Utilizing synchronous care to improve cardiovascular and renal health among patients with type 2 diabetes: Proof-of-concept results from the DECIDE-CV clinical programme.

AIM: The management of patients with type 2 diabetes is asynchronous, i.e. not coordinated in time, resulting in delayed access to care and low use of guideline-directed medical therapy (GDMT). METHODS: We retrospectively analysed consecutive patients assessed in the 'synchronized' DECIDE-CV clinic. In this outpatient clinic, patients with type 2 diabetes and cardiovascular or chronic kidney disease are simultaneously assessed by an endocrinologist, cardiologist and nephrologist in the same visit. The primary outcome was use of GDMT before and after the assessment in the clinic, including sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, renin-angiotensin system blockers and mineralocorticoid receptor antagonists. Secondary outcomes included the baseline-to-last-visit change in surrogate laboratory biomarkers. RESULTS: The first 232 patients evaluated in the clinic were included. The mean age was 67 ± 12 years, 69% were men and 92% had diabetes. In total, 73% of patients had atherosclerotic cardiovascular disease, 65% heart failure, 56% chronic kidney disease and 59% had a urinary albumin-to-creatinine ratio ≥30 mg/g. There was a significant increase in the use of GDMT:sodium-glucose cotransporter 2 inhibitors (from 44% to 87% of patients), glucagon-like peptide 1 receptor agonists (from 8% to 45%), renin-angiotensin system blockers (from 77% to 91%) and mineralocorticoid receptor antagonists (from 25% to 45%) (p < .01 for all). Among patients with paired laboratory data, glycated haemoglobin, urinary albumin-to-creatinine ratio and N-terminal proB-type natriuretic peptide levels significantly dropped from baseline (p < .05 for all). CONCLUSIONS: Joint assessment of patients with diabetes in a synchronized cardiometabolic clinic holds promise for enhancing GDMT use and has led to significant reductions in surrogate cardiovascular and renal laboratory biomarkers.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.

BACKGROUND: Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens. METHODS: A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. RESULTS: Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79). CONCLUSIONS: DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Maximizing Large Language Model Utility in Cardiovascular Care: A Practical Guide.

Large language models (LLMs) have emerged as powerful tools in artificial intelligence, demonstrating remarkable capabilities in natural language processing and generation. In this article, we explore the potential applications of LLMs in enhancing cardiovascular care and research. We discuss how LLMs can be utilized to simplify complex medical information, improve patient-physician communication, and automate tasks such as summarizing medical articles and extracting key information. Additionally, we highlight the role of LLMs in categorizing and analyzing unstructured data, such as medical notes and test results, which could revolutionize data handling and interpretation in cardiovascular research. However, we also emphasize the limitations and challenges associated with LLMs, including potential biases, reasoning opacity, and the need for rigorous validation in medical contexts. This article provides a practical guide for cardiovascular professionals to understand and harness the power of LLMs while navigating their limitations. We conclude by discussing the future directions and implications of LLMs in transforming cardiovascular care and research.

Impact of age and comorbid heart failure on the utility of smart voice-assistant devices.

Aims: The accuracy of voice-assisted technologies, such as Amazon Alexa, to collect data in patients who are older or have heart failure (HF) is unknown. The aim of this study is to analyse the impact of increasing age and comorbid HF, when compared with younger participants and caregivers, and how these different subgroups classify their experience using a voice-assistant device, for screening purposes. Methods and results: Subgroup analysis (HF vs. caregivers and younger vs. older participants) of the VOICE-COVID-II trial, a randomized controlled study where participants were assigned with subsequent crossover to receive a SARS-CoV2 screening questionnaire by Amazon Alexa or a healthcare personnel. Overall concordance between the two methods was compared using unweighted kappa scores and percentage of agreement. From the 52 participants included, the median age was 51 (34-65) years and 21 (40%) were HF patients. The HF subgroup showed a significantly lower percentage of agreement compared with caregivers (95% vs. 99%, P = 0.03), and both the HF and older subgroups tended to have lower unweighted kappa scores than their counterparts. In a post-screening survey, both the HF and older subgroups were less acquainted and found the voice-assistant device more difficult to use compared with caregivers and younger individuals. Conclusion: This subgroup analysis highlights important differences in the performance of a voice-assistant-based technology in an older and comorbid HF population. Younger individuals and caregivers, serving as facilitators, have the potential to bridge the gap and enhance the integration of these technologies into clinical practice. Study Registration: ClinicalTrials.gov Identifier: NCT04508972.

The Role of Perioperative Nutritional Status and Supplementation in Orthopaedic Surgery: A Review of Postoperative Outcomes.

¼ Identification of malnourished and at-risk patients should be a standardized part of the preoperative evaluation process for every patient.¼ Malnourishment is defined as a disorder of energy, protein, and nutrients based on the presence of insufficient energy intake, weight loss, muscle atrophy, loss of subcutaneous fat, localized or generalized fluid accumulation, or diminished functional status.¼ Malnutrition has been associated with worse outcomes postoperatively across a variety of orthopaedic procedures because malnourished patients do not have a robust metabolic reserve available for recovery after surgery.¼ Screening assessment and basic laboratory studies may indicate patients' nutritional risk; however, laboratory values are often not specific for malnutrition, necessitating the use of prognostic screening tools.¼ Nutrition consultation and perioperative supplementation with amino acids and micronutrients are 2 readily available interventions that orthopaedic surgeons can select for malnourished patients.

Body mass index and cardiorenal outcomes in the EMPEROR-Preserved trial: Principal findings and meta-analysis with the DELIVER trial.

AIMS: Both low and high body mass index (BMI) are associated with poor heart failure outcomes. Whether BMI modifies benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in heart failure with preserved ejection fraction (HFpEF) requires further investigation. METHODS AND RESULTS: Using EMPEROR-Preserved data, the effects of empagliflozin versus placebo on the risks for the primary outcome (hospitalization for heart failure [HHF] or cardiovascular [CV] death), change in estimated glomerular filtration rate (eGFR) slopes, change in Kansas City Cardiomyopathy Questionnaire clinical summary score (KCCQ-CSS), and secondary outcomes across baseline BMI categories (<25 kg/m2, 25 to <30 kg/m2, 30 to <35 kg/m2, 35 to <40 kg/m2 and ≥40 kg/m2) were examined, and a meta-analysis conducted with DELIVER. Forty-five percent had a BMI of ≥30 kg/m2. For the primary outcome, there was a consistent treatment effect of empagliflozin versus placebo across the BMI categories with no formal interaction (p trend = 0.19) by BMI categories. There was also no difference in the effects on secondary outcomes including total HHF (p trend = 0.19), CV death (p trend = 0.20), or eGFR slope with slower declines with empagliflozin regardless of BMI (range 1.12-1.71 ml/min/1.73 m2 relative to placebo, p trend = 0.85 for interaction), though there was no overall impact on the composite renal endpoint. The difference in weight change between empagliflozin and placebo was -0.59, -1.48, -1.54, -0.87, and - 2.67 kg in the lowest to highest BMI categories (p trend = 0.016 for interaction). A meta-analysis of data from EMPEROR-Preserved and DELIVER showed a consistent effect of SGLT2i versus placebo across BMI categories for the outcome of HHF or CV death. There was a trend toward greater absolute KCCQ-CSS benefit at 32 weeks with empagliflozin at higher BMIs (p = 0.08). CONCLUSIONS: Empagliflozin treatment resulted in broadly consistent cardiac effects across the range of BMI in patients with HFpEF. SGLT2i treatment yields benefit in patients with HFpEF regardless of baseline BMI.

Decreased Instability in High-Risk (Hip-Spine 2B) Patients After Modifications of Surgical Planning and Technique.

BACKGROUND: Patients undergoing primary total hip arthroplasty (THA) who have spinal deformity and a stiff spine are the highest-risk group for instability. Despite the increasing use of dual-mobility cups and large femoral heads, dislocation remains a major complication after THA. Preoperative planning becomes a critical aspect of ensuring precise component positioning within a safe zone. The purpose of this study was to investigate dislocation rates over a 9-year period. METHODS: A retrospective review of 4,731 THAs performed by 3 orthopaedic surgeons between January 2014 and March 2023 was performed. Spinopelvic measurements were conducted to determine the hip-spine classification group for each patient. Only patients classified as 2B (pelvic incidence-lumbar lordosis > 10° and Δsacral slope < 10°) were eligible. Both absolute and relative dislocation frequencies were then analyzed using time-series analysis techniques and Fisher's exact tests. RESULTS: A total of 281 hip-spine 2B patients undergoing primary THA were eligible for analysis (57% women; mean age, range: 66 years, 23 to 87; mean body mass index, range: 28, 16 to 45). The overall dislocation rate was 4.3%. Use of femoral head sizes ≥ 40 mm increased from 4% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001), while the use of dual-mobility cups decreased from 100% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001). Acetabular component planning was changed from the supine plane to the standing plane in February 2020. Those changes in surgical practice were notably correlated with a significant decrease in dislocation rates from 6.8% in 2014 to 2019 to 1.5% in 2020 to 2023 (P = .03). CONCLUSIONS: Our study demonstrates that the introduction of advanced preoperative THA planning to the standing plane, coupled with precise intraoperative technology for implant placement, can significantly reduce the risk of instability in high-risk THA patients. Notably, we observed a significant decrease in dislocation rates, which aligned with the shift in surgical practice. LEVEL OF EVIDENCE: IV.

Data-driven MEG analysis to extract fMRI resting-state networks.

The electrophysiological basis of resting-state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data-driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase-amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group-specific fMRI-RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI-RSN than the two existing approaches. Importantly, with this approach, all networks-except for the visual network-had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI-RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.

Oscillatory Force Autocorrelations in Equilibrium Odd-Diffusive Systems.

The force autocorrelation function (FACF), a concept of fundamental interest in statistical mechanics, encodes the effect of interactions on the dynamics of a tagged particle. In equilibrium, the FACF is believed to decay monotonically in time, which is a signature of slowing down of the dynamics of the tagged particle due to interactions. Here, we analytically show that in odd-diffusive systems, which are characterized by a diffusion tensor with antisymmetric elements, the FACF can become negative and even exhibit temporal oscillations. We also demonstrate that, despite the isotropy, the knowledge of FACF alone is not sufficient to describe the dynamics: the full autocorrelation tensor is required and contains an antisymmetric part. These unusual properties translate into enhanced dynamics of the tagged particle quantified via the self-diffusion coefficient that, remarkably, increases due to particle interactions.

Label-Free Metal-Oxide Transistor Biosensors for Metabolite Detection in Human Saliva.

Metabolites are essential molecules involved in various metabolic processes, and their deficiencies and excessive concentrations can trigger significant physiological consequences. The detection of multiple metabolites within a non-invasively collected biofluid could facilitate early prognosis and diagnosis of severe diseases. Here, a metal oxide heterojunction transistor (HJ-TFT) sensor is developed for the label-free, rapid detection of uric acid (UA) and 25(OH)Vitamin-D3 (Vit-D3) in human saliva. The HJ-TFTs utilize a solution-processed In2 O3 /ZnO channel functionalized with uricase enzyme and Vit-D3 antibody for the selective detection of UA and Vit-D3, respectively. The ultra-thin tri-channel architecture facilitates strong coupling between the electrons transported along the buried In2 O3 /ZnO heterointerface and the electrostatic perturbations caused by the interactions between the surface-immobilized bioreceptors and target analytes. The biosensors can detect a wide range of concentrations of UA (from 500 nm to 1000 µM) and Vit-D3 (from 100 pM to 120 nm) in human saliva within 60 s. Moreover, the biosensors exhibit good linearity with the physiological concentration of metabolites and limit of detections of ≈152 nm for UA and ≈7 pM for Vit-D3 in real saliva. The specificity is demonstrated against various interfering species, including other metabolites and proteins found in saliva, further showcasing its capabilities.

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark-spatiotemporal signal space separation-and show that it can more accurately reveal brain stimulation-evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical-evoked responses to DBS of the subthalamic nucleus.

High-sensitivity C-reactive protein in heart failure with preserved ejection fraction: Findings from TOPCAT.

BACKGROUND: Inflammation plays a central role in the genesis and progression of heart failure with preserved ejection fraction (HFpEF). C-reactive protein (CRP) is widely used as means to assess systemic inflammation, and elevated levels of CRP have been associated with poor HF prognosis. Identification of chronic low-grade inflammation in outpatients can be performed measuring high-sensitivity CRP (hsCRP). The clinical characteristics and outcome associations of a pro-inflammatory state among outpatients with HFpEF requires further study. AIMS: Using a biomarker subset of TOPCAT-Americas (NCT00094302), we aim to characterize HFpEF patients according to hsCRP levels and study the prognostic associations of hsCRP. METHODS: hsCRP was available in a subset of 232 participants. Comparisons were performed between patients with hsCRP <2 mg/L and ≥ 2 mg/L. Cox regression models were used to study the association between hsCRP and the study outcomes. RESULTS: Compared to patients with hsCRP <2 mg/L (n = 89, 38%), those with hsCRP ≥2 mg/L (n = 143, 62%) had more frequent HF hospitalizations prior to randomization, chronic obstructive pulmonary disease, orthopnea, higher body mass index, and worse health-related quality-of-life. A hsCRP level ≥ 2 mg/L was associated with an increased risk of cardiovascular death and HF hospitalizations: hsCRP ≥2 mg/L vs <2 mg/L adjusted HR 2.36, 95%CI 1.27-4.38, P = 0.006. Spironolactone did not influence hsCRP levels from baseline to month 12: gMean ratio = 1.11, 95%CI 0.87-1.42, P = 0.39. CONCLUSIONS: A hsCRP ≥2 mg/L identified HFpEF patients with a high risk of HF events and cardiovascular mortality. Spironolactone did not influence hsCRP levels at 12 months.

Regional variation in cause of death in patients with type 2 diabetes: Insights from EXSCEL.

AIMS: Type 2 diabetes (T2D) is a risk factor for cardiovascular and non-cardiovascular mortality. However, global distribution of cause-specific deaths in T2D is poorly understood. We characterized cause-specific deaths by geographic region among individuals with T2D at risk for cardiovascular disease (CVD). METHODS AND RESULTS: The international EXSCEL trial included 14,752 participants with T2D (73% with established CVD). We identified the proportion of deaths over 5-year follow-up attributed to cardiovascular and non-cardiovascular causes, and associated risk factors. During median 3.2-year follow-up, 1,091 (7.4%) participants died. Adjudicated causes of death were 723 cardiovascular (66.3% of deaths), including 252 unknown, and 368 non-cardiovascular (33.7%). Most deaths occurred in North America (N = 356/9.6% across region) and Eastern Europe (N = 326/8.1%), with fewest in Asia/Pacific (N = 68/4.4%). The highest proportional cause-specific deaths by region were sudden cardiac in Asia/Pacific (23/34% of regional deaths) and North America (86/24%); unknown in Eastern Europe (90/28%) and Western Europe (39/21%); and non-malignant non-cardiovascular in Latin America (48/31%). Cox proportional hazards model for adjudicated causes of death showed prognostic risk factors (hazard ratio [95% CI]) for cardiovascular and non-cardiovascular deaths, respectively: heart failure 2.04 (1.72-2.42) and 1.86 (1.46-2.39); peripheral artery disease 1.83 (1.54-2.18) and 1.78 (1.40-2.26); and current smoking status 1.61 (1.29-2.01) and 1.77 (1.31-2.40). CONCLUSIONS: In a contemporary T2D trial population, with and without established CVD, leading causes of death varied by geographic region. Underlying mechanisms leading to variability in cause of death across geographic regions and its impact on clinical trial endpoints warrant future research.

Description of new species of Mycobacterium terrae complex isolated from sewage at the São Paulo zoological park foundation in Brazil.

Five mycobacterial isolates from sewage were classified as members of the genus Mycobacterium but presented inconclusive species assignments. Thus, the isolates (MYC017, MYC098, MYC101, MYC123 and MYC340) were analyzed by phenotypical, biochemical, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and genomic features to clarify their taxonomic position. Phenotypic analysis and biochemical tests did not distinguish these isolates from other non-pigmented mycobacteria. In contrast, MALDI-TOF MS analysis showed that isolates were not related to any previously described Mycobacterium species. Comparative genomic analysis showed values of ANI and dDDH between 81.59-85.56% and 24.4-28.8%, respectively, when compared to the genomes of species of this genus. In addition, two (MYC101 and MYC123) presented indistinguishable protein spectra from each other and values of ANI = 98.57% and dDDH = 97.3%, therefore being considered as belonging to the same species. Phylogenetic analysis grouped the five isolates within the Mycobacterium terrae complex (MTC) but in a specific subclade and separated from the species already described and supported by 100% bootstrap value, confirming that they are part of this complex but different from earlier described species. According to these data, we propose the description of four new species belonging to the Mycobacterium genus: (i) Mycobacterium defluvii sp. nov. strain MYC017T (= ATCC TSD-296T = JCM 35364T), (ii) Mycobacterium crassicus sp. nov. strain MYC098T (= ATCC TSD-297T = JCM 35365T), (iii) Mycobacterium zoologicum sp. nov. strain MYC101T (= ATCC TSD-298T = JCM 35366T) and MYC123 (= ATCC BAA-3216 = JCM 35367); and (iv) Mycobacterium nativiensis sp. nov. strain MYC340T (= ATCC TSD-299T = JCM 35368T).

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer.

BACKGROUND: Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance. METHODS: This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. RESULTS: We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18-5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07-4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. CONCLUSION: Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

Primary and Secondary Cardiovascular and Kidney Prevention With Canagliflozin: Insights From the CANVAS Program and CREDENCE Trial.

BACKGROUND: This study evaluated the effects of canagliflozin in patients with type 2 diabetes with and without prevalent cardiovascular disease (secondary and primary prevention). METHODS AND RESULTS: This was a pooled participant-level analysis of the CANVAS (Canagliflozin Cardiovascular Assessment Study) Program and CREDENCE (Canagliflozin and Renal Events in Diabetes With Established Nephropathy Clinical Evaluation) trial. The CANVAS Program included participants with type 2 diabetes at elevated cardiovascular risk, whereas the CREDENCE trial included participants with type 2 diabetes and albuminuric chronic kidney disease. Hazard ratios (HRs) with interaction terms were obtained from Cox regression models to estimate relative risk reduction with canagliflozin versus placebo across the primary and secondary prevention groups. We analyzed 5616 (38.9%) and 8804 (61.1%) individuals in the primary and secondary prevention subgroups, respectively. Primary versus secondary prevention participants were on average younger (62.2 versus 63.8 years of age) and more often women (42% versus 31%). Canagliflozin reduced the risk of major adverse cardiovascular events (HR, 0.84 [95% CI, 0.76-0.94]) consistently across primary and secondary prevention subgroups (Pinteraction=0.86). Similarly, no treatment effect heterogeneity was observed with canagliflozin for hospitalization for heart failure, cardiovascular death, end-stage kidney disease, or all-cause mortality (all Pinteraction>0.5). CONCLUSIONS: Canagliflozin reduced cardiovascular and kidney outcomes with no statistical evidence of heterogeneity for the treatment effect across the primary and secondary prevention subgroups in the CANVAS Program and CREDENCE trial. Although studies on the optimal implementation of canagliflozin within these populations are warranted, these results reinforce canagliflozin's role in cardiorenal prevention and treatment in individuals with type 2 diabetes. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifiers: NCT01032629, NCT01989754, NCT02065791.