Ai-Ling Lin

Alzheimer’s disease (AD) is the most prominent form of dementia in the USA, with 7·2 million Americans aged 65 years and older, living with AD in 2025. As the US population ages, there is a need for preventative measures to reduce the risk of AD. The apo E ϵ4 (ApoE4) allele is the greatest known genetic risk factor for AD, with heterozygous or homozygous ApoE4 carriers having increased likelihood of developing it. Due to abnormal lipid handling by ApoE4 carriers, precision nutrition may play a major role in delaying the onset of AD in these subjects, with n -3 fatty acids, lipids such as olive oil, vitamins C and E, Mediterranean, ketogenic and high-fibre diets showing promising but still limited evidence for ApoE4 carriers, as most of the current evidence comes from preclinical studies. Additionally, compared with non-carriers, ApoE4 carriers exhibit early gut dysbiosis, an imbalance in the gut microbiota that can disrupt normal digestive, metabolic and immune functions. The potential of probiotics and prebiotics to restore microbial balance and reduce dysbiosis in ApoE4 carriers has also been investigated. This review summarises evidence from observational human cohorts and clinical studies, animal models and in vitro mechanistic studies to examine emerging nutritional and gut-microbiome-based approaches to reduce AD risk in ApoE4 carriers. Future directions include the integration of precision nutrition with multi-omics approaches to allow for scalable and comprehensive analyses of ApoE–diet interactions and utilisation of larger human cohorts to determine the efficacy of nutritional and gut-based AD prevention strategies.

Carriers of the apolipoprotein E4 (APOE4) allele often develop cerebrovascular dysfunction and broader systemic alterations decades before the onset of Alzheimer’s disease (AD) pathology or clinical symptoms. Early interventions that can improve these functions may help delay or slow AD progression. In this study, we repurposed rapamycin (sirolimus), an FDA-approved medication with anti-aging properties, to target APOE4 associated multisystem dysfunction. We conducted a single arm trial of low dose rapamycin (1 mg/day for 4 weeks) in cognitively normal adults aged 45 to 65 years, prior to detectable AD pathology, stratified by APOE genotype. The primary outcome was cerebral blood flow (CBF), while secondary outcomes included plasma metabolomics, inflammatory cytokines, AD biomarkers, and gut microbiome composition. Twenty-three participants completed the study, including 9 APOE4 carriers and 14 non-carriers. Rapamycin significantly increased CBF in APOE4 carriers, with increases exceeding 15% across multiple brain regions, and improved metabolic and inflammatory profiles with minimal adverse effects. In contrast, non-carriers showed no significant change in CBF and exhibited distinct physiological responses, highlighting genotype dependent effects. These findings suggest that rapamycin may mitigate early cerebrovascular and systemic dysfunction in APOE4 carriers and support a precision medicine approach in which therapeutic response is influenced by genotype. Trial Registration: ClinicalTrials.gov (NCT05386914). Title: Short Term ApoE-dependent Cerebral Blood Flow Response to Sirolimus in Cognitively Normal Adults (Part I).

Alzheimer's Disease (AD) remains a leading cause of cognitive decline with no known cure, motivating the development of therapies that slow neurodegeneration. Rapamycin, an FDA-approved inhibitor of the mammalian target of rapamycin (mTOR) pathway, has demonstrated promising anti-aging and neuroprotective effects. However, characterizing its treatment effects and identifying the biological factors that contribute to treatment response remain challenging because of complex interactions across multiple biological systems and the limited availability of patient data. In this work, we propose a three-stage multimodal deep learning framework called TreatmentFormer for predicting rapamycin treatment status from heterogeneous biomedical data including both brain imaging data and tabular data (e.g., microbiome profiles, blood-based biomarkers, cerebral blood flow measurements, and clinical variables (e.g., gender, age, and body mass index)). First, a Random Forest-based feature selection module reduces noise in high-dimensional tabular data while preserving representation across modalities. Second, modality-specific encoders map imaging and tabular inputs into a shared latent space via self-supervised contrastive learning, enabling alignment across modalities. Finally, a transformer-based architecture integrates these representations to capture cross-modal interactions and perform treatment classification. Evaluated on a cohort of 23 participants with baseline and post-treatment timepoints, TreatmentFormer achieves an average prediction accuracy of 71.25\% across 10 independent test runs. Despite the challenges of small sample size and heterogeneous data, the model demonstrates stable and consistent performance. Post hoc SHAP-based feature analysis further identifies key biomarkers associated with treatment response, particularly within blood-based and inflammatory modalities. These findings demonstrate that combining feature selection with multimodal representation learning provides a promising and robust approach for modeling treatment effects in small-sample biomedical studies. Importantly, this framework may have significant implications for clinical research and medical applications by identifying the biological features and quantitative measurements that drive individual responses to rapamycin. Such insights could facilitate the development of predictive biomarkers, improve patient stratification, and ultimately inform future approaches to AD diagnosis and therapeutic development.

Microbiome beta diversity analysis relies on distance-based methods, including permutational multivariate analysis of variance (PERMANOVA) combined with fixed ecological distance metrics (Bray-Curtis, Euclidean, Jaccard, and UniFrac), which treat all microbial taxa uniformly, regardless of their biological relevance to community differences. This “one-size-fits-all” approach may miss subtle but biologically meaningful patterns in complex microbiome data. We present Metric Learning for Statistical Inference (MeLSI), a novel machine learning framework that learns data-adaptive distance metrics optimized for detecting community composition differences in multivariate microbiome analyses. MeLSI employs an ensemble of weak learners using bootstrap sampling, feature subsampling, and gradient-based optimization to learn optimal feature weights, combined with rigorous permutation testing for statistical inference. The learned metrics can be used with PERMANOVA for hypothesis testing and with principal coordinates analysis for ordination visualization. Comprehensive validation on synthetic benchmarks and real data sets shows that MeLSI maintains proper type I error control while delivering competitive or superior statistical power for detecting subtle community shifts and, crucially, supplies interpretable feature-weight profiles that clarify which taxa drive group separation. On the DietSwap data set, MeLSI was the only method to achieve significance at α = 0.05, demonstrating that adaptive weighting can detect diet-induced community shifts that fixed metrics miss. Across all data sets, the learned feature weights identified biologically relevant taxa while providing actionable insight that no fixed distance metric can supply. MeLSI therefore offers a statistically rigorous tool that augments beta diversity analysis with transparent, data-driven interpretability. IMPORTANCE: Understanding which microbes differ between groups of interest could reveal therapeutic targets and diagnostic biomarkers. However, current analysis methods treat all microbes equally (similar to using the same ruler to measure everything, regardless of what matters most). This means subtle but biologically important differences may go undetected, especially when only a few key species drive disease states while hundreds of “bystander” species add noise. Metric Learning for Statistical Inference (MeLSI) solves this by learning which microbes matter most for each specific comparison. In comparing male and female gut microbiomes, MeLSI identified specific bacterial families driving the differences, providing actionable biological insights that standard methods miss. This capability is particularly crucial for detecting early disease biomarkers, where differences are subtle and masked by biological variability. By telling researchers not just whether groups differ, but which specific microbes drive those differences, MeLSI accelerates the path from microbiome data to testable biological hypotheses and clinical applications.

Stroke survivors face an elevated risk of developing Alzheimer’s disease (AD), yet the biological mechanisms linking these conditions remain poorly defined. Here, we show that a stroke-induced gut microbiome is a key driver of AD-related pathology. Fecal microbiota transplantation (FMT) from stroke patients into young triple-transgenic Alzheimer’s disease (3xTg-AD) mice accelerated tau phosphorylation, increased neuroinflammation, and disrupted metabolic homeostasis in both the brain and gut, compared with FMT from healthy donors. Mice receiving stroke-derived microbiota exhibited persistent, donor-specific dysbiosis and broad metabolic reprogramming involving redox balance, nucleotide metabolism, and energy pathways in cecal contents and brain tissue. These metabolic disturbances were accompanied by widespread and region-specific transcriptional changes revealed by single-cell spatial transcriptomics, including glial activation, impaired neuron–glia communication, and dysregulation of mitochondrial, amyloid-processing and inflammatory pathways across cortical and hippocampal regions. Collectively, these findings identify post-stroke gut dysbiosis as a mechanistic contributor to heightened neurodegenerative vulnerability and AD risk, highlighting the gut–brain axis as a potentially modifiable target for preventing post-stroke dementia.

Microbiome beta diversity analysis relies on distance-based methods including PERMANOVA combined with fixed ecological distance metrics (Bray-Curtis, Euclidean, Jaccard, and UniFrac), which treat all microbial taxa uniformly regardless of their biological relevance to community differences. This “one-size-fits-all” approach may miss subtle but biologically meaningful patterns in complex microbiome data. We present MeLSI (Metric Learning for Statistical Inference), a novel machine learning framework that learns data-adaptive distance metrics optimized for detecting community composition differences in multivariate microbiome analyses. MeLSI employs an ensemble of weak learners using bootstrap sampling, feature subsampling, and gradient-based optimization to learn optimal feature weights, combined with rigorous permutation testing for statistical inference. The learned metrics can be used with PERMANOVA for hypothesis testing and with Principal Coordinates Analysis (PCoA) for ordination visualization. Comprehensive validation on synthetic benchmarks and real datasets shows that MeLSI maintains proper Type I error control while delivering competitive or superior F-statistics when signal structure aligns with CLR-based weighting and, crucially, supplies interpretable feature-weight profiles that clarify which taxa drive group separation. On the Atlas1006 dataset, MeLSI achieved stronger effect sizes than the best traditional methods, and even when performance was comparable, the learned feature weights provided biological insight that fixed metrics cannot supply. MeLSI therefore offers a statistically rigorous tool that augments beta diversity analysis with transparent, data-driven interpretability. IMPORTANCE: Understanding which microbes differ between groups of interest could reveal therapeutic targets and diagnostic biomarkers. However, current analysis methods treat all microbes equally (similar to using the same ruler to measure everything, regardless of what matters most). This means subtle but clinically important differences may go undetected, especially when only a few key species drive disease while hundreds of “bystander” species add noise. MeLSI solves this by learning which microbes matter most for each specific comparison. In comparing male and female gut microbiomes, MeLSI identified specific bacterial families driving the differences, providing actionable biological insights that standard methods miss. This capability is particularly crucial for detecting early disease biomarkers, where differences are subtle and masked by biological variability. By telling researchers not just whether groups differ, but which specific microbes drive those differences, MeLSI accelerates the path from microbiome data to testable biological hypotheses and clinical applications.

The apolipoprotein E4 (APOE4) allele is the strongest genetic risk factor for late‐onset Alzheimer's disease (AD), associated with early brain metabolic dysfunction and gut microbiome alterations. Targeting these early changes through dietary interventions may reduce AD risk in asymptomatic carriers. This study evaluated whether a ketogenic diet (KD) could reshape the gut microbiome and enhance key brain metabolite levels in young APOE4 mice, using APOE3 mice as a neutral‐risk comparison. Male and female APOE3 and APOE4 mice were fed either a control diet or KD for 16 weeks, starting at 12 weeks of age. We used shotgun metagenomics and targeted brain metabolomics to identify microbe–metabolite signatures linked to neuroprotection. KD increased beneficial species such as Lactobacillus johnsonii and Lactobacillus reuteri while reducing pathogenic Bacteroides intestinalis . These microbial shifts correlated with improved brain metabolites related to mitochondrial function, neurotransmitter balance, redox homeostasis, and lipid metabolism. Notably, Lactobacillus species and B. intestinalis exhibited inverse correlations with key brain metabolite levels, suggesting their roles as both modulators and biomarkers of brain health. APOE4 females showed the greatest benefits, including restored microbiome diversity and normalization of brain metabolite levels. In contrast, APOE3 mice showed microbiome changes but limited brain metabolic responses. These findings highlight KD's potential to reprogram the gut–brain axis in a genotype‐ and sex‐dependent manner, supporting its use as a precision nutrition strategy to reduce AD risk, particularly in asymptomatic female APOE4 carriers.

Pulmonary dynamic contrast-enhanced (DCE) MRI is clinically useful for assessing pulmonary perfusion, but its signal-to-noise ratio (SNR) is limited. A self-supervised learning network-based plug-and-play (PnP) denoising model was developed to improve the image quality of pulmonary perfusion MRI. A dataset of patients with suspected pulmonary diseases was used. Asymmetric pixel-shuffle downsampling blind-spot network (AP-BSN) training inputs were two-dimensional background-subtracted perfusion images without clean ground truth. The AP-BSN is incorporated into a PnP model (PnP-BSN) for balancing noise control and image fidelity. Model performance was evaluated by SNR, sharpness, and overall image quality from two radiologists. The fractal dimension and k-means segmentation of the pulmonary perfusion images were calculated for comparing denoising performance. The model was trained on 29 patients and tested on 8 patients. The performance of PnP-BSN was compared to denoising convolutional neural network (DnCNN) and a Gaussian filter. PnP-BSN showed the highest reader scores in terms of SNR, sharpness, and overall image quality as scored by two radiologists. The expert scoring results for DnCNN, Gaussian, and PnP-BSN were 2.25 ± 0.65, 2.44 ± 0.73, and 3.56 ± 0.73 for SNR; 2.62 ± 0.52, 2.62 ± 0.52, and 3.38 ± 0.64 for sharpness; and 2.16 ± 0.33, 2.34 ± 0.42, and 3.53 ± 0.51 for overall image quality (p < 0.05 for all). PnP-BSN outperformed DnCNN and a Gaussian filter for denoising pulmonary perfusion MRI, which led to improved quantitative fractal analysis.

Rapamycin, known for its anti-aging properties, shows promise as a preventive strategy for Alzheimer’s disease (AD) in APOE4 carriers—the highest-risk group for late-onset AD. Here we show that a 4-week open-label trial of low-dose Rapamycin (Sirolimus; 1 mg/day) significantly improved cerebral blood flow (CBF) relative to baseline in cognitively normal APOE4 carriers (E4(+)) aged 45–65. It also reduced inflammatory cytokines, enhanced lipid metabolism, increased short-chain fatty acids (SCFA) and enriched gut microbiome composition linked to SCFA production. Conversely, non-carriers (E4(-)) displayed stable baseline-to-post-treatment CBF and SCFA and demonstrated different treatment-related patterns of metabolic and anti-inflammatory effects than E4(+). Serum amyloid A and tau remained unchanged for both groups. These findings suggest Rapamycin may counter early vascular and metabolic deficits in E4(+) individuals, with genotype-specific effects. By bridging anti-aging research and AD prevention, this study highlights a novel, safe, and precision-based approach to mitigating AD risk in APOE4 carriers.