Olson, Thomas P.

Introduction: Obesity is prevalent in HFpEF, a syndrome that disproportionality affects women, and is associated with poor exercise tolerance (VO 2 ), reduced quality of life, and greater symptoms. Visceral adipose tissue (VAT), more than general obesity, is related to metabolic and cardiovascular dysfunction common to HFpEF. While fat deposition varies between sexes, these differences and the relation to exercise tolerance remain unclear in HFpEF. Hypothesis: Compared to sex-matched controls, HFpEF patients will demonstrate reduced lean mass, excess VAT, subcutaneous, visceral, and intramuscular fat, and will be associated with reduced VO 2 . Methods: HFpEF females (F) and males (M) (n=78; 37 F/41 M; age 70±9) and non-HFpEF controls (n=50; 30 F/20 M; age 65±5) prospectively underwent body composition measurement via dual-energy x-ray absorptiometry and magnetic resonance imaging (MRI) and cardiopulmonary exercise testing for measurement of peak oxygen consumption (peak VO 2 ). Muscle quality calculated as peak Watts (W) achieved divided by MRI thigh lean mass. Results: HFpEF had greater VAT, subcutaneous (SAT) and intramuscular (IMAT), and poorer muscle quality than controls (all p<0.01). Exercise performance was lower in HFpEF (peak VO 2 : 14±4 vs. 22±6; peak W: 69±29 vs. 116±34; both p<0.01). HFpEF demonstrated increased odds for VAT (OR: 2.02, 95% CI: 1.22-3.37), SAT (OR: 2.05, 95% CI: 1.20-3.49), and IMAT (OR: 2.04, 95% CI: 1.35-3.39) (all p<0.01). Sex-stratification revealed increased odds of excess VAT in HFpEF females (OR: 2.76, 95% CI: 1.16-7.72, p=0.03), SAT in HFpEF males (OR: 3.64, 95% CI: 1.10-12.07, p=0.03), and IMAT in HFpEF females (OR: 2.38, 95%CI: 1.10-5.29, p=0.03) with a trend in males (OR: 1.67, 95%CI: 0.97-2.86, p=0.06). Impaired VO 2 was associated with all AT depots as a combined cohort and among controls (all p<0.01) and linked to poor muscle quality individually for men and women in HFpEF and controls (all p<0.01). Conclusion: HFpEF patients have significantly altered body composition profiles and reduced exercise tolerance compared to controls. Sex differences in regional fat deposition impact peak VO 2 and may serve a distinct role in the pathophysiology of HFpEF.

Introduction: Patients undergoing cardiac rehabilitation (CR) exhibit various degrees of illness severity and high prevalence of chronic conditions (CC). Cardiorespiratory fitness (CRF) is a key predictor of outcomes in patients with cardiovascular disease. Participation in CR is associated with increased CRF, reduced future cardiovascular events, and mortality. Peak oxygen uptake (VO 2 ) is the gold-standard method to evaluate CRF; however, the burden of multimorbidity and its impact on CRF improvement has not been extensively studied. Methods: We included 618 patients who were ≥ 18 years old and underwent cardiopulmonary exercise testing prior to and after CR at Mayo Clinic. Using the Rochester Epidemiology Project records- linkage system, we assessed the prevalence of 18 CC defined by the US Department of Health and Human Services. To evaluate changes in peak VO 2 in patients with multimorbidity (categorized as ≥6 CC), we created logistic regression models adjusting for age, sex, and baseline peak VO 2 . Results: Mean age was 61.5 ± 11.0 years and 75.5% were male. Most patients (81.2%) attended ≥18 CR sessions. Half (50.4%) of patients had at least 6 CC (median 6; range 0-13). Overall, peak VO 2 at baseline was 20.7 ± 6.3 mL/kg per min and the percent change in peak VO 2 following CR was 11.4% ± 21.6%, Figure 1A . Multimorbidity was associated with lower improvement in peak VO 2 following CR compared to patients with <6 CC (7.5% vs 11.4%, p=0.0001). Furthermore, across categories of CRF improvement (i.e., 1%, 5%, and 10% percent change in peak VO 2 ) lower multimorbidity was associated with a 1.5-2-fold increase in CRF when compared to those with higher multimorbidity (≥6 CC), Figure 1B . Conclusions: Patients entering CR have a high multimorbidity burden. Our results suggest that multimorbidity in CR patients attenuates improvement in peak VO 2 . Individualized interventions should focus on managing and reducing multimorbidity to minimize the negative impact on CRF improvement.

Introduction: Multimorbidity is associated with physical disability, increased health care utilization, reduced quality of life, and mortality. Whereas most literature has focused on the co-occurrence of somatic conditions, we aim to (i) establish the prevalence of combined somatic and mental health multimorbidity (SMM) in cardiac rehabilitation (CR) patients, and (ii) determine the most prevalent co-occurrence of cardio-metabolic conditions (CMC) with other somatic or mental health conditions in CR patients. Methods: Using the Rochester Epidemiology Project records-linkage system, we identified residents of Southern MN and Western WI who attended (≥1) CR sessions from 1995 to 2019. The prevalence of 18 chronic conditions defined by the US Department of Health and Human Services was electronically extracted. Heat maps were created to assess the pairwise prevalence of cardio-metabolic (CMC; n=6) with mental (n=5) and other somatic (n=7) chronic conditions (Table 1) . Results: We included 13,056 patients; 68.5% were male. Patients were 65.5±12.5 years old with 44.7% ≤65 years old. Median number of conditions was 7 (Range 0-18) with 52.6% of patients having ≥7 conditions. Hyperlipidemia (89.3%), hypertension (79.5%), and arrhythmias (72.4%) were the most prevalent CMC. The prevalence of combined SMM (≥2 conditions) was 44.9% and was higher in those <65 years ( p <.0001). Depression (26.8%) and anxiety (24.7%) were the most common mental conditions involved in SMM and frequently co-occurred with CMC. Among other prevalent somatic conditions, arthritis (51.7%), chronic obstructive pulmonary disease (33.5%), and cancer (29.3%) co-occurred with CMC (Figure 1). Conclusions: Half of patients in CR have ≥7 chronic conditions. Somatic and mental health multimorbidity (SMM) is highly prevalent and is more frequent in those younger than 65 years old. Our results highlight the critical need to better understand the clinical burden of SMM in patients undergoing CR.

Introduction: The presence of chronic somatic and mental health conditions is complex and challenging for patients and the health care system due to the health deficit accumulation, worsening outcomes, and increased health care utilization. While previous studies on multimorbidity have focused on somatic chronic conditions, we aim to assess the relation between somatic, mental, and combined somatic-mental multimorbidity (SMM) and impact on Major Adverse Cardiac Events (MACE) in patients enrolled in phase II outpatient cardiac rehabilitation (CR). Methods: Using the Rochester Epidemiology Project records-linkage system, we identified patients from Olmsted County, Minnesota who were 18 years old and attended (≥1) CR sessions at Mayo Clinic Rochester. The prevalence of 18 (somatic=13; mental=5) chronic conditions defined by the US Department of Health and Human Services was ascertained electronically. Results: We included 618 patients; 24.4% were female. Patients were 61.5±11.0 years old with 38.0% ≥65 years old. Overall median number of chronic conditions was 6 (Range 0-13) with 98.8% of patients having ≥2 conditions. The prevalence of combined SMM was 44.6%. Mean follow-up was 7.3± 4.5 years, 147 (23.7%) patients had MACE (number): acute coronary syndrome (46), percutaneous coronary intervention (33), heart failure (16), stroke (8), coronary artery bypass grafting (4), ventricular arrhythmias (4), or death (65). Risk of MACE in patients with SMM was significantly higher compared to patients with only somatic conditions (HR: 1.62, 95% CI: 1.14-2.30, p=0.01 ), Figure 1. The association remained significant after adjustment for age and sex ( p=0.006 ). Conclusions: Our results show that somatic-mental multimorbidity (SMM) in CR patients is highly prevalent and imposes a higher risk of MACE compared with patients who have only somatic conditions highlighting the importance of individualized assessment and care plans to reduce adverse events and mortality.

Introduction: High intensity interval training (HIIT) during early outpatient cardiac rehabilitation (CR) improves peak oxygen uptake (VO 2 ) and reduces mortality in patients with cardiovascular disease. In a CR setting, prescribing interval intensity using rating of perceived exertion (RPE) is more feasible than using percent of VO 2peak . Additionally, prescribing HIIT with RPE may allow for progressive changes in the VO 2 response throughout a single HIIT session. However, the acute cardiopulmonary responses during individual intervals of HIIT prescribed via RPE in CR are unknown. Hypothesis: We hypothesized that VO 2peak would increase and time-to-peak (TTP) would shorten across high-intensity intervals during a single HITT session prescribed with RPE. Methods: Patients with CAD (N=14; 2 women; 56±9 yrs; 30.46±4.7 kg/m 2 ) referred to CR after an acute coronary event completed a minimum of 25 supervised HIIT sessions during which VO 2 was measured continuously. Each HIIT session included 5 or more (6±1 sessions) 1-minute high intensity intervals (target RPE: 15-17) separated by a 3 to 5-minute low intensity interval (target RPE: <12). The first and last HIIT sessions were used for analysis. VO 2peak was defined as the highest 3-breath averaged VO 2 during or up to 1 minute following each high intensity interval. Results: RPE was 15±2 and 11±2 during the high and low intensity intervals, respectively. Compared to the first HIIT session, VO 2peak was greater during the high intensity intervals of the last HIIT session (25.4±1.5 vs. 21.8±1.1 mL/kg/min; p=0.04). During the first and last HIIT sessions, VO 2peak increased across high intensity intervals (p<0.001). Although TTP was not significantly reduced across intervals during the first HIIT session (p=0.14), TTP was reduced across intervals during the last HIIT session (first interval: 67±19 vs. last interval: 58±9 s; p<0.01). Conclusions: Prescribing HIIT with RPE resulted in increased VO 2peak across high intensity intervals within single HIIT sessions and shortened TTP during high intensity intervals across CR. These findings provide evidence for a priming effect on VO 2peak during HIIT which may contribute to greater cardiopulmonary adaptations in CAD patients.

Introduction: Cardiac rehabilitation (CR) is associated with improvements in exercise capacity (peak oxygen uptake (VO 2 peak)); however, 23% of CR patients do not improve VO 2 peak following CR. CR adherence (i.e. number of CR sessions attended) has been proposed to play an important role in VO 2 peak improvement following CR, but has not been investigated. Identifying clinical predictors of VO 2 peak improvement are critical as the degree of VO 2 peak improvement is associated with improved long-term outcomes in these patients. Purpose: To determine if clinical characteristics and number of CR sessions attended were predictive of patients who exhibited improvement in VO 2 peak following CR. Methods: Using the Rochester Epidemiology Project records-linkage system, we identified all consecutive patients >18 years old from Olmsted County, Minnesota who underwent cardiopulmonary exercise testing prior to and following CR from 1999 to 2017. Regression models were created to assess clinical predictors of VO 2 peak improvement (>0% baseline) following CR. Results: The analysis included 804 patients (75% men) with a mean age of 60.7±12.4 years. Of the 804 patients, 609 (76%) patients exhibited VO 2 peak improvement following CR. Significant univariate predictors of VO 2 peak improvement included younger age (OR: 0.99, 95% CI: 0.97-1.00), lower pre-CR VO 2 peak (OR: 0.97, 95% CI: 0.94-0.99), history of peripheral artery disease (OR: 0.48, 95% CI: 0.32-0.73), and smoking (OR: 0.63, 95% CI: 0.45-0.90). Significant independent predictors of VO 2 peak improvement from a multivariate analysis included younger age (OR: 0.98, 95% CI: 0.96-1.00), number of CR sessions (OR: 1.02, 95% CI: 1.01-1.03), lower pre-CR VO 2 peak (OR: 0.94, 95% CI: 0.91-0.96), history of peripheral artery disease (OR: 0.42, 95% CI: 0.27-0.66), and smoking (OR: 0.56, 95% CI: 0.39-0.82). Conclusions: These findings demonstrate the importance of CR adherence and individual clinical characteristics in influencing VO 2 peak improvement following CR in patients with CVD. These findings may influence the clinical practice by providing specific clinical characteristics to account for when personalizing the CR treatment plan to maximize positive outcomes.

Introduction: Peripheral factors contribute to exercise limitations in heart failure (HF) with preserved ejection fraction (HFpEF) and may be related to excess regional adiposity. Slowed oxygen uptake (VO 2 ) kinetics are common in HF with reduced ejection fraction (HFrEF) and reflect peripheral O 2 transport impairments and abnormal muscle metabolic control. Differences in VO 2 kinetics between HFpEF and HFrEF may reveal phenotype-specific impairments in peripheral O 2 transport but remain unknown. Hypothesis: We hypothesized that VO 2 kinetics are slower in HFpEF (compared to HFrEF) and associated with greater leg adiposity. Methods: Patients with HFpEF (n=12; 3 women; 66±6 yrs; 29±3 kg/m 2 ; ejection fraction: 59±5%), HFrEF (n=12; 3 women; 62±9 yrs; 28±4 kg/m 2 ; ejection fraction: 37±8%), and controls (n=12; 3 women; 65±5 yrs; 28±3 kg/m 2 ) cycled at 20 W for 5 minutes prior to a maximal exercise test to determine VO 2peak . Age, sex, and BMI were matched by design and were not different among groups (p>0.05). A monoexponential function with a time delay was used to describe the VO 2 transition from rest to 20 W and calculate the O 2 deficit. Leg fat mass was measured via dual energy X-ray absorptiometry. Results: VO 2peak was lower in HFpEF (18.6±6.2 mL/kg/min) and HFrEF (19.0±3.7 mL/kg/min) compared to controls (26.0±3.1; both p<0.05). Amplitude (HFpEF: 507±97 mL/min; HFrEF: 443±91 mL/min; controls: 477±117 mL/min) and time delay (HFpEF: 16±9 s; HFrEF: 11±7 s; controls: 11±6 s) were not different among groups (p>0.05). The time constant was slower in HFrEF compared to controls (45±9 s vs. 32±6 s; p=0.03) and further slowed in HFpEF (66±18 s; p<0.001). The O 2 deficit was significantly greater in HFpEF (895±201 mL) and HFrEF (649±131 mL) compared to controls (590±125 mL; both p<0.05). There were no differences in percent leg fat mass among groups (HFpEF: 33±8%; HFrEF: 31±6%; controls: 34±9%) and no associations were detected between percent leg fat mass and any VO 2 parameters (all p>0.05). Conclusions: Patients with HFpEF exhibited slower VO 2 kinetics compared to HFrEF with no association to leg adiposity. These data suggest differences in peripheral O 2 transport and intramuscular metabolic control abnormalities between HF phenotypes.

Introduction: Heart failure patients with preserved ejection fraction (HFpEF) may exhibit inspiratory muscle dysfunction potentially contributing to exercise intolerance. Activation of neural afferents in response to inspiratory muscle contraction (metaboreflex) elicits increases in mean arterial pressure (MAP) and leg vascular resistance (LVR) through enhanced sympathetic outflow leading to decreases in leg blood flow (LBF) in healthy humans. It is unknown if HFpEF patients exhibit exaggerated cardiovascular responses to inspiratory muscle metaboreflex activation. Hypothesis: We hypothesize that patients with HFpEF will exhibit a greater increase in MAP and LVR with a decrease in LBF during inspiratory muscle metaboreflex activation than controls. Methods: HFpEF patients (n=15, 10M/5W, 69±10 yrs; 33±4 kg/m2) and controls (n=14; 10M/4W; 70±8 yrs; 28±4 kg/m2) performed inspiratory resistive breathing tasks (IRBT) at 2% and 60% of their maximal inspiratory pressure. During the IRBTs, the breathing frequency was 20 breaths/min with a 50% duty cycle. At rest and during the IRBTs, MAP was measured by photoplethysmography, venous norepinephrine was measured, LBF was measured by near-infrared spectroscopy and indocyanine green dye injections, and LVR was calculated. Results: During the 2% IRBT, there were no differences between groups in the changes from rest in MAP (HFpEF: 2±4 vs. CTL: 3±4 mmHg) or LBF (HFpEF: 1±14 vs. CTL: 1±12%) (both, p>0.05). During the 60% IRBT, venous norepinephrine increased from rest in HFpEF and controls (p<0.05), while no differences existed between groups (p>0.05). During the 60% IRBT, HFpEF patients, compared to controls, exhibited a greater increase from rest in MAP (HFpEF: 16±7 vs. CTL: 10±6 mmHg) and LVR (HFpEF: 76±45 vs. CTL: 32±19%) coupled with a greater decrease in LBF (HFpEF: -32±14 vs. CTL: -17±9%) (all, p<0.05). Conclusions: These data indicate that patients with HFpEF exhibit exaggerated cardiovascular responses with inspiratory muscle metaboreflex activation compared to healthy individuals. These findings have important implications for the integrative physiology of exercise in HFpEF patients.

Introduction: Assessment of cardiac function during exercise is critical in the clinical evaluation of patients with heart failure with preserved (HFpEF) or reduced (HFrEF) ejection fraction. Direct invasive measurement of cardiac power (CP) and stroke work (SW) are robust measures of cardiac function. Non-invasive gas exchange based surrogates may provide practical means of assessing cardiac function in clinical and research settings; however, the relationship between invasive and non-invasive measurement of CP or SW during exercise is unclear in HFpEF and HFrEF. We aimed to assess relationships between invasive and non-invasive measures of CP and SW during exercise in HFpEF and HFrEF. Methods: HFpEF (n=47) and HFrEF (n=29) (age 71±10 vs 55±10 yrs; EF% 62±8 vs 21±6%; BSA 2.1±0.3 vs 2.1±0.2 m 2 ; weight 99±22 vs 87±15 kg; peak work 36±14 vs 37±13 watts (W), respectively; all p<0.05, except peak work) underwent peak exercise testing during right heart catheterization. Invasive measurement of cardiac pressures, hemodynamics, and arterial pressures were recorded at rest, 20 W, and peak work. Continuous measurement of HR and VO 2 occurred from rest to peak work via 12-lead ECG and metabolic measurement system, respectively. Invasive measurements were calculated as, CP=(MAP–RAP) x Q x 2.22х10 -3 ; SW=(MAP–PCWP) x SV I x 0.133; and non-invasive as, CP=VO 2 x MAP; SW=VO 2 /HR x MAP. All indices adjusted for body surface area. Results: At rest, there were moderate relationships between invasive and non-invasive measures of CP and SW in HFpEF (r=0.56 and 0.48) and HFrEF (0.57 and 0.73), respectively (all p<0.05). At 20 W, relationships between invasive and non-invasive measures of CP and SW strengthened in HFpEF (r=0.74 and 0.69) and HFrEF (0.74 and 0.72), respectively (all p<0.05). The relationships noted between invasive and non-invasive measures of CP and SW at 20 W persisted at peak work in HFpEF (r=0.74 and 0.67) and HFrEF (0.78 and 0.69), respectively (all p<0.05). Conclusion: These data suggest that non-invasive estimates of CP and SW correlate closely with invasive measures and may be reasonable surrogates for these indices during peak exercise testing in both HFpEF and HFrEF patients.

Introduction: Invasive measurement of cardiac power (CP) and stroke work (SW) during exercise are robust indices of cardiac function and relate to syndrome severity and prognosis in heart failure patients with preserved (HFpEF) or reduced (HFrEF) ejection fraction. Because invasive measurement via catheterization laboratories are not always readily available, non-invasive cardiopulmonary exercise testing to estimate cardiac function is routinely performed in the clinical evaluation of HFpEF and HFrEF. However, it is unknown how VO 2 relates with CP and SW in HFpEF vs HFrEF. We compared relationships between CP and SW with VO 2 at rest and peak exercise in HFpEF vs HFrEF. Methods: 30 HFpEF and 32 HFrEF (EF: 62±2 vs 22±1%; males: 17 vs 30; age: 70±2 vs 55±2 yrs; BMI: 34±1 vs 28±1 kg/m 2 ; wt: 98±4 vs 86±3 kg (all p<0.05)) participated in exercise testing during right heart catheterization. Oxygen consumption (VO 2 ) was measured continuously from rest to peak work. Invasive measures at rest and peak work included mean radial arterial pressure (MAP), right atrial pressure (RAP), and pulmonary capillary wedge pressure (PCWP). We calculated CP=(MAP–RAP) x Q I x 2.22х10 -3 and SW=(MAP–PCWP) x SV I x 0.133. Results: At rest, R 2 between VO 2 and CP or SW were not different between HFpEF (CP=0.41, SW=0.30) and HFrEF (CP=0.39, SW=0.15). Slopes did not differ significantly between HFpEF (CP=0.13, SW=0.01) and HFrEF (CP=0.16, SW=0.01) for regressions between VO 2 and CP or SW; but, fitted intercepts of the regression showed significantly higher intercepts in HFrEF (CP=0.89, SW=1.16) vs HFpEF (CP=0.05, SW=0.27) for VO 2 vs CP and SW, respectively. At peak exercise, R 2 between VO 2 and CP or SW were not different between HFpEF (CP=0.48, SW=0.64) and HFrEF (CP=0.70, SW=0.67). Slopes did not differ between HFpEF (CP=0.18, SW=0.02) and HFrEF (CP=0.15, SW=0.02) for regressions between VO 2 and CP or SW, whereas the fitted intercepts the slope of the regression were significantly higher in HFrEF (CP=5.1, SW=4.4) vs HFpEF (CP=3.2, SW=2.0) for VO 2 vs CP and SW, respectively. Conclusion: Our data suggest that despite lower VO 2 relative to CP and SW in HFpEF, both the strength of relationship and rate of increase in VO 2 relative to CP and SW are similar between HFpEF and HFrEF at rest and peak exercise.