BackgroundObservational studies report inconclusive effects of tea consumption on the risk of Alzheimer’s disease (AD), and the mechanisms are unclear. This study aims to investigate the effects of genetically predicted tea intake (cups of tea consumed per day) on AD, brain volume, and cerebral small vessel disease (CSVD) using the two-sample Mendelian randomization (MR) method.MethodsSummary statistics of tea intake were obtained from UK Biobank (N = 447,485), and AD was from the International Genomics of Alzheimer’s Project (N = 54,162). Genetic instruments were retrieved from UK Biobank using brain imaging-derived phenotypes for brain volume outcomes (N > 33,224) and genome-wide association studies for CSVD (N: 17,663–48,454).ResultsIn the primary MR analysis, tea intake significantly increased the risk of AD using two different methods (ORIVW = 1.48, 95% CI: [1.14, 1.93]; ORWM = 2.00, 95% CI: [1.26, 3.18]) and reached a weak significant level using MR-Egger regression (p < 0.1). The result passed all the sensitivity analyses, including heterogeneity, pleiotropy, and outlier tests. In the secondary MR analysis, per extra cup of tea significantly decreased gray matter (βWM = −1.63, 95% CI: [−2.41, −0.85]) and right hippocampus volume (βWM = −1.78, 95% CI: [−2.76, −0.79]). We found a nonlinear association between tea intake and AD in association analysis, which suggested that over-drinking with more than 13 cups per day might be a risk factor for AD. Association analysis results were consistent with MR results.ConclusionThis study revealed a potential causal association between per extra cup of tea and an increased risk of AD. Genetically predicted tea intake was associated with a decreased brain volume of gray matter and the right hippocampus, which indicates that over-drinking tea might lead to a decline in language and memory functions. Our results shed light on a novel possible mechanism of tea intake to increase the risk of AD by reducing brain volume.

We assessed the causal association of three COVID-19 phenotypes with insulin-like growth factor 1, estrogen, testosterone, dehydroepiandrosterone (DHEA), thyroid-stimulating hormone, thyrotropin-releasing hormone, luteinizing hormone (LH), and follicle-stimulating hormone. We used bidirectional two-sample univariate and multivariable Mendelian randomization (MR) analyses to evaluate the direction, specificity, and causality of the association between CNS-regulated hormones and COVID-19 phenotypes. Genetic instruments for CNS-regulated hormones were selected from the largest publicly available genome-wide association studies of the European population. Summary-level data on COVID-19 severity, hospitalization, and susceptibility were obtained from the COVID-19 host genetic initiative. DHEA was associated with increased risks of very severe respiratory syndrome (odds ratio [OR] = 4.21, 95% confidence interval [CI]: 1.41–12.59), consistent with multivariate MR results (OR = 3.72, 95% CI: 1.20–11.51), and hospitalization (OR = 2.31, 95% CI: 1.13–4.72) in univariate MR. LH was associated with very severe respiratory syndrome (OR = 0.83; 95% CI: 0.71–0.96) in univariate MR. Estrogen was negatively associated with very severe respiratory syndrome (OR = 0.09, 95% CI: 0.02–0.51), hospitalization (OR = 0.25, 95% CI: 0.08–0.78), and susceptibility (OR = 0.50, 95% CI: 0.28–0.89) in multivariate MR. We found strong evidence for the causal relationship of DHEA, LH, and estrogen with COVID-19 phenotypes.

Dementia is a well-known syndrome and Alzheimer's disease (AD) is the main cause of dementia. Lipids play a key role in the pathogenesis of AD, however, the prediction value of serum lipidomics on AD remains unclear. This study aims to construct a lipid score system to predict the risk of progression from mild cognitive impairment (MCI) to AD. First, we used the least absolute shrinkage and selection operator (LASSO) Cox regression model to select the lipids that can signify the progression from MCI to AD based on 310 older adults with MCI.Then we constructed a lipid score based on 14 single lipids using Cox regression and estimated the association between the lipid score and progression from MCI to AD. The prevalence of AD in the low-, intermediate-and high-score groups was 42.3%, 59.8%, and 79.8%, respectively. The participants in the intermediateand high-score group had a 1.65-fold (95% CI 1.10 to 2.47) and 3.55-fold (95% CI 2.40 to 5.26) higher risk of AD, respectively, as compared to those with low lipid scores. The lipid score showed moderate prediction efficacy (c-statistics > 0.72).These results suggested that the score system based on serum lipidomics is useful for the prediction of progression from MCI to AD.

Objectives: We assessed the causal association of three COVID-19 phenotypes with insulin-like growth factor 1 (IGF-1), estrogen, testosterone, dehydroepiandrosterone (DHEA), thyroid-stimulating hormone (TSH), thyrotropin-releasing hormone (TRH), luteinizing hormone (LH), and follicle-stimulating hormone (FSH). Methods: We used a bidirectional two-sample univariate and multivariable Mendelian randomization (MR) analysis to evaluate the direction, specificity, and causality of the association between CNS-regulated hormones and COVID-19 phenotypes. Genetic instruments for CNS-regulated hormones were selected from the largest publicly available genome-wide association studies in the European population. Summary-level data on COVID-19 severity, hospitalization, and susceptibility were obtained from the COVID-19 host genetic initiative. Results: DHEA was associated with increased risks of very severe respiratory syndrome (OR=4.21, 95% CI: 1.41-12.59), consistent with the results in multivariate MR (OR=3.72, 95% CI: 1.20-11.51), and hospitalization (OR = 2.31, 95% CI: 1.13-4.72) in univariate MR. LH was associated with very severe respiratory syndrome (OR=0.83; 95% CI: 0.71-0.96) in univariate MR. Estrogen was negatively associated with very severe respiratory syndrome (OR=0.09, 95% CI: 0.02-0.51), hospitalization (OR=0.25, 95% CI: 0.08-0.78), and susceptibility (OR=0.50, 95% CI: 0.28-0.89) in multivariate MR. Conclusions: We found strong evidence for the causal relationship of DHEA, LH, and estrogen with COVID-19 phenotypes.