Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations.

Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity and mortality. Here we developed a proteomic age clock in the UK Biobank (n = 45,441) using a proteomic platform comprising 2,897 plasma proteins and explored its utility to predict major disease morbidity and mortality in diverse populations. We identified 204 proteins that accurately predict chronological age (Pearson r = 0.94) and found that proteomic aging was associated with the incidence of 18 major chronic diseases (including diseases of the heart, liver, kidney and lung, diabetes, neurodegeneration and cancer), as well as with multimorbidity and all-cause mortality risk. Proteomic aging was also associated with age-related measures of biological, physical and cognitive function, including telomere length, frailty index and reaction time. Proteins contributing most substantially to the proteomic age clock are involved in numerous biological functions, including extracellular matrix interactions, immune response and inflammation, hormone regulation and reproduction, neuronal structure and function and development and differentiation. In a validation study involving biobanks in China (n = 3,977) and Finland (n = 1,990), the proteomic age clock showed similar age prediction accuracy (Pearson r = 0.92 and r = 0.94, respectively) compared to its performance in the UK Biobank. Our results demonstrate that proteomic aging involves proteins spanning multiple functional categories and can be used to predict age-related functional status, multimorbidity and mortality risk across geographically and genetically diverse populations.

One-size-fits-all versus risk-category-based screening interval strategies for cardiovascular disease prevention in Chinese adults: a prospective cohort study.

Background: In non-high-risk individuals, risk-category-based atherosclerotic cardiovascular disease (ASCVD) screening strategies may be more cost-effective than one-size-fits-all approaches. However, current decisions are constrained by a lack of research evidence. We aimed to explore appropriate risk-category-based screening interval strategies for non-high-risk individuals in ASCVD primary prevention in the Chinese population. Methods: We used data from 28,624 participants in the China Kadoorie Biobank (CKB) who had completed at least two field surveys. The risk assessment tools were the 10-year ASCVD risk prediction models developed based on the CKB cohort. We constructed multistate Markov models to model disease progression and estimate transition probabilities between different risk categories. The total person-years spent unidentified in the high-risk state over a 10-year period were calculated for each screening interval protocol. We also estimated the number of ASCVD events prevented, quality-adjusted life years (QALYs) gained, and costs saved when compared to the 3-yearly screening protocol. Findings: When compared to the uniform 3-yearly protocol, most risk-category-based screening interval protocols would identify more high-risk individuals timely, thus preventing more ASCVD events and gaining QALYs. A few of them would reduce total health-care costs. The protocol, which used 6-year, 3-year, and 2-year screening intervals for low-risk, intermediate-low-risk, and intermediate-high risk individuals, was optimal, and would reduce the person-years spent unidentified in the high-risk category by 17.9% (95% CI: 13.1%-21.9%), thus preventing an estimated 113 thousand (95% CI: 83-138) hard ASCVD events for Chinese adults aged 30-79 over a 10-year period. When using a lower cost of statin therapy, more screening protocols would gain QALYs while saving costs. Interpretation: For the primary prevention of ASCVD, risk-category-based screening protocols outperformed the one-size-fits-all approach in the Chinese population. Funding: This work was supported by National Natural Science Foundation of China (82192904, 82388102, 82192900) and grants (2023YFC2509400) from the National Key R&D Program of China. The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z), grants (2016YFC0900500) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 91846303, 81941018), and Chinese Ministry of Science and Technology (2011BAI09B01).

State of the Art Modelling of the Breast Cancer Metastatic Microenvironment: Where Are We?

Metastatic spread of tumour cells to tissues and organs around the body is the most frequent cause of death from breast cancer. This has been modelled mainly using mouse models such as syngeneic mammary cancer or human in mouse xenograft models. These have limitations for modelling human disease progression and cannot easily be used for investigation of drug resistance and novel therapy screening. To complement these approaches, advances are being made in ex vivo and 3D in vitro models, which are becoming progressively better at reliably replicating the tumour microenvironment and will in the future facilitate drug development and screening. These approaches include microfluidics, organ-on-a-chip and use of advanced biomaterials. The relevant tissues to be modelled include those that are frequent and clinically important sites of metastasis such as bone, lung, brain, liver for invasive ductal carcinomas and a distinct set of common metastatic sites for lobular breast cancer. These sites all have challenges to model due to their unique cellular compositions, structure and complexity. The models, particularly in vivo, provide key information on the intricate interactions between cancer cells and the native tissue, and will guide us in producing specific therapies that are helpful in different context of metastasis.

Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults.

Elevated blood pressure (BP) is major risk factor for cardiovascular diseases (CVD). Genome-wide association studies (GWAS) conducted predominantly in populations of European ancestry have identified >2,000 BP-associated loci, but other ancestries have been less well-studied. We conducted GWAS of systolic, diastolic, pulse, and mean arterial BP in 100,453 Chinese adults. We identified 128 non-overlapping loci associated with one or more BP traits, including 74 newly-reported associations. Despite strong genetic correlations between populations, we identified appreciably higher heritability and larger variant effect sizes in Chinese compared with European or Japanese ancestry populations. Using instruments derived from these GWAS, multivariable Mendelian randomisation demonstrated that BP traits contribute differently to the causal associations of BP with CVD. In particular, only pulse pressure was independently causally associated with carotid plaque. These findings reinforce the need for studies in diverse populations to understand the genetic determinants of BP traits and their roles in disease risk.

ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.

MOTIVATION: Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. RESULTS: We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. AVAILABILITY AND IMPLEMENTATION: The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

[Distribution and influencing factors of lipoprotein (a) levels in non-arteriosclerotic cardiovascular disease population in China].

Objective: To describe the distribution of lipoprotein (a) [Lp(a)] levels in non-arteriosclerotic cardiovascular disease (ASCVD) population in China and explore its influencing factors. Methods: This study was based on a nested case-control study in the CKB study measured plasma biomarkers. Lp(a) levels was measured using a polyclonal antibody-based turbidimetric assay certified by the reference laboratory and ≥75.0 nmol/L defined as high Lp(a). Multiple logistic regression model was used to examine the factors related to Lp(a) levels. Results: Among the 5 870 non-ASCVD population included in the analysis, Lp(a) levels showed a right-skewed distribution, with a M (Q1, Q3) of 17.5 (8.8, 43.5) nmol/L. The multiple logistic regression analysis found that female was associated with high Lp(a) (OR=1.23, 95%CI: 1.05-1.43). The risk of increased Lp(a) levels in subjects with abdominal obesity was significantly reduced (OR=0.68, 95%CI: 0.52-0.89). As TC, LDL-C, apolipoprotein A1(Apo A1), and apolipoprotein B(Apo B) levels increased, the risk of high Lp(a) increased, with OR (95%CI) for each elevated group was 2.40 (1.76-3.24), 2.68 (1.36-4.93), 1.29 (1.03-1.61), and 1.65 (1.27-2.13), respectively. The risk of high Lp(a) was reduced in the HDL-C lowering group with an OR (95%CI) of 0.76 (0.61-0.94). In contrast, an increase in TG levels and the ratio of Apo A1/Apo B(Apo A1/B) was negatively correlated with the risk of high Lp(a), with OR (95%CI) of 0.73 (0.60-0.89) for elevated triglyceride group, and OR (95%CI) of 0.60 (0.50-0.72) for the Apo A1/B ratio increase group (linear trend test P≤0.001 except for Apo A1). However, no correlation was found between Lp(a) levels and lifestyle factors such as diet, smoking, and physical activity. Conclusions: Lp(a) levels were associated with sex and abdominal obesity, but less with lifestyle behaviors.

Genetically determined blood pressure, antihypertensive drug classes, and frailty: A Mendelian randomization study.

Observational studies have suggested that the use of antihypertensive drugs was associated with the risk of frailty; however, these findings may be biased by confounding and reverse causality. This study aimed to explore the effect of genetically predicted lifelong lowering blood pressure (BP) through different antihypertensive medications on frailty. One-sample Mendelian randomization (MR) and summary data-based MR (SMR) were applied. We utilized two kinds of genetic instruments to proxy the antihypertensive medications, including genetic variants within or nearby drugs target genes associated with systolic/diastolic BP, and expression level of the corresponding gene. Among 298,618 UK Biobank participants, one-sample MR analysis observed that genetically proxied BB use (relative risk ratios, 0.76; 95% CI, 0.65-0.90; p = 0.001) and CCB use (0.83; 0.72-0.95; p = 0.007), equivalent to a 10-mm Hg reduction in systolic BP, was significantly associated with lower risk of pre-frailty. In addition, although not statistically significant, the effect directions of systolic BP through ACEi variants (0.72; 0.39-1.33; p = 0.296) or thiazides variants (0.74; 0.53-1.03; p = 0.072) on pre-frailty were also protective. Similar results were obtained in analyses for diastolic BP. SMR of expression in artery showed that decreased expression level of KCNH2, a target gene of BBs, was associated with lower frailty index (beta -0.02, p = 2.87 × 10-4). This MR analysis found evidence that the use of BBs and CCBs was potentially associated with reduced frailty risk in the general population, and identified KCNH2 as a promising target for further clinical trials to prevent manifestations of frailty.

Iron Oxide Nanoparticles Inhibit Tumor Progression and Suppress Lung Metastases in Mouse Models of Breast Cancer.

Systemic exposure to starch-coated iron oxide nanoparticles (IONPs) can stimulate antitumor T cell responses, even when little IONP is retained within the tumor. Here, we demonstrate in mouse models of metastatic breast cancer that IONPs can alter the host immune landscape, leading to systemic immune-mediated disease suppression. We report that a single intravenous injection of IONPs can inhibit primary tumor growth, suppress metastases, and extend survival. Gene expression analysis revealed the activation of Toll-like receptor (TLR) pathways involving signaling via Toll/Interleukin-1 receptor domain-containing adaptor-inducing IFN-ß (TRIF), a TLR pathway adaptor protein. Requisite participation of TRIF in suppressing tumor progression was demonstrated with histopathologic evidence of upregulated IFN-regulatory factor 3 (IRF3), a downstream protein, and confirmed in a TRIF knockout syngeneic mouse model of metastatic breast cancer. Neither starch-coated polystyrene nanoparticles lacking iron, nor iron-containing dextran-coated parenteral iron replacement agent, induced significant antitumor effects, suggesting a dependence on the type of IONP formulation. Analysis of multiple independent clinical databases supports a hypothesis that upregulation of TLR3 and IRF3 correlates with increased overall survival among breast cancer patients. Taken together, these data support a compelling rationale to re-examine IONP formulations as harboring anticancer immune (nano)adjuvant properties to generate a therapeutic benefit without requiring uptake by cancer cells.

Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes.

OBJECTIVE: Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. RESULTS: Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. CONCLUSIONS: Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.

Correlates and consequences of atrial fibrillation in a prospective study of 25 000 participants in the China Kadoorie Biobank.

Aims: The prevalence of atrial fibrillation (AF) is positively correlated with prior cardiovascular diseases (CVD) and CVD risk factors but is lower in Chinese than Europeans despite their higher burden of CVD. We examined the prevalence and prognosis of AF and other electrocardiogram (ECG) abnormalities in the China Kadoorie Biobank. Methods and results: A random sample of 25 239 adults (mean age 59.5 years, 62% women) had a 12-lead ECG recorded and interpreted using a Mortara VERITAS™ algorithm in 2013-14. Participants were followed up for 5 years for incident stroke, ischaemic heart disease, heart failure (HF), and all CVD, overall and by CHA2DS2-VASc scores, age, sex, and area. Overall, 1.2% had AF, 13.6% had left ventricular hypertrophy (LVH), and 28.1% had ischaemia (two-thirds of AF cases also had ischaemia or LVH). The prevalence of AF increased with age, prior CVD, and levels of CHA2DS2-VASc scores (0.5%, 1.3%, 2.1%, 2.9%, and 4.4% for scores <2, 2, 3, 4, and ≥5, respectively). Atrial fibrillation was associated with two-fold higher hazard ratios (HR) for CVD (2.15; 95% CI, 1.71-2.69) and stroke (1.88; 1.44-2.47) and a four-fold higher HR for HF (3.79; 2.21-6.49). The 5-year cumulative incidence of CVD was comparable for AF, prior CVD, and CHA2DS2-VASc scores ≥ 2 (36.7% vs. 36.2% vs. 37.7%, respectively) but was two-fold greater than for ischaemia (19.4%), LVH (18.0%), or normal ECG (14.1%), respectively. Conclusion: The findings highlight the importance of screening for AF together with estimation of CHA2DS2-VASc scores for prevention of CVD in Chinese adults.

Elevated blood remnant cholesterol and triglycerides are causally related to the risks of cardiometabolic multimorbidity.

The connection between triglyceride-rich lipoproteins and cardiometabolic multimorbidity, characterized by the concurrence of at least two of type 2 diabetes, ischemic heart disease, and stroke, has not been definitively established. We aim to examine the prospective associations between serum remnant cholesterol, triglycerides, and the risks of progression from first cardiometabolic disease to multimorbidity via multistate modeling in the UK Biobank. We also evaluate the causality of these associations via Mendelian randomization using 13 biologically relevant SNPs as the genetic instruments. Here we show that elevated remnant cholesterol and triglycerides are significantly associated with gradually higher risks of cardiometabolic multimorbidity, particularly the progression of ischemic heart disease to the multimorbidity of ischemic heart disease and type 2 diabetes. These results advocate for effective management of remnant cholesterol and triglycerides as a potential strategy in mitigating the risks of cardiometabolic multimorbidity.

Iron Status and Risk of Heart Disease, Stroke, and Diabetes: A Mendelian Randomization Study in European Adults.

BACKGROUND: The relevance of iron status biomarkers for coronary artery disease (CAD), heart failure (HF), ischemic stroke (IS), and type 2 diabetes (T2D) is uncertain. We compared the observational and Mendelian randomization (MR) analyses of iron status biomarkers and hemoglobin with these diseases. METHODS AND RESULTS: Observational analyses of hemoglobin were compared with genetically predicted hemoglobin with cardiovascular diseases and diabetes in the UK Biobank. Iron biomarkers included transferrin saturation, serum iron, ferritin, and total iron binding capacity. MR analyses assessed associations with CAD (CARDIOGRAMplusC4D [Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus The Coronary Artery Disease Genetics], n=181 522 cases), HF (HERMES [Heart Failure Molecular Epidemiology for Therapeutic Targets), n=115 150 cases), IS (GIGASTROKE, n=62 100 cases), and T2D (DIAMANTE [Diabetes Meta-Analysis of Trans-Ethnic Association Studies], n=80 154 cases) genome-wide consortia. Observational analyses demonstrated J-shaped associations of hemoglobin with CAD, HF, IS, and T2D. In contrast, MR analyses demonstrated linear positive associations of higher genetically predicted hemoglobin levels with 8% higher risk per 1 SD higher hemoglobin for CAD, 10% to 13% for diabetes, but not with IS or HF in UK Biobank. Bidirectional MR analyses confirmed the causal relevance of iron biomarkers for hemoglobin. Further MR analyses in global consortia demonstrated modest protective effects of iron biomarkers for CAD (7%-14% lower risk for 1 SD higher levels of iron biomarkers), adverse effects for T2D, but no associations with IS or HF. CONCLUSIONS: Higher levels of iron biomarkers were protective for CAD, had adverse effects on T2D, but had no effects on IS or HF. Randomized trials are now required to assess effects of iron supplements on risk of CAD in high-risk older people.

AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification.

The design of neural networks typically involves trial-and-error, a time-consuming process for obtaining an optimal architecture, even for experienced researchers. Additionally, it is widely accepted that loss functions of deep neural networks are generally non-convex with respect to the parameters to be optimised. We propose the Layer-wise Convex Theorem to ensure that the loss is convex with respect to the parameters of a given layer, achieved by constraining each layer to be an overdetermined system of non-linear equations. Based on this theorem, we developed an end-to-end algorithm (the AutoNet) to automatically generate layer-wise convex networks (LCNs) for any given training set. We then demonstrate the performance of the AutoNet-generated LCNs (AutoNet-LCNs) compared to state-of-the-art models on three electrocardiogram (ECG) classification benchmark datasets, with further validation on two non-ECG benchmark datasets for more general tasks. The AutoNet-LCN was able to find networks customised for each dataset without manual fine-tuning under 2 GPU-hours, and the resulting networks outperformed the state-of-the-art models with fewer than 5% parameters on all the above five benchmark datasets. The efficiency and robustness of the AutoNet-LCN markedly reduce model discovery costs and enable efficient training of deep learning models in resource-constrained settings.

CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

Tumor circadian clock strength influences metastatic potential and predicts patient prognosis in luminal A breast cancer.

Studies in shift workers and model organisms link circadian disruption to breast cancer. However, molecular circadian rhythms in noncancerous and cancerous human breast tissues and their clinical relevance are largely unknown. We reconstructed rhythms informatically, integrating locally collected, time-stamped biopsies with public datasets. For noncancerous breast tissue, inflammatory, epithelial-mesenchymal transition (EMT), and estrogen responsiveness pathways show circadian modulation. Among tumors, clock correlation analysis demonstrates subtype-specific changes in circadian organization. Luminal A organoids and informatic ordering of luminal A samples exhibit continued, albeit dampened and reprogrammed rhythms. However, CYCLOPS magnitude, a measure of global rhythm strength, varied widely among luminal A samples. Cycling of EMT pathway genes was markedly increased in high-magnitude luminal A tumors. Surprisingly, patients with high-magnitude tumors had reduced 5-y survival. Correspondingly, 3D luminal A cultures show reduced invasion following molecular clock disruption. This study links subtype-specific circadian disruption in breast cancer to EMT, metastatic potential, and prognosis.

Association between health insurance cost-sharing and choice of hospital tier for cardiovascular diseases in China: a prospective cohort study.

Background: Hospitals in China are classified into tiers (1, 2 or 3), with the largest (tier 3) having more equipment and specialist staff. Differential health insurance cost-sharing by hospital tier (lower deductibles and higher reimbursement rates in lower tiers) was introduced to reduce overcrowding in higher tier hospitals, promote use of lower tier hospitals, and limit escalating healthcare costs. However, little is known about the effects of differential cost-sharing in health insurance schemes on choice of hospital tiers. Methods: In a 9-year follow-up of a prospective study of 0.5 M adults from 10 areas in China, we examined the associations between differential health insurance cost-sharing and choice of hospital tiers for patients with a first hospitalisation for stroke or ischaemic heart disease (IHD) in 2009-2017. Analyses were performed separately in urban areas (stroke: n = 20,302; IHD: n = 19,283) and rural areas (stroke: n = 21,130; IHD: n = 17,890), using conditional logit models and adjusting for individual socioeconomic and health characteristics. Findings: About 64-68% of stroke and IHD cases in urban areas and 27-29% in rural areas chose tier 3 hospitals. In urban areas, higher reimbursement rates in each tier and lower tier 3 deductibles were associated with a greater likelihood of choosing their respective hospital tiers. In rural areas, the effects of cost-sharing were modest, suggesting a greater contribution of other factors. Higher socioeconomic status and greater disease severity were associated with a greater likelihood of seeking care in higher tier hospitals in urban and rural areas. Interpretation: Patient choice of hospital tiers for treatment of stroke and IHD in China was influenced by differential cost-sharing in urban areas, but not in rural areas. Further strategies are required to incentivise appropriate health seeking behaviour and promote more efficient hospital use. Funding: Wellcome Trust, Medical Research Council, British Heart Foundation, Cancer Research UK, Kadoorie Charitable Foundation, China Ministry of Science and Technology, and National Natural Science Foundation of China.

Global heterogeneity in folic acid fortification policies and implications for prevention of neural tube defects and stroke: a systematic review.

Background: Folic acid (pteroylmonoglutamic acid) supplements are highly effective for prevention of neural tube defects (NTD) prompting implementation of mandatory or voluntary folic acid fortification for prevention of NTDs. We used plasma folate levels in population studies by country and year to compare effects of folic acid fortification types (mandatory or voluntary folic acid fortification policies) on plasma folate levels, NTD prevalence and stroke mortality rates. Methods: We conducted systematic reviews of (i) implementation of folic acid fortification in 193 countries that were member states of the World Health Organization by country and year, and (ii) estimated population mean plasma folate levels by year and type of folic acid fortification. We identified relevant English language reports published between Jan 1, 1990 and July 31, 2023 using Google Scholar, Medline, Embase and Global Health. Eligibility criteria were observational or interventional studies with >1000 participants. Studies of pregnant women or children <15 years were excluded. Using an ecological study design, we examined the associations of folic acid fortification types with NTD prevalence (n = 108 studies) and stroke mortality rates (n = 3 countries). Findings: Among 193 countries examined up to 31 July 2023, 69 implemented mandatory folic acid fortification, 47 had voluntary fortification, but 77 had no fortification (accounting for 32%, 53% and 15% of worldwide population, respectively). Mean plasma folate levels were 36, 21 and 17 nmol/L in populations with mandatory, voluntary and no fortification, respectively (and proportions with mean folate levels >25 nmol/L were 100%, 15% and 7%, respectively). Among 75 countries with NTD prevalence, mean (95% CI) prevalence per 10,000 population were 4.19 (4.11-4.28), 7.61 (7.47-7.75) and 9.66 (9.52-9.81) with mandatory, voluntary and no folic acid fortification, respectively. However, age-standardised trends in stroke mortality rates were unaltered by the introduction of folic acid fortification. Interpretation: There is substantial heterogeneity in folic acid fortification policies worldwide where folic acid fortification are associated with 50-100% higher population mean plasma folate levels and 25-50% lower NTD prevalence compared with no fortification. Many thousand NTD pregnancies could be prevented yearly if all countries implemented mandatory folic acid fortification. Further trials of folic acid for stroke prevention are required in countries without effective folic acid fortification policies. Funding: Medical Research Council (UK) and British Heart Foundation.

PCSK9 genetic variants and risk of vascular and non-vascular diseases in Chinese and UK populations.

AIMS: Lowering low-density lipoprotein cholesterol (LDL-C) through PCSK9 inhibition represents a new therapeutic approach to preventing and treating cardiovascular disease (CVD). Phenome-wide analyses of PCSK9 genetic variants in large biobanks can help to identify unexpected effects of PCSK9 inhibition. METHODS AND RESULTS: In the prospective China Kadoorie Biobank, we constructed a genetic score using three variants at the PCSK9 locus associated with directly measured LDL-C [PCSK9 genetic score (PCSK9-GS)]. Logistic regression gave estimated odds ratios (ORs) for PCSK9-GS associations with CVD and non-CVD outcomes, scaled to 1 SD lower LDL-C. PCSK9-GS was associated with lower risks of carotid plaque [n = 8340 cases; OR = 0.61 (95% confidence interval: 0.45-0.83); P = 0.0015], major occlusive vascular events [n = 15 752; 0.80 (0.67-0.95); P = 0.011], and ischaemic stroke [n = 11 467; 0.80 (0.66-0.98); P = 0.029]. However, PCSK9-GS was also associated with higher risk of hospitalization with chronic obstructive pulmonary disease [COPD: n = 6836; 1.38 (1.08-1.76); P = 0.0089] and with even higher risk of fatal exacerbations amongst individuals with pre-existing COPD [n = 730; 3.61 (1.71-7.60); P = 7.3 × 10-4]. We also replicated associations for a PCSK9 variant, reported in UK Biobank, with increased risks of acute upper respiratory tract infection (URTI) [pooled OR after meta-analysis of 1.87 (1.38-2.54); P = 5.4 × 10-5] and self-reported asthma [pooled OR of 1.17 (1.04-1.30); P = 0.0071]. There was no association of a polygenic LDL-C score with COPD hospitalization, COPD exacerbation, or URTI. CONCLUSION: The LDL-C-lowering PCSK9 genetic variants are associated with lower risk of subclinical and clinical atherosclerotic vascular disease but higher risks of respiratory diseases. Pharmacovigilance studies may be required to monitor patients treated with therapeutic PCSK9 inhibitors for exacerbations of respiratory diseases or respiratory tract infections. LAY SUMMARY: Genetic analyses of over 100 000 participants of the China Kadoorie Biobank, mimicking the effect of new drugs intended to reduce cholesterol by targeting the PCSK9 protein, have identified potential severe effects of lower PCSK9 activity in patients with existing respiratory disease.PCSK9 genetic variants that are associated with lower cholesterol and reduced rates of cardiovascular disease are also associated with increased risk of a range of respiratory diseases, including asthma, upper respiratory tract infections, and hospitalization with chronic obstructive pulmonary disease (COPD).These genetic variants are not associated with whether or not individuals have COPD; instead, they are specifically associated with an increase in the chance of those who already have COPD being hospitalized and even dying, suggesting that careful monitoring of such patients should be considered during development of and treatment with anti-PCSK9 medication.

Denture use and risk for cardiometabolic disease: observational and Mendelian randomization analyses.

AIMS: Denture use may potentially increase the risk of cardiometabolic diseases (CMDs), but the casual relevance and strength of the associations are currently unknown. METHODS AND RESULTS: A total of 495 938 participants from the UK Biobank were included in the observational analyses. Linkage disequilibrium score (LDSC) regression and Mendelian randomization analyses were employed to estimate genetic correlation and the associations between the genetic liability for denture use with coronary artery disease, myocardial infarction, heart failure (HF), any stroke (AS), ischaemic stroke, haemorrhagic stroke, type 2 diabetes (T2D), and related clinical risk factors. In observational analysis, denture use was associated with 14-25% higher risks of various CMDs. The LDSC analysis found that denture use showed a positive genetic correlation with CMDs (rg 0.21-0.38). Genetic liability for denture use was associated with an elevated risk of HF [odds ratio: 1.49 (1.20-1.83)] and T2D [1.11 (1.01-1.24)]. By integrating genetic summary data of denture use with the sum of decayed, missing, and filled tooth surfaces (DMFS), a clinical measure of dental caries obtained from an independent source, genetically determined denture use/DMFS was also associated with an elevated risk of AS [1.21 (1.04-1.40)]. Furthermore, genetically predicted denture use/DMFS was significantly associated with established cardiometabolic risk factors, including HDL cholesterol, triglycerides, waist circumference, waist-to-hip ratio, and height. CONCLUSION: Our study supported potential causal associations between the genetic liability for denture use and risks for HF, AS, T2D, and related clinical risk factors. These findings may inform prevention and intervention strategies targeting dental diseases and CMDs.

This study examined the association of denture use with cardiometabolic diseases (CMDs) and related clinical risk factors through Mendelian randomization analyses using data from UK Biobank and published consortia. Genetic liability for denture use was associated with an 11­49% higher risk of heart failure, stroke, and type 2 diabetes.The potential causal relationship between denture use and CMDs was further strengthened by the associations of denture use with HDL cholesterol, triglycerides, waist circumference, waist-to-hip ratio, and height, which are among the major risk factors of CMDs.