang L, Wu J, Zheng Z, Gao J, Lin Y, Liu Y, Xu H, Zhou Y. Machine learning-based classification of circadian rhythm characteristics for mild cognitive impairment in the elderly. Front Public Health 2022; 10:1036886. [PMID: 36388285 PMCID: PMC9650188 DOI: 10.3389/fpubh.2022.1036886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/10/2022] [Indexed: 01/29/2023] Open
Abstract
Introduction Using wrist-wearable sensors to ecological transient assessment may provide a more valid assessment of physical activity, sedentary time, sleep and circadian rhythm than self-reported questionnaires, but has not been used widely to study the association with mild cognitive impairment and their characteristics. Methods 31 normal cognitive ability participants and 68 MCI participants were monitored with tri-axial accelerometer and nocturnal photo volumetric pulse wave signals for 14 days. Two machine learning algorithms: gradient boosting decision tree and eXtreme gradient boosting were constructed using data on daytime physical activity, sedentary time and nighttime physiological functions, including heart rate, heart rate variability, respiratory rate and oxygen saturation, combined with subjective scale features. The accuracy, precision, recall, F1 value, and AUC of the different models are compared, and the training and model effectiveness are validated by the subject-based leave-one-out method. Results The low physical activity state was higher in the MCI group than in the cognitively normal group between 8:00 and 11:00 (P < 0.05), the daily rhythm trend of the high physical activity state was generally lower in the MCI group than in the cognitively normal group (P < 0.05). The peak rhythms in the sedentary state appeared at 12:00-15:00 and 20:00. The peak rhythms of rMSSD, HRV high frequency output power, and HRV low frequency output power in the 6h HRV parameters at night in the MCI group disappeared at 3:00 a.m., and the amplitude of fluctuations decreased; the amplitude of fluctuations of LHratio nocturnal rhythm increased and the phase was disturbed; the oxygen saturation was between 90 and 95% and less than 90% were increased in all time periods (P < 0.05). The F1 value of the two machine learning algorithms for MCI classification of multi-feature data combined with subjective scales were XGBoost (78.02) and GBDT (84.04). Conclusion By collecting PSQI Scale data combined with circadian rhythm characteristics monitored by wrist-wearable sensors, we are able to construct XGBoost and GBDT machine learning models with good discrimination, thus providing an early warning solution for identifying family and community members with high risk of MCI.
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Affiliation(s)
- Zhizhen Liu
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, China,Zhizhen Liu
| | - Lin Zhang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jingsong Wu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Zhicheng Zheng
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Jiahui Gao
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Yongsheng Lin
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yinghua Liu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Haihua Xu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yongjin Zhou
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China,Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China,*Correspondence: Yongjin Zhou
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