Left ventricular ejection fraction was measured by radionuclide left ventriculography before and immediately after hemodialysis in 20 uremic patients, 11 of whom presented with congestive heart failure. Ejection fraction and contraction were normal in 15 patients (Group A), six of whom had signs of congestive failure; they were abnormal in five patients (Group B), all of whom were in clinical heart failure. Mean arterial pressure and body weight decreased by a similar amount after dialysis in both groups, and heart rate did not change. In Group A ejection fraction was unchanged by dialysis (0.63±0.06 before vs. 0.62±0.09 after) (mean ±S.D.), but in Group B it was improved significantly (0.32±0.04 before vs. 0.44±0.08 after) (P<0.01). In three patients in Group B cardiomegaly and ejection fraction returned to normal with long-term hemodialysis. In end-stage renal failure, radionuclide left ventriculography can separate patients with circulatory congestion due to fluid overload from patients with left ventricular dysfunction in whom hemodialysis can provide immediate and long-term improvement. (N Engl J Med. 1980; 302:547–51.)
Abstract Background Obstructive sleep apnoea (OSA) is a heterogeneous disorder with certain phenotypes at increased risk of major adverse cardiovascular events (MACE). We investigated whether symptom subtypes and/or symptom burden are useful predictors of MACE risk in severe OSA. Method In a longitudinal sleep clinic cohort with apnoea-hypopnoea index ≥30 events/hour (n=1767), we investigated 19 OSA-related symptoms across four symptom domains (upper airway [UA], insomnia and disturbed sleep, morning, and daytime sleepiness) and the Epworth Sleepiness Scale score. Latent class analysis identified five symptom subtypes. A symptom burden score (0–8) was developed by selecting the two symptoms from each domain most strongly associated with MACE. Multivariable-adjusted associations of subtypes and symptom burden with future MACE were investigated using Cox regressions. Results Over a median follow-up of 7 years, 18.7% developed MACE. Relative to the moderately sleepy subtype, the disturbed sleep (adjusted hazard ratio [HR], 1.65; 95%CI, 1.15–2.37) and UA symptoms predominant (HR, 1.57; 95%CI, 1.05–2.34) subtypes showed increased MACE risk. There was a graded increase in MACE risk with increasing symptom burden score (adjusted p for linear trend = 0.003). Compared to patients that reported ≤2 of 8 symptoms, those with ≥7 symptoms showed an independent risk for MACE (HR, 1.77; 95%CI, 1.12–2.77). Discussion Both symptom subtypes and symptom burden may help identify severe OSA patients at increased risk of MACE. However, our novel symptom burden score may have more clinical utility as it is an easily calculated summative measure of OSA-related symptoms that allows objective comparisons across diverse patient populations.
Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.
Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Western Australian Department of Health. Background Chronic coronary heart disease (CCD) is associated with a high healthcare burden and expenditure in Western societies, yet population-level data on CCD are limited. Use of hospitalisation data to investigate CCD epidemiology has not been well explored. Our aim was to describe characteristics of patients hospitalised with CCD and to estimate the long-term risk of coronary outcomes in this cohort. Method We used Western Australian state-wide linked hospitalisation/mortality data to identify all hospitalisations for CCD, comprised stable angina (SA, ICD-10-AM I20.1-I20.9) and chronic ischaemic heart disease (IHD, I25) from 2002 – 2017. Index admissions were defined as the first SA or chronic IHD admission in the study period. 14-years of hospitalisation history data were used to identify prior medical history, including admissions for acute coronary syndromes (ACS) +/- angiography, PCI and CABG. Kaplan-Meier survival analyses were used to estimate risk of readmission for coronary outcomes and procedures using up to 15-years of follow-up from linked morbidity/mortality data. Results There were 32,557 index SA and 29,505 index chronic IHD admissions from 2002-2017. SA index case number declined by -3.9%/year while chronic IHD admissions increased steadily (+5.3%/year, age-adjusted). The mean age was 66 years for SA and chronic IHD index admissions, with women on average 3 years younger than men; median length of stay was 3 days. Women comprised a higher proportion of SA admissions (37.8%) than chronic IHD (26.9%, p<0.0001). SA admissions were more likely to be emergency admissions (45.6% vs 13.4%) and have comorbidities including diabetes, hypertension and stroke. Patients with a chronic IHD admission were more likely to undergo angiography only (65.3% vs 46.7%) or revascularisation (20.6% vs 12.9%) during the index admission. Around 1/3 of SA and chronic IHD patients had a prior ACS admission (n=9964 and 8291 respectively); around half of these prior ACS admissions occurred in the 1-year preceding the index SA and chronic IHD admissions (Figure), with the majority occurring in the 90 days immediately prior to the index admission. Following an index SA or chronic IHD admission, the risk of ACS within the initial year of follow-up was 9.4% in men and 8.6% in women with SA, and 5.6% in chronic IHD men and women. This risk increased to >20% in both groups with 15 years of follow-up. The 15-year risk of CVD mortality was 4.3% in men and 4.2% in women following a chronic IHD admission; similar long-term risks were seen following SA admissions. Conclusion The heterogeneity in clinical profile and outcomes between patients admitted for SA versus chronic IHD requires cautious interpretation of hospitalisation data to inform the epidemiology of CCD. However, while mortality risk is low, the risk of ACS readmission is high, indicating significant ongoing morbidity and healthcare burden of CCD.
Abstract Background Low levels of serum adiponectin have been linked to central obesity, insulin resistance, metabolic syndrome, and type 2 diabetes. Variants in ADIPOQ , the gene encoding adiponectin, have been shown to influence serum adiponectin concentration, and along with variants in the adiponectin receptors ( ADIPOR1 and ADIPOR2 ) have been implicated in metabolic syndrome and type 2 diabetes. This study aimed to comprehensively investigate the association of common variants in ADIPOQ, ADIPOR1 and ADIPOR2 with serum adiponectin and insulin resistance syndromes in a large cohort of European-Australian individuals. Methods Sixty-four tagging single nucleotide polymorphisms in ADIPOQ , ADIPOR1 and ADIPOR2 were genotyped in two general population cohorts consisting of 2,355 subjects, and one cohort of 967 subjects with type 2 diabetes. The association of tagSNPs with outcomes were evaluated using linear or logistic modelling. Meta-analysis of the three cohorts was performed by random-effects modelling. Results Meta-analysis revealed nine genotyped tagSNPs in ADIPOQ significantly associated with serum adiponectin across all cohorts after adjustment for age, gender and BMI, including rs10937273, rs12637534, rs1648707, rs16861209, rs822395, rs17366568, rs3774261, rs6444175 and rs17373414. The results of haplotype-based analyses were also consistent. Overall, the variants in the ADIPOQ gene explained <5% of the variance in serum adiponectin concentration. None of the ADIPOR1/R2 tagSNPs were associated with serum adiponectin. There was no association between any of the genetic variants and insulin resistance or metabolic syndrome. A multi-SNP genotypic risk score for ADIPOQ alleles revealed an association with 3 independent SNPs, rs12637534, rs16861209, rs17366568 and type 2 diabetes after adjusting for adiponectin levels (OR=0.86, 95% CI=(0.75, 0.99), P=0.0134). Conclusions Genetic variation in ADIPOQ , but not its receptors, was associated with altered serum adiponectin. However, genetic variation in ADIPOQ and its receptors does not appear to contribute to the risk of insulin resistance or metabolic syndrome but did for type 2 diabetes in a European-Australian population.
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