Mendelian randomization study of diabetes and dementia in the Million Veteran Program

Plasma IL-6 levels and their association with brain health and dementia risk: A population-based cohort study

BACKGROUND

Recent studies have associated immune abnormalities with dementia. IL-6 is a crucial cytokine in inflammatory responses, and recent evidence has linked elevated IL-6 levels to changes in brain structure and cognitive decline. However, the connection between IL-6 levels, cognition, brain volumes, and dementia risk requires exploration in large prospective cohorts.

METHODS

This study utilized a longitudinal cohort from the UK Biobank to analyze the correlation between IL-6 expression levels, cognitive performance, and cortical and subcortical brain volumes through linear regression. Additionally, we assessed the association between IL-6 levels and long-term dementia risk using Cox regression analysis. We also used one-sample Mendelian randomization to analyze the impact of genetic predisposition of dementia on elevated IL-6 levels.

RESULTS

A total of 50,864 participants were included in this study, with 1,391 new cases of all-cause dementia identified. Higher plasma IL-6 levels are associated with cortical and subcortical atrophy in regions such as the fusiform, thalamus proper, hippocampus, and larger ventricle volumes. IL-6 levels are negatively associated with cognitive performance in pair matching, numeric memory, prospective memory, and reaction time tests. Furthermore, elevated IL-6 levels are linked to a 23-35 % increased risk of all-cause dementia over an average follow-up period of 13.2 years. The one-sample Mendelian randomization analysis did not show associations between the genetic predisposition of dementia and elevated IL-6 levels.

CONCLUSIONS

Increased IL-6 levels are associated with worse cognition, brain atrophy, and a heightened risk of all-cause dementia. Our study highlights the need to focus on the role of peripheral IL-6 levels in managing brain health and dementia risk.

Role of endothelial glycocalyx in central nervous system diseases and evaluation of the targeted therapeutic strategies for its protection: a review of clinical and experimental data

Central nervous system (CNS) diseases, such as stroke, traumatic brain injury, dementia, and demyelinating diseases, are generally characterized by high morbidity and mortality, which impose a heavy economic burden on patients and their caregivers throughout their lives as well as on public health. The occurrence and development of CNS diseases are closely associated with a series of pathophysiological changes including inflammation, blood-brain barrier disruption, and abnormal coagulation. Endothelial glycocalyx (EG) plays a key role in these changes, making it a novel intervention target for CNS diseases. Herein, we review the current understanding of the role of EG in common CNS diseases, from the perspective of individual pathways/cytokines in pathophysiological and systematic processes. Furthermore, we emphasize the recent developments in therapeutic agents targeted toward protection or restoration of EG. Some of these treatments have yielded unexpected pharmacological results, as previously unknown mechanisms underlying the degradation and destruction of EG has been brought to light. Furthermore, the anti-inflammatory, anticoagulative, and antioxidation effects of EG and its protective role exerted via the blood-brain barrier have been recognized.

Peripheral GFAP and NfL as early biomarkers for dementia: longitudinal insights from the UK Biobank

BACKGROUND

Peripheral glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are sensitive markers of neuroinflammation and neuronal damage. Previous studies with highly selected participants have shown that peripheral GFAP and NfL levels are elevated in the pre-clinical phase of Alzheimer's disease (AD) and dementia. However, the predictive value of GFAP and NfL for dementia requires more evidence from population-based cohorts.

METHODS

This was a prospective cohort study to evaluate UK Biobank participants enrolled from 2006 to 2010 using plasma GFAP and NfL measurements measured by Olink Target Platform and prospectively followed up for dementia diagnosis. Primary outcome was the risk of clinical diagnosed dementia. Secondary outcomes were cognition. Linear regression was used to assess the associations between peripheral GFAP and NfL with cognition. Cox proportional hazard models with cross-validations were used to estimate associations between elevated GFAP and NfL with risk of dementia. All models were adjusted for covariates.

RESULTS

A subsample of 48,542 participants in the UK Biobank with peripheral GFAP and NfL measurements were evaluated. With an average follow-up of 13.18 ± 2.42 years, 1312 new all-cause dementia cases were identified. Peripheral GFAP and NfL increased up to 15 years before dementia diagnosis was made. After strictly adjusting for confounders, increment in NfL was found to be associated with decreased numeric memory and prolonged reaction time. A greater annualized rate of change in GFAP was significantly associated with faster global cognitive decline. Elevation of GFAP (hazard ratio (HR) ranges from 2.25 to 3.15) and NfL (HR ranges from 1.98 to 4.23) increased the risk for several types of dementia. GFAP and NfL significantly improved the predictive values for dementia using previous models (area under the curve (AUC) ranges from 0.80 to 0.89, C-index ranges from 0.86 to 0.91). The AD genetic risk score and number of APOE*E4 alleles strongly correlated with GFAP and NfL levels.

CONCLUSIONS

These results suggest that peripheral GFAP and NfL are potential biomarkers for the early diagnosis of dementia. In addition, anti-inflammatory therapies in the initial stages of dementia may have potential benefits.

Diabetes and vascular mild cognitive impairment among Chinese ≥50 years: A cross-sectional study with 2020 participants

BACKGROUND

With the decline of cognitive function in vascular cognitive impairment, the burden on the family and society will increase. Therefore, early identification of vascular mild cognitive impairment (VaMCI) is crucial. The focus of early identification of VaMCI is on the attention of risk factors. Therefore, this study aimed to investigate the relationship between diabetes and VaMCI among the Chinese, hoping to predict the risk of VaMCI by diabetes and to move the identification of vascular cognitive impairment forward.

METHODS

We collected data from seven clinical centers and nine communities in China. All participants were over 50 years of age and had cognitive complaints. We collected basic information of the participants, and cognitive function was professionally assessed by the Montreal Cognitive Assessment scale. Finally, logistic regression analysis was used to analyze the correlation between each factor and VaMCI.

RESULTS

A total of 2020 participants were included, including 1140 participants with VaMCI and 880 participants with normal cognition. In univariate logistic regression analysis, age, heavy smoking, and diabetes had a positive correlation with VaMCI. At the same time, being married, high education, and light smoking had a negative correlation with VaMCI. After correction, only diabetes (OR = 1.04, 95% CI: 1.01-1.09, p = 0.05) had a positive correlation with VaMCI, and high education (OR = 0.60, 95% CI:.45-.81, p = 0.001) had a negative correlation with VaMCI.

CONCLUSION

In our study, we found that diabetes had a positive correlation with VaMCI, and high education had a negative correlation with VaMCI. Therefore, early identification and timely intervention of diabetes may reduce the risk of VaMCI and achieve early prevention of VaMCI.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.