RESUMO
Current genome-wide association studies (GWAS) for kidney function lack ancestral diversity, limiting the applicability to broader populations. The East-Asian population is especially under-represented, despite having the highest global burden of end-stage kidney disease. We conducted a meta-analysis of multiple GWASs (n = 244,952) on estimated glomerular filtration rate and a replication dataset (n = 27,058) from Taiwan and Japan. This study identified 111 lead SNPs in 97 genomic risk loci. Functional enrichment analyses revealed that variants associated with F12 gene and a missense mutation in ABCG2 may contribute to chronic kidney disease (CKD) through influencing inflammation, coagulation, and urate metabolism pathways. In independent cohorts from Taiwan (n = 25,345) and the United Kingdom (n = 260,245), polygenic risk scores (PRSs) for CKD significantly stratified the risk of CKD (p < 0.0001). Further research is required to evaluate the clinical effectiveness of PRSCKD in the early prevention of kidney disease.
Assuntos
Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Taxa de Filtração Glomerular , Polimorfismo de Nucleotídeo Único , Insuficiência Renal Crônica , Humanos , Taiwan/epidemiologia , Japão/epidemiologia , Insuficiência Renal Crônica/genética , Insuficiência Renal Crônica/epidemiologia , Masculino , Feminino , Membro 2 da Subfamília G de Transportadores de Cassetes de Ligação de ATP/genética , Rim/fisiopatologia , Proteínas de Neoplasias/genética , Pessoa de Meia-Idade , Herança Multifatorial/genética , Mutação de Sentido Incorreto , Fatores de Risco , Adulto , Povo Asiático/genética , IdosoRESUMO
Midurethral sling surgery is the current gold standard worldwide for stress urinary incontinence (SUI) surgery, with over 90% of surgeons worldwide using the midurethral sling for SUI between 2008 and 2018. However, concerns surround mesh-related adverse events associated with the midurethral sling. The decision to use the midurethral sling for surgical treatment has become a challenging one for clinicians, surgeons and patients. We sought to determine the factors for 5-year complications after midurethral sling surgery, to improve the clinical decision-making process. Records were reviewed from a total of 1961 female patients who underwent their first midurethral sling surgery for SUI between 2003 and 2018 at a single teaching hospital in Taiwan. A multivariable Cox proportional hazard model calculated the hazard ratios of risk factors for surgical complications, after adjusting for confounders. Surgical complications (i.e., secondary surgery and urinary retention) occurred in 93 (4.7%) patients within 5 years following the index operations. These patients were more likely to be older, to have a history of menopausal syndrome within 1 year prior to the index operation, a medication history of oral antidiabetic drug use, hormone replacement therapy (HRT), slower average flow rate, and longer voiding time compared with patients without surgical complications. In the multivariate analysis, HRT (adjusted hazard ratio, 1.787; 95% confidence interval, 1.011-3.158, p = 0.04) was significantly associated with surgical complications at 5 years, after adjusting for age, gender, diabetes, menopause syndrome, average flow rate, and sling type. Our findings suggest that a medication history of HRT may be a risk factor associated with surgical complications, especially urinary retention, at 5 years in women undergoing midurethral sling surgery for SUI.
Assuntos
Slings Suburetrais , Incontinência Urinária por Estresse , Retenção Urinária , Feminino , Humanos , Estudos Retrospectivos , Retenção Urinária/etiologia , Slings Suburetrais/efeitos adversos , Incontinência Urinária por Estresse/etiologia , Taiwan/epidemiologia , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/etiologia , Complicações Pós-Operatórias/cirurgia , Fatores de Risco , Resultado do TratamentoRESUMO
BACKGROUND: The prognostic role of the cardiothoracic ratio (CTR) in chronic kidney disease (CKD) remains undetermined. METHODS: We conducted a retrospective cohort study of 3117 patients with CKD aged 18-89 years who participated in an Advanced CKD Care Program in Taiwan between 2003 and 2017 with a median follow up of 1.3(0.7-2.5) and 3.3(1.8-5.3) (IQR) years for outcome of end-stage renal disease (ESRD) and overall death, respectively. We developed a machine learning (ML)-based algorithm to calculate the baseline and serial CTRs, which were then used to classify patients into trajectory groups based on latent class mixed modelling. Association and discrimination were evaluated using multivariable Cox proportional hazards regression analyses and C-statistics, respectively. RESULTS: The median (interquartile range) age of 3117 patients is 69.5 (59.2-77.4) years. We create 3 CTR trajectory groups (low [30.1%], medium [48.1%], and high [21.8%]) for the 2474 patients with at least 2 CTR measurements. The adjusted hazard ratios for ESRD, cardiovascular mortality, and all-cause mortality in patients with baseline CTRs ≥0.57 (vs CTRs <0.47) are 1.35 (95% confidence interval, 1.06-1.72), 2.89 (1.78-4.71), and 1.50 (1.22-1.83), respectively. Similarly, greater effect sizes, particularly for cardiovascular mortality, are observed for high (vs low) CTR trajectories. Compared with a reference model, one with CTR as a continuous variable yields significantly higher C-statistics of 0.719 (vs 0.698, P = 0.04) for cardiovascular mortality and 0.697 (vs 0.693, P < 0.001) for all-cause mortality. CONCLUSIONS: Our findings support the real-world prognostic value of the CTR, as calculated by a ML annotation tool, in CKD. Our research presents a methodological foundation for using machine learning to improve cardioprotection among patients with CKD.
An enlarged heart occurs during various medical conditions and can result in early death. However, it is unclear whether this is also the case in patients with chronic kidney disease (CKD). Although the size of the heart can be measured on chest X-rays, this process is time consuming. We used artificial intelligence to quantify the heart size of 3117 CKD patients based on their chest X-rays within hours. We found that CKD patients with an enlarged heart were more likely to develop end-stage kidney disease or die. This could improve monitoring of CKD patients with an enlarged heart and improve their care.