High Body Mass Index is Not Associated With Coronary Artery Disease in Angina Patients With Chronic Kidney Disease: A Coronary Angiography Study

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease

Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

Association between Parathyroid Hormone, 25 (OH) Vitamin D, and Chronic Kidney Disease: A Population-Based Study

Identification of the accurate risk factor for CKD remains mandatory to combat the high prevalence of diseases. Growing evidence suggests the association of serum vitamin D with diverse health conditions. However, the relationship between vitamin D, intact parathyroid hormone (PTH), and calcium-phosphate metabolism and development of CKD remains controversial. We conduct this cross-sectional observational study to investigate the association between serum 25 (OH) vitamin D, intact PTH, and calcium and phosphate levels with eGFR and albuminuria, as a surrogate marker of CKD, in a community population. A total of 4080 participants were recruited. The mean age was 58.4 ± 13.3 years and 1480 (36.3%) were men. The mean eGFR was 94.1 ± 26.3 mL/min/1.73 m². The prevalence of CKD was 19.8%. Serum 25 (OH) vitamin D and log intact PTH levels were inversely correlated with eGFR but positively correlated with log albuminuria. Logistic regression analysis identified the log intact PTH as an independent factor associated with eGFR ≤ 60 mL/min/1.73 m² and proteinuria. This association was consistent when serum intact PTH was analyzed as continuous as well as categorical variables (as hyperparathyroidism). The relationship remains significant using resampling subset analysis with comparable baseline characteristics and adjustment for 25 (OH) vitamin D, calcium, and phosphate levels. This finding warranted further research to clarify the causal relationship of PTH/25 (OH) vitamin D with the risk of CKD in the general population.

Short stature is associated with coronary heart disease: a systematic review of the literature and a meta-analysis

AIMS

The aim of this study was to assess the relationship between short stature and coronary heart disease (CHD) morbidity and mortality.

METHODS AND RESULTS

We performed a systematic search from MEDLINE, PREMEDLINE, and All EBM Reviews as well as from a reference list of relevant articles. We used SPICO (Study design, Patient, Intervention, Control-intervention, Outcome) criteria. The methodological quality of studies was analysed by modified Borghoust criteria. From a total of 1907 articles, we selected 52 studies comprising population-based follow-up studies and patient cohorts followed after a CHD event, as well as case-control studies with height either as a continuous or categorical variable, totalling 3 012 747 individuals. The short ones were below 160.5 cm and tall ones over 173.9 cm on average. Among the shortest height category, the relative risks were 1.35 (95% CI 1.25-1.44) for all-cause mortality, 1.55 (1.37-1.74) for all cardiovascular disease (CVD) mortality, 1.49 (1.33-1.67) for CHD, and 1.52 (1.28-1.81) for myocardial infarction when compared with those within the highest height category. The mean relative risk was 1.46 (1.37-1.55). Short stature was associated with increased cardiovascular morbidity and mortality in both genders.

CONCLUSION

The relationship between short stature and CVD appears to be a real one. On the basis of comparison, adults within the shortest category had an approximately 50% higher risk of CHD morbidity and mortality than tall individuals.

Low serum acylated ghrelin levels are associated with the development of cardiovascular disease in hemodialysis patients

BACKGROUND

Ghrelin has a protective effect on endothelial cells. Endothelial cell dysfunction is associated with cardiovascular disease (CVD) and CVD remains the leading cause of morbidity in hemodialysis (HD) patients. Acylated ghrelin (A-Ghr) is the functional form of ghrelin, so we hypothesized that A-Ghr is associated with the occurrence of CVD in HD patients.

METHODS

We conducted a prospective cohort study in 412 HD patients. The cohort was sub-grouped into low and high A-Ghr groups according to the median A-Ghr level of 4.88 pg/mL. The association between the low/high A-Ghr groups and the incidence of CVD were analyzed.

RESULTS

The HD patients in a low A-Ghr group had a greater risk of incidental CVD than those in a high A-Ghr ghrelin. This association remained significant after the adjustment for possible confounding factors, including age, gender, HD duration, BMI, diabetes, albumin, nPCR and Charlson's comorbidity index score.

CONCLUSION

It appears that a low serum A-Ghr level is associated with the development of CVD in HD patients.