The skin has a multifactorial aging process, caused by both intrinsic and extrinsic factors. A major theory of aging involves cellular senescence or apoptosis resulting from oxidative damage as the skin’s antioxidant system tends to weaken with age. The human microbiota is a complex ecosystem that is made up of microorganisms (bacteria, fungi, and viruses). Both gut and skin microbiota have essential roles in the protection against invading pathogens, mediating inflammatory conditions, and the modulation of the immune system which is involved in both innate and adaptive immune responses. However, the human microbiome could be changed during the life stage and affected by various perturbations. An alteration of the intestinal bacteria results in “microbial dysbiosis” which is associated with the influence of various diseases, including aging. The skin interactome is a novel integration of the “genome-microbiome-exposome” that plays a significant role in skin aging and skin health. Mitigating the negative impacts of factors influencing the skin interactome should be the future strategy to protect, prevent, and delay skin aging along with preserving healthy skin conditions. This review summarizes the current evidence on how human microbiomes affect skin aging and demonstrates the possible interventions, relating to human microbiomes, to modulate skin health and aging. Probiotics-based products are currently available mainly for the add-on treatment of many dermatologic conditions. However, at this point, there are limited clinical studies on skin anti-aging purposes and more are required as this evolving concept is on the rise and might provide an insight into future therapeutic options.

The Thai Osteoporosis Foundation (TOPF) is an academic organization that consists of a multidisciplinary group of healthcare professionals managing osteoporosis. The first clinical practice guideline for diagnosing and managing osteoporosis in Thailand was published by the TOPF in 2010, then updated in 2016 and 2021. This paper presents important updates of the guideline for the diagnosis and management of osteoporosis in Thailand. A panel of experts in the field of osteoporosis was recruited by the TOPF to review and update the TOPF position statement from 2016. Evidence was searched using the MEDLINE database through PubMed. Primary writers submitted their first drafts, which were reviewed, discussed, and integrated into the final document. Recommendations are based on reviews of the clinical evidence and experts' opinions. The recommendations are classified using the Grading of Recommendations, Assessment, Development, and Evaluation classification system. The updated guideline comprises 90 recommendations divided into 12 main topics. This paper summarizes the recommendations focused on 4 main topics: the diagnosis and evaluation of osteoporosis, fracture risk assessment and indications for bone mineral density measurement, fracture risk categorization, management according to fracture risk, and pharmacological management of osteoporosis. This updated clinical practice guideline is a practical tool to assist healthcare professionals in diagnosing, evaluating, and managing osteoporosis in Thailand.

The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). We evaluated the performance of a machine learning-based predictive model using existing periprocedural variables for valve replacement modality selection. We analyzed 415 patients in a retrospective longitudinal cohort of adult patients undergoing aortic valve replacement for aortic stenosis. A total of 72 clinical variables including demographic data, patient comorbidities, and preoperative investigation characteristics were collected on each patient. We fit models using LASSO (least absolute shrinkage and selection operator) and decision tree techniques. The accuracy of the prediction on confusion matrix was used to assess model performance. The most predictive independent variable for valve selection by LASSO regression was frailty score. Variables that predict SAVR consisted of low frailty score (value at or below 2) and complex coronary artery diseases (DVD/TVD). Variables that predicted TAVR consisted of high frailty score (at or greater than 6), history of coronary artery bypass surgery (CABG), calcified aorta, and chronic kidney disease (CKD). The LASSO-generated predictive model achieved 98% accuracy on valve replacement modality selection from testing data. The decision tree model consisted of fewer important parameters, namely frailty score, CKD, STS score, age, and history of PCI. The most predictive factor for valve replacement selection was frailty score. The predictive models using different statistical learning methods achieved an excellent concordance predictive accuracy rate of between 93% and 98%.

Intrinsic factors such as age and ethnicity influence statin pharmacokinetics (PK) and may increase the risk of muscle‐related adverse events. This study investigated the effect of age on the PK of simvastatin (SV) and its active metabolite, simvastatin acid (SVA), in healthy Thai subjects using an LC–MS/MS assay while accounting for key SLCO1B1 variants known to affect statin exposure. We further compared our results with published data across multiple ethnic populations via meta‐analysis and conducted an exploratory pharmacogenetic analysis of SVA disposition‐related genes to identify potential candidate variants contributing to PK differences observed between Thai and Caucasian populations. The pharmacokinetic results showed that aging significantly increased SV C _max but did not alter SVA exposure, while SLCO1B1 decreased and poor functions were associated with higher SVA exposure. The meta‐analysis revealed that Thai subjects showed significantly higher SVA levels than Caucasians, Chinese, and Japanese. The pharmacogenetic analysis identified functional or promoter variants in SLCO1B1 and PON genes with allele frequency differences directionally consistent with the observed PK differences between Thais and Caucasians. Our results may partly explain the higher incidence of SV‐associated rhabdomyolysis previously reported in Thais compared with Caucasians and highlight the relevance of these intrinsic factors in SV and SVA PK, with implications for dosing considerations in Thai patients. Additional studies are warranted to further support the involvement of these polymorphic variants in the observed interethnic differences in SVA PK and adverse outcomes.