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INTRODUCTION: The association of testosterone and cognitive decline is inconclusive, and its joint effect with neurofilaments light chain (NfL) remains largely unknown. METHODS: A total of 581 non‐demented older men in the Shanghai Aging Study were included. Blood total testosterone (TT), free testosterone (FT), and NfL were measured at baseline. The relationships between TT, FT, TT/FT‐NfL, and cognitive decline were explored by Cox regression models. RESULTS: During a median follow‐up of 6.7 years, there was an inverse association between TT/FT and cognitive decline (TT, trend p = 0.004, Q1 vs Q4, hazard ratio [HR] = 4.39, 95% confidence interval [CI] = 1.60 to 12.04; FT, trend p = 0.002, Q1 vs Q4, HR = 5.29, 95% CI = 1.50 to 16.89). Compared to participants with high TT/FT‐low NfL, those with low TT/FT‐high NfL had significantly higher risks of cognitive decline (TT, HR = 5.10, 95% CI = 1.11 to 23.40; FT, HR = 6.14, 95% CI = 1.34 to 28.06). DISCUSSION: Our findings suggest that the combination of testosterone and neurodegenerative markers may provide reliable predictive insights into future cognitive decline. Highlights: Testosterone is inversely associated with cognitive decline in older men.There is a joint effect of testosterone and NfL on cognitive decline.Sex hormone and neurodegeneration may synergistically contribute to cognitive deterioration. [ABSTRACT FROM AUTHOR]

Background: Olfactory dysfunction has been reported for its association with cognitive impairment and incident dementia. However, the underlying role of neurodegeneration in this relationship is not fully understood. Method: This prospective cohort study included participants from the Shanghai Aging Study (SAS) who were 60 years or older and diagnosed as dementia‐free at baseline and completed at least one follow‐up interview between 2010 and 2022. At baseline, olfactory identification function was assessed by the 12‐item Sniffin' Sticks Smell test (SSST‐12). Olfactory dysfunction was defined as SSST‐12 < 7, 6, and 5 in three age groups of participants with (age < 70, 70 ≤ age < 80, and age ≥ 80), respectively, according to previously reported normative data of olfaction in the SAS. Baseline blood Neurofilament Light Chain (NfL) was quantified using the ultra‐sensitive Single Molecular Array technology (Quanterix), and dichotomized into low and high levels according to the median concentration. Multiple cognitive function assessments were administered by the Mini‐mental State Examination (MMSE) at baseline and follow‐ups. We used the linear mixed‐effect model to fit the MMSE trajectories in participants with and without olfactory dysfunction adjusting for baseline age, sex, education year, smoking, and APOE ε4, with person‐specific MMSE intercept and slope. Stratified analysis was conducted in participants with low and high NfL. Result: Among 1125 participants included in this study, 296 were defined as having olfactory dysfunction at baseline. Less education, higher proportion of APOE ε4 allele, and lower baseline and follow‐up MMSE scores were observed in participants with olfactory dysfunction (Table 1). During the median 9.4 years follow‐up, participants with olfactory dysfunction at baseline evidenced a faster MMSE decline compared with those without olfactory dysfunction (b [95% CI]: ‐0.08 [‐0.15, ‐0.02], P = 0.02) (Figure 1). Such association remained statistically significant (‐0.18 [‐0.31, ‐0.06], P = 0.005) in the subgroup with high NfL, but not in the subgroup with low NfL (0.004 [‐0.07, 0.07], P = 0.92) (Figure 2). Conclusion: Blood NfL may modify the association of olfactory dysfunction with long‐term cognitive decline in community‐dwelling older adults. In individuals with high potential neurodegeneration, olfactory dysfunction may be an independent indicator of future cognitive deterioration. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown retinal thickness and vascular alterations in Alzheimer's disease (AD), even in patients with mild cognitive impairment (MCI), suggesting that retinal parameters are potential biomarkers for AD. However, the prospective observation of retinal markers is still insufficient. In this study, we aimed to observe longitudinal retinal thickness and vascular changes in patients with AD and MCI, and to identify critical retinal markers that significantly change over time. Method: Patients with AD (N = 22) and MCI (N = 21) were recruited from the Shanghai Memory Study. Age‐ and gender‐matched cognitively normal (NC) participants (N = 26) were included from the Shanghai Aging Study. Optical coherence tomography (OCT) and OCT angiography were performed in all participants at baseline and after an average 19.2 months follow‐up to assess changes in peripapillary retinal nerve fiber layer thickness (p‐RNFL), ganglion cell layer thickness (GCC), macular thickness and volume divided by the Early Treatment Diabetic Retinopathy Study grid, retinal vessel density, and fovea avascular zone (FAZ). Differences of monthly variation rate of retinal parameters between NC, MCI, and AD individuals were evaluated. Result: The monthly variation rates among groups showed significant differences in thickness and volume of full temporal para‐fovea sector, inner temporal peri‐fovea sector, outer temporal para‐fovea sector, and thickness of outer superior hemisphere para‐fovea sector (all P<0.05). There was no difference in the rate of change of p‐RNFL, GCC, retinal vessel density, and FAZ among all groups. Post hoc analysis revealed that the monthly change in thickness and volume of the full‐temporal and outer sectors were significantly faster in AD than in NC, whereas the inner‐temporal sectors changed faster in MCI than in NC groups (all P<0.05). Furthermore, the monthly changes in inner temporal peri‐fovea thickness were faster in MCI than in NC and AD participants (all P<0.05). Conclusion: During follow‐up, retinal temporal thinning was faster in patients with AD, whilst inner retina‐temporal sector thinning was faster in participants with MCI. Accelerated thinning of temporal sectors of the retina, especially the inner‐temporal sector may be an early marker of AD disease progression. [ABSTRACT FROM AUTHOR]

Background: The blood‐based biomarkers are approaching the clinical practice of Alzheimer's disease (AD). Chronic kidney disease has a potential confounding effect on peripheral protein levels. It is essential to characterize the impact of renal function on AD markers. Method: Plasma phospho‐tau181 (P‐tau181), and neurofilament light (NfL) were assayed via the Simoa HD‐X platform in 1189 dementia‐free participants from the Shanghai Aging Study (SAS). The estimated glomerular filter rate (eGFR) was calculated. The association between blood markers and renal function was analyzed. Result: Plasma NfL significantly increased as the eGFR descended (median = 12.27,15.50,20.70pg/mL in the high, medium, and low eGFR groups, respectively, P<0.001). The same trend was observed for plasma p‐tau181, but insignificant after adjustment (P = 0.485). A significant interaction between age and eGFR was found for plasma NfL (Pinteraction< 0.001). Among participants older than 70 years old, the association between plasma NfL and eGFR was stronger (B = ‐0.222, P < 0.001). Conclusion: In a community‐based cohort, renal function differentially impacted blood NfL and P‐tau 181 levels. Plasma NfL level was markedly influenced, whereas P‐tau 181 was a relatively robust marker to comorbidities. [ABSTRACT FROM AUTHOR]

Background: Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD) with high risk of conversion. It is of great challenge to construct reliable biomarkers for predicting conversion from MCI to AD, while the underlying mechanism is still not fully explored. Inter‐dataset generalizability is a prerequisite for clinical use of biomarkers and always a shortage of neuroimaging‐based studies. Method: In this study, we propose a novel framework by integrating structural MRI (sMRI) and both static and dynamic resting‐state functional MRI (fMRI) measurements to investigate the differences between MCI converters (MCI_C) and non‐converters (MCI_NC), and then utilized support vector machine (SVM) to construct the prediction models based on selected features. A total of 186 MCI patients with both MRI and three‐year outcome data were selected from two independent cohorts: Shanghai Memory Study (SMS) cohort for selection of MRI predictors and internal cross‐validation, and ADNI cohort for external validation on the generalizability of these MRI predictors. Result: In comparison with MCI_NC, the MRI converters were mainly characterized by alterations of medial temporal lobe (MTL) with atrophy extending to lateral temporal and regional hyperactivity and instability, posterior parietal cortex (PPC) with atrophy and inter‐regional hypo‐connectivity and connectional instability, and occipital cortex with functional instability. All of the imaging‐based prediction models achieved an AUC above 0.7 and ACC above 70% in both SMS and ADNI cohorts. The combination of static and dynamic fMRI features resulted in overall good performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features into the fMRI model. In both cohorts, the best imaging model was the multi‐modality MRI model which provided excellent performance with AUC above 0.85 and average ACC/sensitivity/specificity around 80%. Conclusion: This inter‐cohort validation study provides a new insight into the mechanisms of MCI conversion and paves a way for eventual clinical use of MRI biomarkers. [ABSTRACT FROM AUTHOR]

Background: Previous studies indicated that blood‐based biomarkers could predict cognitive decline in Alzheimer's disease (AD) continuum. Method: Two hundred and fifty‐one participants with amnestic mild cognitive impairment (aMCI) from the Shanghai Memory Study were followed up for a maximum of 8 years. Baseline blood biomarkers were measured with the single‐molecule array (Simoa) platform. Multipoint clinical diagnosis and domain‐specific cognitive functions were assessed to investigate the longitudinal relationship between blood biomarkers and clinical AD progression. Result: Participants with high‐risk plasma Aβ42/Aβ40 (A) and p‐tau181 (T) level demonstrated the highest probability of incident AD (HR 5.54, 95% CI 2.99‐10.27), and the most dramatic decline in global cognition, attention, executive function, visuospatial function, and language. Comparing to young‐old participants, the old‐old ones with low‐ and moderate‐risk AT showed higher AD risks (HR 3.70, 95% CI 1.16‐11.81; HR 3.15, 95% CI 1.37‐7.26), and faster cognitive deterioration. Conclusion: The results supported the use of plasma Aβ42/Aβ40 and p‐tau181 as accessible and feasible indicators of AD progression and the long‐term cognitive deterioration, especially in patients with older age. [ABSTRACT FROM AUTHOR]