Cognitive decline in older adults is a growing public health concern, and traditional measures such as chronological age are insufficient for accurately assessing cognitive function. Phenotypic age (PhenoAge) and phenotypic age acceleration (PhenoAgeAccel), which reflect biological age and aging acceleration, may be better predictors of cognitive decline. Additionally, physical activity (PA) has been recognized for its protective effects on aging and cognitive health. This study explored the role of PhenoAge and PhenoAgeAccel in cognitive performance and investigated whether PA moderates this relationship. We used data from the National Health and Nutrition Examination Survey, which analyzed 1298 participants aged 60 years and older. PhenoAge was calculated using 10 biomarkers, and PhenoAgeAccel was derived as the difference between chronological age and PhenoAge. Cognitive performance was assessed using the Digit Symbol Substitution Test. The relationship between PhenoAge, PhenoAgeAccel, and low cognitive performance was analyzed using weighted logistic regression models. Subgroup and sensitivity analyses were conducted, and the interactions between PhenoAgeAccel and PA were evaluated. Both PhenoAge and PhenoAgeAccel scores were significantly associated with low cognitive performance. The highest quartiles of PhenoAge (odds ratio = 3.22, P = .025) and PhenoAgeAccel (odds ratio = 2.31, P = .022) were associated with higher odds of low cognitive performance. By contrast, chronological age did not show a significant relationship with cognitive performance. PA was found to moderate the association between PhenoAgeAccel and cognitive performance (P for interaction = .045). Higher levels of PA attenuated the impact of PhenoAgeAccel on cognitive decline. Receiver operating characteristic curve analysis showed that PhenoAge (area under the curve [AUC] = 0.562), PhenoAgeAccel (AUC = 0.589), and chronological age (AUC = 0.513) were significantly different. In conclusion, PhenoAgeAccel and PA are significant predictors of cognitive decline, with PA offering a protective effect against the impact of accelerated aging on cognition.

Introduction. The oral microbiota is the second most complex microbial community in the human body. It has been suggested that poor oral health may be associated with an increased risk of obesity. Hypothesis/Gap Statement. However, both previous observational and mechanistic studies on oral microbiota do not take into account the obesity-related acanthosis nigricans (AN), which is the most common dermatological manifestation in individuals with obesity. Aim. This study aimed to investigate the altered composition, function and diagnostic value of the oral microbiota in obesity with or without acanthosis nigricans (AN). Methodology. We characterized the oral bacteria signature in a Chinese cohort (ChiCTR2300073353) of 99 patients with obesity and obesity-related AN (Ob_AN) and 50 healthy controls using 16S rRNA gene V3–V4 region sequencing. Results. The microbial richness (abundance-based coverage estimators and observed species indices) was significantly greater in the Ob_AN and obesity groups than in the control group; however, microbial diversity (Shannon index) did not differ significantly. Distinct separation in the microbial community amongst the three groups was observed. Prevotella species, including Prevotella melaninogenica, Prevotella nanceiensis and Prevotella pallens, were associated with composition alterations and predicted functions (significant downregulation of ATP-binding cassette transporters) associated with microbial dysbiosis in the obesity and Ob_AN groups. Moreover, Prevotella and Lautropia genera assessments could indicate obesity and obesity-related AN risk. Conclusions. The notable reduction of plenty of oral microbiota and high levels of Prevotella spp. may play a critical role in obesity with AN. Oral microbiota may serve as biomarkers for diagnosing, preventing and even treating obesity-related AN.