Effects of 3-month dapagliflozin on left atrial function in treatment-naïve patients with type 2 diabetes mellitus: Assessment using 4-dimensional echocardiography.

BACKGROUND: The sodium-glucose transporter-2 (SGLT-2) inhibitor dapagliflozin can improve left ventricular (LV) performance in patients with type 2 diabetes mellitus (T2DM). However, the effects on left atrial (LA) function in treatment-naïve T2DM patients remain unclear. The aim of our study was 1) to investigate the effects of 3-month treatment with dapagliflozin on LA function in treatment-naïve patients with T2DM using 4-dimensional automated LA quantification (4D Auto LAQ) and 2) to explore linked covariation patterns of changes in clinical and LA echocardiographic variables. METHODS: 4D Auto LAQ was used to evaluate LA volumes, longitudinal and circumferential strains in treatment-naïve T2DM patients at baseline, at follow-up, and in healthy control (HC). Sparse canonical correlation analysis (sCCA) was performed to capture the linked covariation patterns between changes in clinical and LA echocardiographic variables within the treatment-naïve T2DM patient group. RESULTS: This study finally included 61 treatment-naïve patients with T2DM without cardiovascular disease and 39 healthy controls (HC). Treatment-naïve T2DM patients showed reduced LA reservoir and conduit function at baseline compared to HC, independent of age, sex, BMI, and blood pressure (LASr: 21.11 ± 5.39 vs. 27.08 ± 5.31 %, padjusted = 0.017; LAScd: -11.51 ± 4.48 vs. -16.74 ± 4.51 %, padjusted = 0.013). After 3-month treatment with dapagliflozin, T2DM patients had significant improvements in LA reservoir and conduit function independent of BMI and blood pressure changes (LASr: 21.11 ± 5.39 vs. 23.84 ± 5.74 %, padjusted < 0.001; LAScd: -11.51 ± 4.48 vs. -12.75 ± 4.70 %, padjusted < 0.001). The clinical and LA echocardiographic parameters showed significant covariation (r = 0.562, p = 0.039). In the clinical dataset, changes in heart rate, insulin, and BMI were most associated with the LA echocardiographic variate. In the LA echocardiographic dataset, changes in LAScd, LASr, and LASr_c were most associated with the clinical variate. CONCLUSION: Compared with HC, treatment-naïve patients with T2DM had lower LA function, and these patients benefited from dapagliflozin administration, particularly in LA function.

99mTc-HFAPi imaging identifies early myocardial fibrosis in the hypertensive heart.

BACKGROUND: This study aimed to explore whether 99mTc-radiolabeled fibroblast activation protein inhibitor (99mTc-HFAPi) imaging can detect early myocardial fibrosis in the hypertensive heart. METHODS: In the experimental model, spontaneously hypertensive rats (SHRs) and age-matched Wistar Kyoto rats (WKYs) were randomly divided into three groups (8, 16, and 28 weeks). The animals underwent 99mTc-HFAPi imaging and echocardiography. Autoradiography and histological analyses were performed in the left ventricle. The mRNA and protein expression level of the fibroblast activation protein (FAP) and collagen I were measured using quantitative PCR and western blot. In the clinical investigation, a total of 106 patients with essential hypertension and 20 gender-matched healthy controls underwent 99mTc-HFAPi imaging and echocardiography. RESULTS: In-vivo and in-vitro autographic images demonstrated diffusely enhanced 99mTc-HFAPi uptake in the SHR heart starting at week 8, before irreversible collagen deposition. The mRNA and protein levels of FAP in SHRs began to increase from week 8, whereas changes in collagen I levels were not detected until week 28. In the clinical investigation, even in hypertensive patients with normal diastolic indicators, normal left ventricular geometry, and normal global longitudinal strain (GLS), the prevalence of increased 99mTc-HFAPi uptake reached 34, 41, and 20%, respectively, indicating that early fibrogenesis precedes structural and functional myocardial abnormalities. CONCLUSION: In hypertension, 99mTc-HFAPi imaging can detect early fibrotic process before myocardial functional and structural changes.

Association between left ventricular geometry and global myocardial work in patients with heart failure with preserved ejection fraction: assessment using strain-pressure loop.

Concentric LV remodeling and hypertrophy are common structural abnormalities in patients with heart failure with preserved ejection fraction (HFpEF) and tend to be accompanied by impaired LV function. Assessment of global myocardial work (GMW) using strain-pressure loop may provide more comprehensive assessment of LV myocardial function, overcoming the limitations of the conventional parameters. We investigated the value of GMW in patients with HFpEF and assessed the relationship of GMW with concentric remodeling and hypertrophy. Consecutive patients with HFpEF (n = 107) and sex-matched healthy controls (n = 32) were prospectively enrolled. Clinical and conventional echocardiography variables were obtained. Further analyses of offline data were performed to obtain GMW indices including global work index (GWI), global constructive work (GCW), global waste work (GWW), and global work efficiency (GWE). Association of concentric remodeling and hypertrophy with GMW was analyzed by univariate and multivariate analysis. HFpEF patients showed lower GWE (94% vs 96%, P < 0.001) and higher GWW (114 mmHg% vs 78 mmHg%, P = 0.003) than control group, while GWI (2111 mmHg% vs 2146 mmHg%, P = 0.877) and GCW (2369 mmHg% vs 2469 mmHg%, P = 0.733) were comparable in the two groups. HFpEF patients with relative wall thickness (RWT) > 0.42 had reduced GWE (94% vs 95%, P = 0.034) compared to HFpEF patients with RWT ≤ 0.42, while GWI, GCW, and GWW were comparable between these two subgroups. Multivariate analysis showed an independent association of RWT with GWI, GCW, and GWE, respectively. Impaired global myocardial work was detected in patients with HFpEF. Impaired LV GMW may be associated with increased RWT.

A general semiparametric hazards regression model: efficient estimation and structure selection.

We consider a general semiparametric hazards regression model that encompasses the Cox proportional hazards model and the accelerated failure time model for survival analysis. To overcome the nonexistence of the maximum likelihood, we derive a kernel-smoothed profile likelihood function and prove that the resulting estimates of the regression parameters are consistent and achieve semiparametric efficiency. In addition, we develop penalized structure selection techniques to determine which covariates constitute the accelerated failure time model and which covariates constitute the proportional hazards model. The proposed method is able to estimate the model structure consistently and model parameters efficiently. Furthermore, variance estimation is straightforward. The proposed estimation performs well in simulation studies and is applied to the analysis of a real data set.

Parametric variable selection in generalized partially linear models with an application to assess condom use by HIV-infected patients.

To study significant predictors of condom use in HIV-infected adults, we propose the use of generalized partially linear models and develop a variable selection procedure incorporating a least squares approximation. Local polynomial regression and spline smoothing techniques are used to estimate the baseline nonparametric function. The asymptotic normality of the resulting estimate is established. We further demonstrate that, with the proper choice of the penalty functions and the regularization parameter, the resulting estimate performs as well as an oracle procedure. Finite sample performance of the proposed inference procedure is assessed by Monte Carlo simulation studies. An application to assess condom use by HIV-infected patients gains some interesting results, which cannot be obtained when an ordinary logistic model is used.

Rank-based variable selection with censored data.

A rank-based variable selection procedure is developed for the semiparametric accelerated failure time model with censored observations where the penalized likelihood (partial likelihood) method is not directly applicable. The new method penalizes the rank-based Gehan-type loss function with the ℓ1 penalty. To correctly choose the tuning parameters, a novel likelihood-based χ2-type criterion is proposed. Desirable properties of the estimator such as the oracle properties are established through the local quadratic expansion of the Gehan loss function. In particular, our method can be easily implemented by the standard linear programming packages and hence numerically convenient. Extensions to marginal models for multivariate failure time are also considered. The performance of the new procedure is assessed through extensive simulation studies and illustrated with two real examples.

Sparse optimal scoring for multiclass cancer diagnosis and biomarker detection using microarray data.

Gene expression data sets hold the promise to provide cancer diagnosis on the molecular level. However, using all the gene profiles for diagnosis may be suboptimal. Detection of the molecular signatures not only reduces the number of genes needed for discrimination purposes, but may elucidate the roles they play in the biological processes. Therefore, a central part of diagnosis is to detect a small set of tumor biomarkers which can be used for accurate multiclass cancer classification. This task calls for effective multiclass classifiers with built-in biomarker selection mechanism. We propose the sparse optimal scoring (SOS) method for multiclass cancer characterization. SOS is a simple prototype classifier based on linear discriminant analysis, in which predictive biomarkers can be automatically determined together with accurate classification. Thus, SOS differentiates itself from many other commonly used classifiers, where gene preselection must be applied before classification. We obtain satisfactory performance while applying SOS to several public data sets.

Association between body mass index and chronic kidney disease in men and women: population-based study of Malay adults in Singapore.

BACKGROUND: In contrast to previous studies from western populations, studies from Japan reported a positive association between body mass index (BMI) and chronic kidney disease (CKD) among men but not women. In this context, we examined the relationship between BMI and CKD, by gender, in a study of Malay adults from Singapore. METHODS: This was a population-based cross-sectional sample of adults (n = 2783, 53% women, aged 49-80 years), free of clinical cardiovascular disease. The outcome of interest was presence of CKD [estimated glomerular filtration rate (eGFR) <60 mL/min per 1.73 m(2) (n = 517)]. The statistical methods used were logistic and nonparametric logistic regressions. RESULTS: Higher BMI levels were found to be positively associated with CKD among Malay men. Among men, compared to BMI quartile 1 (<23 kg/m(2)), the multivariable odds ratio (OR) [95% confidence intervals (CI)] of CKD was 3.12 (1.97-4.94) in quartile 2 (23-24.9 kg/m(2)), 2.49 (1.63-3.79) in quartile 3 (25-29.9 kg/m(2)) and 3.70 (2.13-6.42) in quartile 4 (>or=30 kg/m(2)); P-trend < 0.0001. In contrast, among women BMI levels were not associated with CKD; P-trend = 0.32. In nonparametric models, among men, the observed positive association between BMI and CKD appeared to be present across the full range of BMI values, without any threshold. In contrast, among women, results from nonparametric models were consistent with the conclusion of a lack of association between BMI and CKD. CONCLUSIONS: Higher BMI levels were positively associated with CKD among men but not women in a population-based study from Singapore. These results are consistent with the hypothesis of a male gender-specific association between BMI and CKD among Asians.

Path consistent model selection in additive risk model via Lasso.

As a flexible alternative to the Cox model, the additive risk model assumes that the hazard function is the sum of the baseline hazard and a regression function of covariates. For right censored survival data when variable selection is needed along with model estimation, we propose a path consistent model selector using a modified Lasso approach, under the additive risk model assumption. We show that the proposed estimator possesses the oracle variable selection and estimation property. Applications of the proposed approach to three right censored survival data sets show that the proposed modified Lasso yields parsimonious models with satisfactory estimation and prediction results.