Somers, Virend K.

Abstract Aims To test whether an index based on the combination of demographics and body volumes obtained with a multisensor 3D body volume (3D-BV) scanner and biplane imaging using a mobile application (myBVI®) will reliably predict the severity and presence of metabolic syndrome (MS). Methods and results We enrolled 1280 consecutive subjects who completed study protocol measurements, including 3D-BV and myBVI®. Body volumes and demographics were screened using the least absolute shrinkage and selection operator to select features associated with an MS severity score and prevalence. We randomly selected 80% of the subjects to train the models, and performance was assessed in 20% of the remaining observations and externally validated on 133 volunteers who prospectively underwent myBVI® measurements. The mean ± SD age was 43.7 ± 12.2 years, 63.7% were women, body mass index (BMI) was 28.2 ± 6.2 kg/m2, and 30.2% had MS and an MS severity z-score of −0.2 ± 0.9. Features β coefficients equal to zero were removed from the model, and 14 were included in the final model and used to calculate the body volume index (BVI), demonstrating an area under the receiving operating curve (AUC) of 0.83 in the validation set. The myBVI® cohort had a mean age of 33 ± 10.3 years, 61% of whom were women, 10.5% MS, an average MS severity z-score of −0.8, and an AUC of 0.88. Conclusion The described BVI model was associated with an increased severity and prevalence of MS compared with BMI and waist-to-hip ratio. Validation of the BVI had excellent performance when using myBVI®. This model could serve as a powerful screening tool for identifying MS.

Despite significant efforts in the development of noninvasive blood glucose (BG) monitoring solutions, delivering an accurate, real-time BG measurement remains challenging. We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18–65 with prediabetes or type 2 diabetes and no other significant medical history. During control sessions and oral glucose tolerance test sessions, data were collected from both a RF sensor that rapidly scans thousands of frequencies and concurrently from a venous blood draw measured with an US Food and Drug Administration (FDA)-cleared glucose hospital meter system to create paired observations. We trained a time series forest machine learning model on 80% of the paired observations and reported results from applying the model to the remaining 20%. Our findings show that the model correctly classified glycemic status 93.37% of the time as high, normal, or low.

Patients with obstructive sleep apnea (OSA) have increased risk for cardiovascular diseases. Data from the Atherosclerosis Risk in the Communities and Sleep Heart Health Study suggest that OSA is more deleterious to females versus males, although mechanistic data are lacking. Estimates of central (aortic) blood pressure (BP) are superior in predicting future cardiovascular events than traditional brachial BP measurements. While patients with OSA have altered central hemodynamics (e.g., higher aortic pulse wave velocity; PWV), potential sex differences are unexplored. We tested the hypothesis that females with OSA (n=7, 50±3yrs, 35.1±1.9kg/m2, 22.2±7.6 events/hr) have worse central hemodynamics than males with OSA (n=9, 47±3yrs, 35.0±1.4kg/m2, 31.7±6.2 events/hr). Data collection consisted of an overnight sleep study (polysomnography) to identify the presence, and characterize the severity, of OSA via the apnea-hypopnea index (AHI). Then, duplicate measures of central (aortic) and peripheral (brachial) BP, as well as central artery stiffness (PWV) and wave reflection (AIx), were made in the morning after ≥15min of quiet, supine rest. Additional hemodynamic measurements were made if data differed by ≥2% (AIx) or ≥0.5m/s (PWV). Data that met these criteria were averaged and used for analysis. Comparisons were made using two-tailed, independent samples t-tests or nonparametric Mann-Whitney Rank Sum tests, as indicated. No differences were observed in age (p=0.44), body mass index (p=0.96), nor AHI (p=1.00) between sexes. Similarly, no sex differences were observed in brachial systolic (female vs. male: 129±7 vs. 134±8mmHg, p=0.83), diastolic (77±5 vs. 85±4mmHg, p=0.26), or mean BP (96±6 vs. 101±5mmHg, p=0.45). Similarly, central systolic (122±6 vs. 125±7mmHg, p=0.96), diastolic (78±5 vs. 86±4mmHg, p=0.25), and augmentation (21±2 vs. 15±2mmHg, p=0.07) BP did not differ between sexes. Resting heart rate was also comparable between groups (67±3 vs. 65±2bpm, p=0.64). However, AIx (AIx; 42±3 vs. 35±4%) and AIx normalized to a heart rate of 75bpm (42±3 vs. 30±3%) were higher in females with OSA relative to males with OSA (p<0.05 for both). Interestingly, the forward (30±3 vs. 28±2mmHg, p=0.63) and reflected (20±2 vs. 18±2mmHg, p=0.44) pulse heights, as well as PWV (7.3±0.6 vs. 7.4±0.6m/s, p=0.85), were similar between groups. Our data indicate that despite a trend towards higher BP in males with OSA (~5mmHg) that may be clinically, though not statistically different, females with OSA have a higher AIx. As AIx is positively associated with left ventricular hypertrophy, these data also suggest that altered central (aortic) hemodynamics may contribute to the sex differences in cardiovascular risk with OSA. This work was supported by the National Institutes of Health T32-HL007111 and U54AG044170 (JMB), HL065176 (PS and VKS), and HL134885 (VKS). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Short sleep duration is associated with future cardiovascular disease including hypertension, especially in females, but mechanistic evidence is largely lacking. Experimental sleep restriction has been shown to cause short sleep-mediated tachycardia and reduced heart rate variability, indicative of autonomic cardiac instability, in some but not all studies. Heart rate (HR) response to spontaneous cortical arousals can be regarded as an index of autonomic cardiac control given its characteristic temporal pattern. Whether the HR response to cortical arousals is altered during experimental sleep restriction remains unknown. We hypothesized that sleep restriction would result in augmented HR reactivity to spontaneous arousals, and that this response would be more marked in females. We studied 19 young, healthy subjects (eight females, age: 23.5 ± 5 years, body mass index [BMI]: 24.5 ± 3.6 kg/m2). In a randomized, crossover design, participants were tested during a normal sleep (NS, 9 hr sleep opportunity) or sleep restriction (SR, 4 hr sleep opportunity) study arm. Each 16-day study visit consisted of a 4-day study acclimation phase, 9-day experimental sleep perturbation segment, followed by a 3-day recovery period. An overnight polysomnography (PSG) study was conducted on study days 2, 5 and 11 (at baseline and after 1 and 7 days of restricted versus control sleep). A registered PSG technician provided sleep staging and spontaneous arousal scoring, which was confirmed by an investigator with sleep expertise (IMG). For each identified spontaneous arousal, the HR change was quantified for 30 cardiac cycles post-arousal onset and compared to a pre-arousal baseline (average HR 5 – 10 cardiac cycles prior to arousal). An arousal was considered valid for analysis if at least one minute of stage N2 or rapid eye movement (REM) sleep occurred following sleep stage transition and was at least 30 seconds apart from other sleep disturbance-related events (e.g., limb movement, apneic event, etc.). Given low prevalence of slow wave sleep (SWS) arousals, arousals occurring in that sleep stage were excluded from present analysis. Statistical analysis, including repeated measures ANOVA (RMANOVA, p < 0.05) with condition and time (cardiac cycles) as within-subjects factors and sex as the between-subjects factor, was conducted to compare the beat-to-beat HR response to arousal between study conditions. Additionally, average and peak HR responses to spontaneous arousals were compared via RMANOVA with condition and time (study days) as within-subjects and sex as between-subjects factors. HR response to spontaneous arousal in stage two and REM sleep was similar between NS and SR on study day 5 or day 11 (condition × time: p = 0.943, 0.865). Secondary analysis of average and peak HR responsiveness to spontaneous arousals in stage two and REM sleep between conditions and study days was also not statistically different (condition × time: p = 0.193, 0.185). Last, no significant sex differences were detected across comparisons (condition × time × sex: p > 0.05 for all). These findings suggest that a 9-day sleep restriction paradigm may not alter cardiac autonomic function in otherwise healthy adults. Future work is warranted to determine whether these results extend to individuals with established cardiovascular risk factors or cardiovascular disease. Support: NIH T32-DK007013, R01-114676, and CTSA grant UL1-TR002377. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Diabetes Mellitus (DM) affects over 530 million people globally and is a condition characterized by high blood glucose (BG) that can result in severe long-term health consequences if poorly managed. To mitigate the risks associated with DM, it is crucial to regulate BG levels through regular monitoring. Current methods of monitoring BG come with drawbacks in the form of pain and expense. With the rapid increase in prevalence of DM, there is an increasing need for economical, accurate, and non-invasive continuous measures of BG. Here we present a validation for a novel sensor that rapidly scans through a wide range of radio frequencies (RF) and uses machine learning (ML) techniques for the purpose of non-invasive BG measurement. After model training, the RF sensor and ML techniques employed in this study can predict BG at a level significantly better than chance, as shown below. Data were collected from 13 healthy participants over a series of tests lasting 2 – 3 hours. During each test, participants placed their forearm on an RF sensor that measured their BG levels using sweeps across the 500 MHz – 1500 MHz range at 0.1 MHz intervals. Participants ingested 37.5 grams of glucose solution to generate BG readings across normoglycemic and hyperglycemic ranges. Each sweep took approximately 22 seconds, including a one second pause between sweeps. Across 110 tests, we collected 3,311 BG observations. Concurrent measurements from a Continuous Glucose Monitor (CGM) were taken as reference. Data were divided using a 60-20-20 (training-validation-test) split and trained on a Light Gradient-Boosting Machine (lightGBM) model to predict BG values. A ‘blind’ evaluation of model performance was determined using the held-out test dataset. We predicted BG values in the held-out test dataset with a Mean Absolute Relative Difference (MARD) of 10.8% in the normoglycemic range and 15.9% in the hyperglycemic range (11.27% overall MARD). These results were significantly better than a chance model (two-sample t-test, p <0.01). In a Surveillance Error Grid analysis of model accuracy, 89.0% of predictions fell in Risk Level 0, 10.1% in Risk Level 1, and 0.9% in Risk Level 2. These results demonstrate that the novel RF sensor and ML techniques described can predict a reference BG value in the population under study. More research is underway to refine and expand these methods using a gold-standard blood glucose reference device and among a broader participant population with an expanded glucose range. DK and VS are consultants for and own stock in Know Labs. JA and KC are employed by and have stock options in Know Labs. CW and KP are consultants for Know Labs. NA. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Introduction: LMNA encodes the nuclear envelope proteins lamin A/C that are involved in ageing processes. Mutations in LMNA cause a spectrum of phenotypes which include muscular dystrophy, arrhythmogenic dilated cardiomyopathy, peripheral neuropathy, progeroid syndrome and overlap syndrome. Early diagnosis is crucial to prevent sudden cardiac death. We hypothesized that LMNA mutations result in an ECG biological age older than chronological age. Methods: In this study, we used AI-enabled ECG network to investigate standard digital 12-lead ECGs in genetically characterized LMNA patients. Results: We identified 33 LMNA patients with 331, 12-lead ECG available for analysis. Median age at symptoms onset was 23.5 (range: <1 to 61 years;15 females). Mean follow up time was 86 months (range: <1 to 367 months). Symptoms at onset included muscle weakness (n=10) or cardiac symptoms (n=18) and asymptomatic (n=5). We included a total of 1532 age sex matched controls. We found significant differences in positive age-gap (biologically older than chronological age) comparing LMNA patients (median of 17.5) versus controls (4.9 years; p<0.001, Figure 1 ). AI-ECG age with 10 years or higher age-gap was observed in 73% of LMNA patients compared to 27% in the controls (p<0.001). Consecutive serial analysis for all ECG’s in the study versus predicted age showed faster aging when comparing the rates of age-gap overtime in the LMNA group compared to controls (p<0.0001, Figure 2 ). Moreover, the subset analysis of 23 ECG in the 5 asymptomatic patients versus controls also showed significantly higher AI-ECG age-gap (p=0.004) Conclusions: An AI-ECG algorithm to detect biological age effectively demonstrates accelerated aging in patients with LMNA mutations related to physiological aging. Our findings further validate the role of AI-ECG to detect accelerated aging, expanding previous studies demonstrating higher mortality among individuals with in increased positive age-gap.

Objectives: To assess compliance with chronic coronary artery disease (CAD) secondary prevention guidelines in the USA, to determine time trends, and to identify potential sociodemographic predictors of low compliance. Design, Setting and Participants: We analyzed the National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2010, representative of non-institutionalized civilian population in the USA. We included people with self-reported previous diagnosis of CAD, angina or myocardial infarction (MI). Primary Outcome: Adherence to secondary prevention guidelines including blood pressure control, lipid control, not smoking, normal body mass index, physical activity at goal, and meeting all 5 outcomes (“all-or-none”). Use of antiplatelet therapy was reported and analyzed from 1999-2004. Secondary Outcome: Use of beta-blocker therapy in people with history of MI; in diabetes (DM), use of ACE-inhibitors or angiotensin receptor blocker, and HgbA1C <7%. Age, gender, race, income, education and insurance status were evaluated as covariates. Analyses were weighed according to Center for Disease Control guidelines. Results: There were 62,160 individuals in the cycle data of which 2,615 comprised the study group, representative of 73,574,548 individuals in the USA. Mean age was 65 yr, 58% men, 79% white. Only 2.8% of people with CAD met all 5 main outcomes. Only blood pressure and LDL control showed significant improvement over time, see Figure. Antiplatelet therapy was reported in 58.2%. Of those with history of MI, only 50.5% were on a beta-blocker and of those with history of DM, 59.4% were using either an ACE-I or an ARB; and 54% had a HgbA1C less than 7%. When adjusting for demographics, no significant differences were found regarding compliance with all 5 main outcomes. Conclusions: Adherence to guidelines for secondary prevention of CAD is suboptimal. There is an immense opportunity for improvement in secondary prevention for CAD in the USA.

Purpose: Subjects with normal weight obesity (NWO), defined as normal BMI but high body fat (BF)%, are at increased cardiovascular risk. Factors contributing to NWO are not well studied. We hypothesized that NWO is associated with reduced physical activity and unhealthy eating patterns. Methods: We analyzed 5942 people ≥18 years with normal BMI (18.5-24.9 kg/m 2 ) from the NHANES III survey (1988-1994), a representative sample of the US population. Subjects were classified as NWO based on BF% (>35% in women and >25% in men). Leisure-time physical activity was measured with a standard survey. Subjects were stratified as INACTIVE (no physical activity reported), ACTIVE (3-6 METs for ≥ 5 times/week or >6 METs, ≥3 times/week), or insufficiently active (those who were not INACTIVE but did not meet criteria for ACTIVE). Total activity was expressed in METs/hr/month, assuming an exercise duration of 30 minutes for each day reported. A Healthy Eating Index was based on 24-hour dietary recall data. Analyses were weighted for the complex study design. Results: Mean age was 43 years, 54.4% were men; 1170 people (19.7%) with normal BMI had NWO. Table shows demographics, physical activity and nutritional habits by group. After controlling for age, gender, poverty levels, smoking status and Healthy Eating Index, METs/hr/month was an independent predictor of NWO (OR per 100 MET/hours/month is 0.72(95%CI 0.60;0.88). People who were inactive and insufficiently active had higher odds of NWO as compared with active people (OR-1.47; 95%CI 1.10;1.96 and OR-1.60, 95% CI 1.25; 2.05, respectively). Also, strength training was independently associated with a decreased odds of NWO (0.63; 95%CI 0.44;0.89). Dietary pattern was not predictive of NWO. Conclusions: Physical activity, particularly resistance exercise, but not diet, was associated with NWO independently of potential confounders. Promoting physical activity may be an important interventional strategy in these patients.

Tomas Konecny, 2012 Finalist and Presenting Author

Thais Coutinho, 2011 Finalist and Presenting Author