Cardiovascular diseases (CVDs) and cerebrovascular diseases (CeVDs) are closely related vascular diseases, sharing common cardiometabolic risk factors (RFs). Although pleiotropic genetic variants of these two diseases have been reported, their underlying pathological mechanisms are still unclear. Leveraging GWAS summary data and using genetic correlation, pleiotropic variants identification, and colocalization analyses, we identified 11 colocalized loci for CVDs-CeVDs-BP (blood pressure), CVDs-CeVDs-LIP (lipid traits), and CVDs-CeVDs-cIMT (carotid intima-media thickness) triplets. No shared causal loci were found for CVDs-CeVDs-T2D (type 2 diabetes) or CVDs-CeVDs-BMI (body mass index) triplets. The 11 loci were mapped to 12 genes, namely CASZ1, CDKN1A, TWIST1, CDKN2B, ABO, SWAP70, SH2B3, LRCH1, FES, GOSR2, RPRML, and LDLR, where both GOSR2 and RPRML were mapped to one locus. They were enriched in pathways related to cellular response to external stimulus and regulation of the phosphate metabolic process and were highly expressed in endothelial cells, epithelial cells, and smooth muscle cells. Multi-omics analysis revealed methylation of two genes (CASZ1 and LRCH1) may play a causal role in the genetic pleiotropy. Notably, these pleiotropic loci are highly enriched in the targets of antihypertensive drugs, which further emphasizes the role of the blood pressure regulation pathway in the shared etiology of CVDs and CeVDs.

Background: The paucity of evidence on longitudinal and consecutive recordings of physical activity (PA) and blood pressure (BP) under real-life conditions and their relationships is a vital research gap that needs to be addressed. Objective: This study aims to (1) investigate the short-term relationship between device-measured step volume and BP; (2) explore the joint effects of step volume and variability on BP; and (3) examine whether the association patterns between PA and BP varied across sex, hypertension status, and chronic condition status. Methods: This study used PA data of a prospective cohort of 3070 community-dwelling older adults derived from a mobile health app. Daily step counts, as a proxy of step volume, were derived from wearable devices between 2018 and 2022 and categorized into tertiles (low, medium, and high). Step variability was assessed using the SD of daily step counts. Consecutive daily step count recordings within 0 to 6 days preceding each BP measurement were analyzed. Generalized estimation equation models were used to estimate the individual and joint associations of daily step volume and variability with BP. Stratified analyses by sex, the presence of hypertension, and the number of morbidities were further conducted. Results: A total of 3070 participants, with a median age of 72 (IQR 67-77) years and 71.37% (2191/3070) women, were included. Participants walked a median of 7580 (IQR 4972-10,653) steps and 5523 (IQR 3590-7820) meters per day for a total of 592,597 person-days of PA monitoring. Our results showed that higher levels of daily step volume were associated with lower BP (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure). Compared with participants with low step volume (daily step counts <6000/d) and irregular steps, participants with high step volume (≥9500/d) and regular steps showed the strongest decrease in systolic BP (–1.69 mm Hg, 95% CI –2.2 to –1.18), while participants with medium step volume (6000/d to <9500/d) and regular steps were associated with the lowest diastolic BP (–1.067 mm Hg, 95% CI –1.379 to –0.755). Subgroup analyses indicated generally greater effects on women, individuals with normal BP, and those with only 1 chronic disease, but the effect pattern was varied and heterogeneous between participants with different characteristics. Conclusions: Increased step volume demonstrated a substantial protective effect on BP among older adults with chronic conditions. Furthermore, the beneficial association between step volume and BP was enhanced by regular steps, suggesting potential synergistic protective effects of both increased step volume and step regularity. Targeting both step volume and variability through PA interventions may yield greater benefits in BP control, particularly among participants with hypertension and a higher chronic disease burden.

Emerging evidence implicates the gut microbiota in type 2 diabetes (T2D) development and their association with multiple host factors. This study aimed to explore the differences in gut microbiota between T2D patients and non-diabetic controls, and to estimate the relationship between host factors and specific microbial signatures in T2D. 16S rRNA gene sequencing was performed on fecal samples from 508 T2D patients and 1,538 controls in a Chinese population. We observed significant differences in microbial composition and diversity between T2D and controls, with 20 microbial signatures associated with T2D. Microbial co-occurrence networks revealed that T2D patients exhibited a distinct microbiome signatures pattern. Random forest models incorporating these microbial signatures and covariates improved accuracy of T2D classification. Furthermore, geographic region, diet habits of different staple food sources and coffee drinking habit, and common comorbidities, such as hypertension, were associated with specific microbial signatures. Our study highlights the potential role of gut microbiota in T2D pathogenesis and provides clues that specific microbial signatures may serve as biomarkers for T2D diagnosis and personalized management in Chinese populations. The findings indicate that modifiable factors including dietary factors, living regions and common comorbidities may influence the gut microbiota, thereby affecting T2D development.

Coronary artery disease (CAD) is a common comorbidity of type 2 diabetes mellitus (T2DM). However, the pathophysiology connecting these two phenotypes remains to be further understood. Combined analysis in multi-ethnic populations can help contribute to deepening our understanding of biological mechanisms caused by shared genetic loci. We applied genetic correlation analysis and then performed conditional and joint association analyses in Chinese, Japanese, and European populations to identify the genetic variants jointly associated with CAD and T2DM. Next, the associations between genes and the two traits were also explored. Finally, fine-mapping and functional enrichment analysis were employed to identify the potential causal variants and pathways. Genetic correlation results indicated significant genetic overlap between CAD and T2DM in the three populations. Over 10,000 shared signals were identified, and 587 were shared by East Asian and European populations. Fifty-six novel shared genes were found to have significant effects on both CAD and T2DM. Most loci were fine-mapped to plausible causal variant sets. Several similarities and differences of the involved genes in GO terms and KEGG pathways were revealed across East Asian and European populations. These findings highlight the importance of immunoregulation, neuroregulation, heart development, and the regulation of glucose metabolism in shared etiological mechanisms between CAD and T2DM.

Variations in lipid levels are the result of combinations of genetic and environmental factors. We aim to investigate the indirect effect between siblings of the three polymorphisms of ADIPOQ on serum lipid levels in rural Chinese populations. A total of 2571 sibling pairs were enrolled as study participants. A generalized estimating equation was used to accommodate a family-based design. We used stratified analysis to detect sex combination differences in the indirect genetic effect. We found a significant association between the number of altered risk alleles of rs182052 and ego lipid levels of TG (β = 0.177, P = 0.003), TC (β = 0.140, P = 0.004) and LDL-C (β = 0.098, P = 0.014). Ego and altered genotypes of rs182052 demonstrated a joint effect on ego lipid levels of TC (β = 0.212, P = 0.019), HDL-C (β = 0.099, P = 0.002) and LDL-C (β = 0.177, P = 0.013) in recessive inheritance mode. In opposite-sex siblings, the altered GG genotype of rs182052 increased the ego lipid levels. Thus, our findings demonstrate that ADIPOQ has an indirect genetic effect on lipid levels in sibling pairs, and there are sex-combination differences in the indirect genetic effect in siblings.

Background/Objectives: Genes and environments were transmitted across generations. Parents’ genetics influence the environments of their offspring; these two modes of inheritance can produce a genetic nurture effect, also known as indirect genetic effects. Such indirect effects may partly account for estimated genetic variance in T2D. However, the well-established specific genetic risk factors about genetic nurture effect for T2D are not fully understood. This study aimed to investigate the genetic nurture effect on type 2 diabetes and reveal the potential underlying mechanism using publicly available data. Methods: Whole-genome genotyping data of 881 offspring and/or their parents were collected. We assessed SNP-level, gene-based, and pathway-based associations for different types of genetic effects. Results: Rs3805116 (β: 0.54, p = 4.39 × 10⁻⁸) was significant for paternal genetic nurture effects. MRPS33 (p = 1.58 × 10⁻⁶), PIH1D2 (p = 6.76 × 10⁻⁷), and SD1HD (p = 2.67 × 10⁻⁶) revealed significantly positive paternal genetic nurture effects. Five ontologies were identified as enrichment in both direct and indirect genetic effects, including flavonoid metabolic process and antigen processing and presentation via the MHC class Ib pathway. Two pathways were only enriched in paternal genetic nurture effects, including the transforming growth factor beta pathway. Tissue enrichment of type 2 diabetes-associated genes on different genetic effect types was performed using publicly available gene expression data from the Human Protein Atlas database. We observed significant gene enrichment in paternal genetic nurture effects in the gallbladder, smooth muscle, and adrenal gland tissues. Conclusions: MRPS33, PIH1D2, and SD1HD are associated with increased T2D risk through the environment influenced by paternal genotype, suggesting a novel perspective on paternal contributions to the T2D predisposition.