Association Between Microvascular Disease and Cardiorespiratory Fitness Among Adults With Type 2 Diabetes.

OBJECTIVE: Little is known about the extent to which microvascular disease is associated with cardiorespiratory fitness (CRF) among individuals with type 2 diabetes. RESEARCH DESIGN AND METHODS: A total of 4,766 participants with type 2 diabetes underwent maximal exercise testing in the Look AHEAD (Action for Health in Diabetes) study at baseline. Low CRF was defined based on the Aerobics Center Longitudinal Study reference standards. Microvascular disease was defined as having one or more of diabetes-related kidney disease (DKD), retinopathy, and neuropathy. The burden of microvascular disease was defined as the number of microvascular beds affected. RESULTS: Of the 4,766 participants (mean age 58.9 ± 6.7 years, 58.5% women, 66.1% White individuals), 1,761 (37%) had microvascular disease. Participants with microvascular complications in three vascular territories had a lower CFR than those without any microvascular disease (mean adjusted metabolic equivalent of task [MET] 6.58 vs. 7.26, P = 0.001). Participants with any microvascular disease had higher odds of low CRF than those without microvascular disease (adjusted odds ratio [OR] 1.45, 95% CI 1.24-1.71). An increasing burden of microvascular disease was associated with higher odds of low CRF (for microvascular disease in three vascular territories, adjusted OR 2.82, 95% CI 1.36-5.85). Adjusted ORs for low CRF were 1.24 (95% CI 0.99-1.55), 1.34 (95% CI 1.02-1.76), and 1.44 (95% CI 1.20-1.73) for neuropathy, retinopathy, and DKD associations, respectively. CONCLUSIONS: In a large cohort of adults with type 2 diabetes, the presence of microvascular disease and its burden were independently associated with lower CRF.

Titration of Basal and Prandial Insulin Doses With the Initiation of Glucagon-Like Peptide-1 Receptor Agonist Therapy.

Objective Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have demonstrated significant efficacy in improving glycemic control in type 2 diabetes mellitus, which often results in decreased insulin dose requirements. The purpose of this study was to examine the changes in basal and prandial insulin dose requirements from baseline to three months following initiation of a GLP-1 RA. Methodology A retrospective chart review was conducted of adult insulin-treated patients at the Chertow Diabetes Center, Huntington, WV, who were started on GLP-1 RAs for 24 months. Results Mean daily basal insulin doses decreased by 8.7 units (P = 0.29; mean 8.3% change) and mean daily prandial insulin doses decreased by 9.4 units (P = 0.10; mean 18.4% change) from baseline to three months after starting a GLP-1 RA. Average hemoglobin A1c significantly decreased from 8.8% (73 mmol/mol) at baseline to 8.0% (64 mmol/mol) at three months (P < 0.001). Significant decreases from baseline to three months were also observed in mean body weight, mean low-density lipoprotein (LDL) cholesterol, and mean total cholesterol. Conclusions GLP-1 RA therapy was associated with a significant decrease in hemoglobin A1c, body weight, and LDL-cholesterol from baseline to three months after initiation. Therapy with GLP-1 RAs was also associated with an overall decrease in daily basal and prandial insulin dose requirements, although this finding did not reach statistical significance.

Fully-automated CT derived body composition analysis reveals sarcopenia in functioning adrenocortical carcinomas.

Determination of body composition (the relative distribution of fat, muscle, and bone) has been used effectively to assess the risk of progression and overall clinical outcomes in different malignancies. Sarcopenia (loss of muscle mass) is especially associated with poor clinical outcomes in cancer. However, estimation of muscle mass through CT scan has been a cumbersome, manually intensive process requiring accurate contouring through dedicated personnel hours. Recently, fully automated technologies that can determine body composition in minutes have been developed and shown to be highly accurate in determining muscle, bone, and fat mass. We employed a fully automated technology, and analyzed images from a publicly available cancer imaging archive dataset (TCIA) and a tertiary academic center. The results show that adrenocortical carcinomas (ACC) have relatively sarcopenia compared to benign adrenal lesions. In addition, functional ACCs have accelerated sarcopenia compared to non-functional ACCs. Further longitudinal research might shed further light on the relationship between body component distribution and ACC prognosis, which will help us incorporate more nutritional strategies in cancer therapy.

Somatostatin receptor imaging of thyroid tissue and differentiated thyroid cancer using gallium-68-labeled radiotracers-a review of clinical studies.

INTRODUCTION: The rise in thyroid cancer incidence, especially papillary thyroid cancer (PTC), has underscored the need for improved diagnostic methods and management strategies. Herein, we aim to comprehensively review the evolving landscape in thyroid cancer diagnosis and the potential utility of Gallium-68 (Ga-68) based somatostatin receptor imaging. METHODS: We reviewed the clinical studies involving Ga-68 based radiotracers by looking at the following literature databases -PUBMED, EMBASE, WEB OF SCIENCE and COCHRANE. We employed a detailed search strategy with the following search terms; PubMed: ("gallium Ga 68 dotatate" [Supplementary Concept]) AND ("Thyroid Gland"[Mesh] OR "Thyroid Nodule"[Mesh] OR "Thyroid Neoplasms"[Mesh]), Embase ("gallium 68" AND "Thyroid Disease"), Web of Science: ("Gallium 68 and Thyroid"). RESULTS: A comparison between Ga-68 DOTATATE and Ga-68 DOTANOC showed similar sensitivities but a higher uptake for Ga-68 DOTATATE. Studies comparing Ga-68-based SSTR PET with FDG PET highlighted the potential advantages of both approaches, with Ga-68-based SSTR PET being more specific in certain cases. DISCUSSION: Ga-68-based somatostatin receptor imaging displays clinical utility in RAI-R DTC, offering valuable insight into detecting skeletal lymph node metastases. Notably, it shows potential as a primary imaging tool, potentially augmenting the role of FDG PET. However, SSTR PET imaging's efficacy in distinguishing benign from malignant thyroid nodules varies, with a complex interplay of factors influencing its specificity, indicating its value as an adjunct to existing methods, warranting further research for a refined role in thyroid cancer management. CONCLUSION: Although study variations exist, Ga-based somatostatin receptor imaging holds potential as a complementary tool alongside diagnostic methods in thyroid cancer diagnosis, with particular relevance to RAI-R DTC. In carefully selected patients demonstrating the presence of Ga-68 DOTATATE avid lesions, further exploration, and investigation into the potential utilization of Lu177 DOTATATE are warranted.

Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction.

With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.

Artificial intelligence and body composition.

AIMS: Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends. METHODS: We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review. RESULTS: AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis. CONCLUSIONS: AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context.

Perioperative Evaluation and Management of Patients on Glucocorticoids.

Myriad questions regarding perioperative management of patients on glucocorticoids (GCs) continue to be debated including which patients are at risk for adrenal insufficiency (AI), what is the correct dose and duration of supplemental GCs, or are they necessary for everyone? These questions remain partly unanswered due to the heterogeneity and low quality of data, studies with small sample sizes, and the limited number of randomized trials. To date, we know that although all routes of GC administration can result in hypothalamic-pituitary-adrenal (HPA) axis suppression, perioperative adrenal crisis is rare. Correlation between biochemical testing for AI and clinical events is lacking. Some of the current perioperative management recommendations based on daily GC dose and duration of therapy may be difficult to follow in clinical practice. The prospective and retrospective studies consistently report that continuing the daily dose of GCs perioperatively is not associated with a higher risk for adrenal crises in patients with GC-induced AI. Considering that oral GC intake may be unreliable in the early postoperative period, providing the daily GC plus a short course of IV hydrocortisone 25 to 100 mg per day based on the degree of surgical stress seems reasonable. In patients who have stopped GC therapy before surgery, careful assessment of the HPA axis is necessary to avoid an adrenal crisis. In conclusion, our literature review indicates that lower doses and shorter duration of supplemental GCs perioperatively are sufficient to maintain homeostasis. We emphasize the need for well-designed randomized studies on this frequently encountered clinical scenario.

Relationship Between TSH Levels and Cognition in the Young Adult: An Analysis of the Human Connectome Project Data.

CONTEXT: The nature of the relationship between serum thyrotropin (TSH) levels and higher cognitive abilities is unclear, especially within the normal reference range and in the younger population. OBJECTIVE: To assess the relationship between serum TSH levels and mental health and sleep quality parameters (fluid intelligence [Gf], MMSE (Mini-Mental State Examination), depression scores, and, finally, Pittsburgh Sleep Quality Index (PSQI) scores (working memory, processing speed, and executive function) in young adults. METHODS: This was a retrospective analysis of the data from the Human Connectome Project (HCP). The HCP consortium is seeking to map human brain circuits systematically and identify their relationship to behavior in healthy adults. Included were 391 female and 412 male healthy participants aged 22-35 years at the time of the screening interview. We excluded persons with serum TSH levels outside the reference range (0.4-4.5 mU/L). TSH was transformed logarithmically (log TSH). All the key variables were normalized and then linear regression analysis was performed to assess the relationship between log TSH as a cofactor and Gf as the dependent variable. Finally, a machine learning method, random forest regression, predicted Gf from the dependent variables (including alcohol and tobacco use). The main outcome was normalized Gf (nGf) and Gf scores. RESULTS: Log TSH was a significant co-predictor of nGF in females (ß = 0.31(±0.1), P < .01) but not in males. Random forest analysis showed that the model(s) had a better predictive value for females (r = 0.39, mean absolute error [MAE] = 0.81) than males (r = 0.24, MAE = 0.77). CONCLUSION: Higher serum TSH levels might be associated with higher Gf scores in young women.

Variability of adiposity indices and incident heart failure among adults with type 2 diabetes.

BACKGROUND: It remains unclear how the variability of adiposity indices relates to incident HF. This study evaluated the associations of the variability in several adiposity indices with incident heart failure (HF) in individuals with type 2 diabetes (T2DM). METHODS: We included 4073 participants from the Look AHEAD (Action for Health in Diabetes) study. We assessed variability of body mass index (BMI), waist circumference (WC), and body weight across four annual visits using three variability metrics, the variability independent of the mean (VIM), coefficient of variation (CV), and intraindividual standard deviation (SD). Multivariable Cox regression models were used to generate adjusted hazard ratios (aHR) and 95% confidence intervals (CI) for incident HF. RESULTS: Over a median of 6.7 years, 120 participants developed incident HF. After adjusting for relevant confounders including baseline adiposity levels, the aHR for the highest (Q4) versus lowest quartile (Q1) of VIM of BMI was 3.61 (95% CI 1.91-6.80). The corresponding aHRs for CV and SD of BMI were 2.48 (95% CI 1.36-4.53) and 2.88 (1.52-5.46), respectively. Regarding WC variability, the equivalent aHRs were 1.90 (95% CI 1.11-3.26), 1.79 (95% CI 1.07-3.01), and 1.73 (1.01-2.95) for Q4 versus Q1 of VIM, CV and SD of WC, respectively. CONCLUSIONS: In a large sample of adults with T2DM, a greater variability of adiposity indices was associated with higher risks of incident HF, independently of traditional risk factors and baseline adiposity levels. Registration-URL: https://clinicaltrials.gov/ct2/show/NCT00000620 .

Body Weight Variability and Risk of Cardiovascular Outcomes and Death in the Context of Weight Loss Intervention Among Patients With Type 2 Diabetes.

Importance: Body weight fluctuation is associated with greater risks of adverse health outcomes. Whether intensive weight loss interventions affect the association of variability in adiposity measures with adverse health outcomes in individuals with type 2 diabetes has not been studied previously. Objective: To evaluate the associations of long-term variability in adiposity indices with cardiovascular disease (CVD) outcomes and whether these associations are affected by an intensive lifestyle intervention among adults with type 2 diabetes. Design, Setting, and Participants: This prospective cohort study included participants in the Action for Health in Diabetes (Look AHEAD) trial without CVD at baseline (August 2001 to April 2004). The Look AHEAD study included 16 centers in the United States. Data analysis was performed from December 2020 to June 2021. Exposures: Variability of body mass index (BMI) and waist circumference (WC) across 4 annual visits, assessed using the coefficient of variation (CV), variability independent of the mean (VIM), and standard deviation (SD). Main Outcomes and Measures: Main outcomes were (1) all-cause mortality, (2) cardiovascular deaths (deaths from myocardial infarction [MI] or stroke), and (3) CVD events (MI, stroke, and/or death from cardiovascular causes). Results: Among 3604 study participants (mean [SD] age, 58.4 [6.6] years; 2240 [62.3%] women; 1364 [37.7%] Black participants; 2404 [66%] White participants), there were 216 CVD events, 33 CVD deaths, and 166 deaths over a median of 6.7 years. In the control group, the hazard ratios (HRs) for the highest quartile (quartile 4) compared with the lowest quartile (quartile 1) of CV of BMI were 4.06 (95% CI, 2.17-7.57), 15.28 (95% CI, 2.89-80.90), and 2.16 (95% CI, 1.21-3.87) for all-cause mortality, CVD mortality, and cardiovascular events, respectively. In the intervention group, the corresponding HRs were 0.99 (95% CI, 0.45-2.16), 1.14 (95% CI, 0.12-10.53), and 0.77 (95% CI, 0.40-1.49) for quartile 4 vs quartile 1. Regarding WC, in the control group, HRs for quartile 4 vs quartile 1 were 1.84 (95% CI, 1.01-3.35), 6.46 (95% CI, 1.16-36.01), and 1.28 (95% CI, 0.72-2.29). In the intervention group, HRs were 1.23 (95% CI, 0.61-2.46), 0.55 (95% CI, 0.15-2.11), and 0.70 (95% CI, 0.39-1.25) for quartile 4 vs quartile 1. Conclusions and Relevance: In this cohort study of individuals with type 2 diabetes, higher variability of adiposity indices was associated with significantly increased risk of CVD outcomes and death in the control group but not in the intensive lifestyle intervention group.

Correlates of cardiorespiratory fitness among overweight or obese individuals with type 2 diabetes.

INTRODUCTION: Mechanistic studies suggest that type 2 diabetes is independently associated with low cardiorespiratory fitness (CRF). Little is known about the CRF profile in type 2 diabetes; we assessed the correlates of low CRF among overweight/obese adults with type 2 diabetes. RESEARCH DESIGN AND METHODS: A total of 4215 participants with type 2 diabetes and without cardiovascular disease underwent maximal exercise testing in the Look AHEAD (Action for Health in Diabetes) study. Low CRF was defined based on the Aerobics Center Longitudinal Study reference standards. Calorie intake and physical activity were assessed using questionnaires. Body fat composition was assessed using dual-energy X-ray absorptiometry. RESULTS: Waist circumference, systolic blood pressure, glycemic measures, whole body fat, caloric intake, and fat-free mass were inversely associated with fitness across sex (all p<0.001). Comparing with moderate or high CRF groups, the low CRF group was associated with higher adjusted odds of obesity (OR 3.19 (95% CI 1.95 to 5.20) in men, 3.86 (95% CI 2.55 to 5.84)) in women), abdominal obesity (OR 3.99 (95% CI 2.00 to 7.96) in men, 2.28 (95% CI 1.08 to 4.79) in women), hypertension (OR 1.74 (95% CI 1.09 to 2.77) in men, 1.44 (95% CI 1.02 to 2.05) in women), metabolic syndrome (OR 5.52 (95% CI 2.51 to 12.14) in men, 2.25 (95% CI 1.35 to 3.76) in women), use of beta-blocker (1.22 (95% CI 0.86 to 1.73) in men, 1.33 (95% CI 1.03 to 1.73) in women), and ACE inhibitor/angiotensin-receptor blocker (1.86 (95% CI 1.39 to 2.50) in men, 1.07 (95% CI 0.86 to 1.32) in women). Women with low CRF had higher odds of current smoking (2.02 (95% CI 1.25 to 3.28)). CONCLUSIONS: Low CRF was associated with increased odds of cardiometabolic correlates in a large cohort of adults with type 2 diabetes.

Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort.

Albuminuria and estimated glomerular filtration rate (e-GFR) are early markers of renal disease and cardiovascular outcomes in persons with diabetes. Although body composition has been shown to predict systolic blood pressure, its application in predicting albuminuria is unknown. In this study, we have used machine learning methods to assess the risk of albuminuria in persons with diabetes using body composition and other determinants of metabolic health. This study is a comparative analysis of the different methods to predict albuminuria in persons with diabetes mellitus who are older than 40 years of age, using the LOOK AHEAD study cohort-baseline characteristics. Age, different metrics of body composition, duration of diabetes, hemoglobin A1c, serum creatinine, serum triglycerides, serum cholesterol, serum HDL, serum LDL, maximum exercise capacity, systolic blood pressure, diastolic blood pressure, and the ankle-brachial index are used as predictors of albuminuria. We used Area under the curve (AUC) as a metric to compare the classification results of different algorithms, and we show that AUC for the different models are as follows: Random forest classifier-0.65, gradient boost classifier-0.61, logistic regression-0.66, support vector classifier -0.61, multilayer perceptron -0.67, and stacking classifier-0.62. We used the Random forest model to show that the duration of diabetes, A1C, serum triglycerides, SBP, Maximum exercise Capacity, serum creatinine, subtotal lean mass, DBP, and subtotal fat mass are important features for the classification of albuminuria. In summary, when applied to metabolic imaging (using DXA), machine learning techniques offer unique insights into the risk factors that determine the development of albuminuria in diabetes.

Artificial Intelligence (AI) approach to identifying factors that determine systolic blood pressure in type 2 diabetes (study from the LOOK AHEAD cohort).

BACKGROUND AND AIMS: Artificial Intelligence (AI) methods have recently become critical for research in diabetes in the era of big-data science. METHODS: In this study, we used the data from the LOOK AHEAD and applied Random Forest to examine the factors determining SBP in persons with diabetes using the software R (version 4.0.3). RESULTS: Our analysis (that included 4723 participants) showed that maximal exercise capacity, age, albumin to creatinine ratio, and serum creatinine were the key variables that determined systolic blood pressure. CONCLUSIONS: Maximum exercise capacity is an important predictor of systolic blood pressure in patients with type 2 diabetes.

Real-time quantitation of thyroidal radioiodine uptake in thyroid disease with monitoring by a collar detection device.

Radioactive iodine (RAI) is safe and effective in most patients with hyperthyroidism but not all individuals are cured by the first dose, and most develop post-RAI hypothyroidism. Postoperative RAI therapy for remnant ablation is successful in 80-90% of thyroid cancer patients and sometimes induces remission of nonresectable cervical and/or distant metastatic disease but the effective tumor dose is usually not precisely known and must be moderated to avoid short- and long-term adverse effects on other tissues. The Collar Therapy Indicator (COTI) is a radiation detection device embedded in a cloth collar secured around the patient's neck and connected to a recording and data transmission box. In previously published experience, the data can be collected at multiple time points, reflecting local cervical RAI exposure and correlating well with conventional methods. We evaluated the real-time uptake of RAI in patients with hyperthyroid Graves' disease and thyroid cancer. We performed a pilot feasibility prospective study. Data were analyzed using R© (version 4.0.3, The R Foundation for Statistical Computing, 2020), and Python (version 3.6, Matplotlib version 3.0.3). The COTI was able to provide a quantitative temporal pattern of uptake within the thyroid in persons with Graves' disease and lateralized the remnant tissue in persons with thyroid cancer. The study has demonstrated that the portable collar radiation detection device outside of a healthcare facility is accurate and feasible for use after administration of RAI for diagnostic studies and therapy to provide a complete collection of fractional target radioactivity data compared to that traditionally acquired with clinic-based measurements at one or two time-points.Clinical Trials Registration NCT03517579, DOR 5/7/2018.

Microvascular Disease and Incident Heart Failure Among Individuals With Type 2 Diabetes Mellitus.

Background Microvascular disease (MVD) is a potential contributor to the pathogenesis of diabetes mellitus-related cardiac dysfunction. However, there is a paucity of data on the link between MVD and incident heart failure (HF) in type 2 diabetes mellitus. We examined the association of MVD with incident HF in adults with type 2 diabetes mellitus. Methods and Results A total of 4095 participants with type 2 diabetes mellitus and free of HF were assessed for diabetes mellitus-related MVD including nephropathy, retinopathy, or neuropathy at baseline in the Look AHEAD (Action for Health in Diabetes) study. Incident HF events were prospectively assessed and adjudicated using hospital and death records. Cox models were used to generate hazard ratios and 95% CIs for HF. Of 4095 participants, 34.8% (n=1424) had MVD, defined as the presence of ≥1 of nephropathy, retinopathy, or neuropathy at baseline. Over a median of 9.7 years, there were 117 HF events. After adjusting for relevant confounders, participants with MVD had a 2.5-fold higher risk of incident HF than those without MVD (hazard ratio, 2.54; 95% CI, 1.73-3.75). This association remained significant after additional adjustment for interval development of coronary artery disease (hazard ratio, 2.42; 95% CI, 1.64-3.57). The hazard ratios for HF by type of MVD were 2.22 (95% CI, 1.51-3.27), 1.30 (95% CI, 0.72-2.36), and 1.33 (95% CI, 0.86-2.07) for nephropathy, retinopathy, and neuropathy, respectively. CONCLUSIONS MVD is associated with an excess HF risk in individuals with type 2 diabetes mellitus after adjusting for other known risk factors. Our findings underscore the contribution of MVD to the development of diabetes mellitus-related HF. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00017953.

Long-term variability of blood pressure and incidence of heart failure among individuals with Type 2 diabetes.

AIMS: Data on the association of long-term variability of blood pressure (BP) with incident heart failure (HF) in individuals with Type 2 diabetes are scarce. We evaluated this association in a large community-based sample of adults with Type 2 diabetes. METHODS AND RESULTS: A total of 4200 participants with Type 2 diabetes who had available BP measurements at four visits (baseline and 12, 24, and 36 months) in the Look AHEAD (Action for Health in Diabetes) study were included. Variability of systolic BP (SBP) and diastolic BP (DBP) across the four visits was assessed using four metrics. Participants free of HF during the first 36 months were followed for HF events. Cox regression was used to generate hazard ratios (HRs) and 95% confidence intervals (CIs) for HF. Of the 4200 participants, the average age was 59 years [standard deviation (SD): 6.8]; 58.5% were women. Over a median follow-up of 6.7 years, 129 developed HF events. After adjusting for relevant confounders, the HR of incident HF for the highest vs. lowest quartile of SD of SBP was 1.77 (95% CI 1.01-3.09); the HR for the highest (vs. lowest) quartile of variability independent of the mean of SBP was 1.29 (95% CI 0.78-2.14). The adjusted HR for participants in the highest (compared with the lowest) quartile of SD of DBP was 1.61 (95% CI 1.01-2.59), and the adjusted HR for variability independent of the mean of DBP was 1.65 (95% CI 1.03-2.65). CONCLUSIONS: A greater variability in SBP and DBP is independently associated with greater risk of incident HF in individuals with Type 2 diabetes.

Microvascular disease and cardiovascular outcomes among individuals with type 2 diabetes.

AIM: To evaluate the associations of microvascular disease (MVD) with incident cardiovascular disease (CVD) in individuals with type 2 diabetes. METHODS: A total of 4098 participants with type 2 diabetes and without CVD were assessed for MVD (diabetic kidney disease, retinopathy or neuropathy) in the Look AHEAD (Action for Health in Diabetes) study. Cox models were used to generate hazard ratios (HRs) for: (1) CVD composite (myocardial infarction, stroke, hospitalization for angina and/or death from cardiovascular causes), (2) coronary artery disease (CAD), (3) stroke, and (4) CVD-related deaths. RESULTS: Of 4098 participants, 34.7% (n = 1424) had MVD at baseline. Over a median of 9.5 years, 487 developed the CVD composite, 410 CAD events, 100  stroke, and 54 CVD-related deaths. After adjusting for relevant confounders, MVD was associated with increased risks of CVD composite (HR 1.34, 95% CI 1.11-1.61), CAD (HR 1.24, 95% CI 1.01-1.52), stroke (HR 1.55, 95% CI 1.03-2.33), and cardiovascular mortality (HR 1.26, 95% CI 0.72-2.22). HRs for CVD composite by type of MVD were 1.11 (95% CI 0.89-1.38), 1.63 (95% CI 1.22-2.17) and 1.16 (95% CI 0.92-1.46) for diabetic kidney disease, retinopathy, and neuropathy, respectively. CONCLUSIONS: Our findings underscore the relevance of MVD in CVD risk assessment in type 2 diabetes.

Long-Term Variability of Blood Pressure, Cardiovascular Outcomes, and Mortality: The Look AHEAD Study.

BACKGROUND: We evaluated the associations of visit-to-visit blood pressure (BP) variability with incident cardiovascular disease (CVD) and deaths in adults with type 2 diabetes. METHODS: We analyzed 4,152 participants in Look AHEAD (Action for Health in Diabetes) free of CVD events and deaths during the first 36 months of follow-up. Variability of systolic BP (SBP) and diastolic BP (DBP) across 4 annual visits was assessed using the intraindividual SD, variation independent of the mean, and coefficient of variation. Cox regression was used to generate the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for CVD (myocardial infarction [MI], stroke, or CVD-related deaths) and mortality. RESULTS: Over a median of 6.6 years, there were 220 MIs, 105 stroke cases, 62 CVD-related deaths, and 236 deaths. After adjustment for confounders including average BP, the aHRs for the highest (vs. lowest) tertile of SD of SBP were 1.98 (95% CI 1.01-3.92), 1.25 (95% CI 0.90-1.72), 1.26 (95% CI 0.96-1.64), 1.05 (95% CI 0.75-1.46), and 1.64 (95% CI 0.99-2.72) for CVD mortality, all-cause mortality, CVD, MI, and stroke, respectively. The equivalent aHRs for SD of DBP were 1.84 (95% CI 0.98-3.48), 1.43 (95% CI 1.03-1.98), 1.19 (95% CI 0.91-1.56), 1.14 (95% CI 0.82-1.58), and 0.97 (95% CI 0.58-1.60), respectively. CONCLUSIONS: In a large sample of individuals with type 2 diabetes, a greater variability in SBP was associated with higher cardiovascular mortality and CVD events; a higher variability in DBP was linked to increased overall and cardiovascular mortality.

Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach.

Diabetes mellitus is associated with increased cardiovascular disease (CVD) related morbidity, mortality and death. Exercise capacity in persons with type 2 diabetes has been shown to be predictive of cardiovascular events. In this study, we used the data from the prospective randomized LOOK AHEAD study and used machine learning algorithms to help predict exercise capacity (measured in Mets) from the baseline data that included cardiovascular history, medications, blood pressure, demographic information, anthropometric and Dual-energy X-Ray Absorptiometry (DXA) measured body composition metrics. We excluded variables with high collinearity and included DXA obtained Subtotal (total minus head) fat percentage and Subtotal lean mass (gms). Thereafter, we used different machine learning methods to predict maximum exercise capacity. The different machine learning models showed a strong predictive performance for both females and males. Our study shows that using baseline data from a large prospective cohort, we can predict maximum exercise capacity in persons with diabetes mellitus. We show that subtotal fat percentage is the most important feature for predicting the exercise capacity for males and females after accounting for other important variables. Until now, BMI and waist circumference were commonly used surrogates for adiposity and there was a relative under-appreciation of body composition metrics for understanding the pathophysiology of CVD. The recognition of body fat percentage as an important marker in determining CVD risk has prognostic implications with respect to cardiovascular morbidity and mortality.

Metabolic Dyslipidemia and Cardiovascular Outcomes in Type 2 Diabetes Mellitus: Findings From the Look AHEAD Study.

Background Metabolic dyslipidemia (high triglyceride) and low high-density lipoprotein cholesterol (HDL-C) is highly prevalent in type 2 diabetes mellitus (T2DM). The extent to which diabetes mellitus-related abnormalities in the triglyceride-HDL-C profile associates with cardiovascular disease (CVD) risk is incompletely understood. We evaluated the associations of triglyceride and HDL-C status with CVD outcomes in individuals with T2DM. Methods and Results We analyzed data from 4199 overweight/obese adults with T2DM free of CVD with available data on triglyceride and HDL-C at baseline (2001-2004) in the Look AHEAD (Action for Health in Diabetes) study. We used Cox proportional models to estimate hazard ratios (HRs) and 95% CIs of: (1) composite CVD outcome (myocardial infarction, stroke, hospitalization for angina, and/or death from cardiovascular causes); (2) coronary artery disease events; and (3) cerebrovascular accidents (stroke). Of the 4199 participants, 62% (n=2600) were women, with a mean age of 58 years (SD, 7), and 40% (n=1659) had metabolic dyslipidemia at baseline. Over a median follow-up of 9.5 years (interquartile range, 8.7-10.3), 500 participants experienced the composite CVD outcome, 396 experienced coronary artery disease events, and 100 experienced stroke. Low HDL-C was associated with higher hazards of the composite CVD outcome (HR, 1.36; 95% CI, 1.12-1.64 [P=0.002]) and coronary artery disease events (HR, 1.46; 95% CI, 1.18-1.81 [P=0.001]) but not stroke (HR, 1.38; 95% CI, 0.90-2.11 [P=0.140]). Compared with patients with normal triglyceride and normal HDL, participants with metabolic dyslipidemia had higher risks of the composite CVD outcome (HR, 1.30; 95% CI, 1.03-1.63 [P=0.025]) and coronary artery disease events (HR, 1.48; 95% CI, 1.14-1.93 [P=0.003]) but not stroke (HR, 1.23; 95% CI, 0.74-2.05 [P=0.420]). Conclusions In a large sample of overweight/obese individuals with T2DM, metabolic dyslipidemia was associated with higher risks of CVD outcomes. Our findings highlight the necessity to account for metabolic dyslipidemia in CVD risk stratification among patients with T2DM. Registration URL: https://www.lookaheadtrial.org; Unique identifier: NCT00017953.