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Fulltext Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023)

Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, et al.

Comput Biol Med, 2024 Apr;172:108207.
PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
Fulltext Gut Microbiome Associated With Graves Disease and Graves Orbitopathy: The INDIGO Multicenter European Study

Biscarini F, Masetti G, Muller I, Verhasselt HL, Covelli D, Colucci G, et al.

J Clin Endocrinol Metab, 2023 Jul 14;108(8):2065-2077.
PMID: 36683389 DOI: 10.1210/clinem/dgad030

CONTEXT: Gut bacteria can influence host immune responses but little is known about their role in tolerance-loss mechanisms in Graves disease (GD; hyperthyroidism caused by autoantibodies, TRAb, to the thyrotropin receptor, TSHR) and its progression to Graves orbitopathy (GO).
OBJECTIVE: This work aimed to compare the fecal microbiota in GD patients, with GO of varying severity, and healthy controls (HCs).
METHODS: Patients were recruited from 4 European countries (105 GD patients, 41 HCs) for an observational study with cross-sectional and longitudinal components.
RESULTS: At recruitment, when patients were hyperthyroid and TRAb positive, Actinobacteria were significantly increased and Bacteroidetes significantly decreased in GD/GO compared with HCs. The Firmicutes to Bacteroidetes (F:B) ratio was significantly higher in GD/GO than in HCs. Differential abundance of 15 genera was observed in patients, being most skewed in mild GO. Bacteroides displayed positive and negative correlations with TSH and free thyroxine, respectively, and was also significantly associated with smoking in GO; smoking is a risk factor for GO but not GD. Longitudinal analyses revealed that the presence of certain bacteria (Clostridiales) at diagnosis correlated with the persistence of TRAb more than 200 days after commencing antithyroid drug treatment.
CONCLUSION: The increased F:B ratio observed in GD/GO mirrors our finding in a murine model comparing TSHR-immunized with control mice. We defined a microbiome signature and identified changes associated with autoimmunity as distinct from those due to hyperthyroidism. Persistence of TRAb is predictive of relapse; identification of these patients at diagnosis, via their microbiome, could improve management with potential to eradicate Clostridiales.

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