Effects of weight loss and weight loss maintenance on cardiac autonomic function in obesity: a randomized controlled trial

Abstract

To investigate relationships between weight loss and weight loss maintenance with cardiac autonomic function and exercise in obesity, 39 adults (45.7 ± 10.7 years; BMI: 34.2 ± 3.4 kg·m⁻²) participated in a 10-week, medical weight loss program combined with aerobic exercise. A subset (n = 18) participated in an aerobic exercise weight loss maintenance program (550 or 970 MET min·week⁻¹) for 18 additional weeks. Primary outcomes included markers of cardiac autonomic function assessed by heart rate variability (HRV) (i.e., SDNN, RMSSD, HFln). Following weight loss, we observed significant improvements for SDNN (48.2 [41.4–55.1] vs. 55.1 [45.7–64.4] ms, p = 0.03), RMSSD (37.7 [29.1–46.4] vs. 47.9 [37.4–58.4] ms, p = 0.002), and HFln (5.88 [5.39–6.36] vs. 6.32 [5.86–6.78] ms, p = 0.001). Regression analyses showed fasting insulin concentration predicted 24% and 27% of the variance in RMSSD (r² = 0.236, p = 0.007) and HFln (r² = 0.274, p = 0.004), respectively. Following weight loss maintenance, no significant changes in HRV were observed. Changes in LDL (r=−0.54, p = 0.04) and non-HDL (r=−0.77, p = 0.001) were inversely associated with RMSSD changes. Clinically significant weight loss via caloric restriction and aerobic exercise improved HRV markers of cardiac vagal modulation. Following weight loss maintenance, we did not observe any further changes in HRV. Thus, our data suggest that commonly prescribed exercise volumes contribute to maintenance of parasympathetic modulation following medical weight loss programming and exercise.

Introduction

Obesity is a major risk factor for cardiovascular disease (CVD) and CVD mortality (Bischoff et al. 2017). An underlying mechanism for CVD risk in obesity is cardiac autonomic dysfunction (Kaufman et al. 2007), defined as an imbalance between cardiac parasympathetic nervous system (PNS) and sympathetic nervous system (SNS) activation (Thayer et al. 2010). Decreased heart rate variability (HRV), a non-invasive quantitative marker of cardiac autonomic function is associated with the development of hyperglycemia, hypertension, diabetes, and CVD, which are more prevalent in obese than normal weight individuals (Thayer et al. 2010; Stein et al. 2008; Wulsin et al. 2015; Guarino et al. 2017; Triggiani et al. 2019). Low HRV (i.e., autonomic dysfunction) is associated with a 32%–45% elevated risk of a first cardiovascular event and a 41%–48% elevation in 10-year risk of coronary heart disease development (Yoo et al. 2011; Hillebrand et al. 2013). Importantly, adults with overweight or obesity are at greater risk of cardiac autonomic dysfunction compared to lean adults (Fraley et al. 2005; Guarino et al. 2017; Triggiani et al. 2019). Adults with obesity have reduced parasympathetic influence and an elevated sympathetic influence on cardiac autonomic function (i.e., lower HRV) than lean adults. This cardiac autonomic imbalance is suggestive of a maladaptive nervous system, which is associated with elevated risk of CVD mortality (Wulsin et al. 2015). Thus, markers of autonomic function, measured by HRV, are an important health measure for intervention strategies to reduce CVD risk and overall health outcomes for those with overweight or obesity.

Current guidelines recommend clinically significant weight loss (CWL) (5%–10% body weight) for individuals with overweight or obesity to improve CVD risk factors (Jensen et al. 2014). Caloric restriction and increased levels of physical activity are major components of lifestyle-based obesity treatment programs (Livingston and Zylke 2012). In terms of cardiac autonomic function in obesity, recent evidence indicates that weight loss effects on HRV are potentiated by the combination of caloric restriction and exercise, compared to caloric restriction alone (Drbošalová et al. 2010; Prado et al. 2010). Observational studies and controlled clinical trials have presented mixed results for weight loss through hypocaloric diet to improve HRV (Yamamoto et al. 2001; Esposito et al. 2003; Poirier et al. 2003; Adachi et al. 2011; Müller et al. 2015). However, studies demonstrating 7%–17% weight loss from caloric restriction observed improvements in HRV indices related to autonomic balance (Akehi et al. 2001; Poirier et al. 2003; Müller et al. 2015) and cardiac parasympathetic and sympathetic modulation (Akehi et al. 2001; Poirier et al. 2003). Additionally, improvements of HRV in obesity associated with exercise have been observed independent of weight loss (Earnest et al. 2008; Earnest et al. 2010). Importantly, there is a lack of studies investigating the effects of a combined caloric restriction and exercise intervention on HRV in adults with obesity. To date, there are no studies to our knowledge which address the impact of exercise volume during weight loss maintenance on HRV following weight loss. Previous cross-sectional and longitudinal data indicate that greater total physical activity levels are associated with greater cardiac vagal modulation and overall cardiac autonomic balance (Soares-Miranda et al. 2014; Tornberg et al. 2019). A dose-dependent increase in vagal-mediated HRV has been reported with volumes of exercise training in the Dose-Response to Exercise in Women (DREW) study, where improvements were solely exhibited in exercise levels at or above physical activity recommendations (Earnest et al. 2008). Post-intervention weight regain has been associated with regression of autonomic function toward pre–weight loss levels (Gentile et al. 2007; Adachi et al. 2011; Müller et al. 2015). Therefore, the amount of exercise necessary for preserving cardiac autonomic balance after CWL has important clinical implications.

The aim of the present study was to investigate the effects of CWL and supervised aerobic exercise training on HRV in adults with overweight or obesity. Secondarily, we evaluated the impact of two aerobic exercise volumes on HRV during an 18-week weight loss maintenance condition. Lastly, we aimed to identify associations between HRV measures and traditional CVD risk factors (e.g., weight, body composition, fitness, glucose, insulin, blood lipid profile). We hypothesized that weight loss would be associated with improvements in HRV markers indicative of reduced CVD risk; these HRV adaptations would then be retained during weight loss maintenance condition at either level of aerobic exercise prescriptions.

Methods

The present investigation is an ancillary study of the Prescribed Exercise to Reduce Recidivism After Weight Loss Pilot (PREVAIL-P) study. PREVAIL-P protocol was approved by the University and Medical Center Institutional Review Board at East Carolina University (ECU) and registered on ClinicalTrials.gov (NCT03685123).

Participants and study design

The rationale and methods of the PREVAIL-P study have been previously described (Swift et al. 2021). We recruited sedentary adults (<20 min, ≤2 days·week for previous 3 months) 30–65 years of age who were either overweight (BMI: 25–29.9 kg·m⁻²) with one elevated risk factor (e.g., prediabetes, hypertension, dyslipidemia, and elevated waist circumference) or obese (Class I and II) (BMI: 30–39.9 kg·m⁻²), based on current guidelines (Jensen et al. 2014). Adults were recruited via newspaper advertisement, targeted social media advertisement, direct mailers, emails to ECU employees, and flyers. Interested individuals completed a web-based questionnaire on the study website. Individuals meeting the eligibility criteria were subsequently screened by phone for inclusion/exclusion criteria and invited for a study screening visit. At the screening visit, PREVAIL-P staff described all aspects of study participation. Individuals wishing to participate provided written informed consent. Height, weight, and resting blood pressure were assessed, and blood was drawn to evaluate hepatic, renal, hematological, endocrine, and metabolic function. Premenopausal women took a pregnancy test to confirm they were not pregnant. Following screening visits, participants underwent baseline testing for primary and secondary measurements.

Major exclusions for the PREVAIL-P study included diagnosed type 1 or type 2 diabetes (or fasting blood glucose ≥126 mg·dL⁻¹, or use of diabetes medication), known CVD (e.g., heart failure, serious arrhythmias, and peripheral vascular disease), previous stroke or myocardial infarction, excessively high resting systolic (>180 mmHg) or diastolic (>100 mmHg) blood pressure, significant medical conditions, life-threatening conditions, pregnancy or plans to become pregnant, and medical conditions that contraindicate exercise training. Individuals engaged in a diet or weight loss program or those demonstrating non-compliance during screening visits were also excluded.

Participants first underwent a 10-week weight loss condition in a combined caloric restriction and aerobic exercise program to achieve the goal of ≥7% weight loss. Those who reached CWL (≥7% weight loss) were then randomized to perform 18 weeks of aerobic exercise training consistent with either the minimum weekly physical activity recommendations (PA-REC group) or weight maintenance recommendations (WM-REC group). Participants in the weight loss condition who did not attain CWL were excluded from the weight loss maintenance condition.

In the weight loss condition, 39 participants were recruited to undergo an OPTIFAST weight loss program and supervised aerobic exercise training for 10 weeks to achieve CWL (≥7.0% weight loss). This weight loss cut point was chosen as a balance between the general 5%–10% range (Donnelly et al. 2009) and was the criteria used by The Diabetes Prevention Program Research Group (2002). The aerobic exercise intervention included treadmill walking at a heart rate (HR) associated with 50%–75% maximal oxygen consumption (VO₂ max) and a progression of weekly exercise volume from 300 to 700 metabolic equivalent of energy (MET) min across the weight loss condition.

The weight loss maintenance condition was conducted in a subset of 32 participants who achieved CWL from the weight loss condition. Participants were randomized to 18 weeks of aerobic exercise training consistent with either the minimum weekly PA-REC group (~550 MET-min per week) or WM-REC group (~970 MET-min per week). An adaptive randomization was utilized to balance the study groups by baseline BMI and magnitude of weight loss (Lachin et al. 1988). To test the effects of different exercise volumes on HRV during the weight loss maintenance condition, PA-REC and WM-REC groups were: (a) provided a caloric intake range based on resting metabolic rate (RMR) data, (b) encouraged to follow the 2015–2020 Dietary Guidelines for Americans (U.S. Department of Health and Human Services and U.S. Department of Agriculture 2015), and (c) consumed all self-prepared foods.

Assessment visits

The primary measure was the change in cardiac autonomic nervous system modulation after weight loss, using standard deviation of R-R intervals (SDNN), root mean square of successive differences of intervals (RMSSD), high-frequency (HF), and low-frequency (LF) power indices obtained through HRV (described below). Major secondary measures included weight, body composition, RMR, cardiorespiratory fitness levels, and fasted blood glucose, insulin, and lipid levels. All measures were obtained at baseline, post–weight loss (Week 10), and post–weight loss maintenance (Week 28). The study flow chart is shown in Figure S1.

HR variability

Cardiac autonomic nervous system function was assessed non-invasively through HRV according to the Task Force for Pacing and Electrophysiology (Task Force of The European Society of Cardiology and The North American Electrophysiology Society of Pacing and Electrophysiology 1996). Briefly, participants fasted and refrained from caffeine for ≥12 h, light-to-moderate exercise for ≥24 h, and moderate-to-heavy exercise for ≥48 h prior to testing. Medications and supplements taken before HRV assessment were documented and repeated at post-weight loss (Week 10) and post–weight loss maintenance (Week 28) visits. All HRV assessments were conducted in the morning hours (6:00–11:00) in a quiet, temperature-controlled, dim room. Participants wore a HR monitor (Zephyr Bioharness 3, Medtronic, Annapolis, MD) while resting in the seated position for ≥20 min and, subsequently, rested in the supine position at their normal unpaced breathing rate for 20–25 min. The final 10–12 min of supine rest was timestamped for HRV recording; analysis consisted of 10 min of valid HRV data (<10% artifact).

HRV analyses were examined using Kubios software version 3.2.0. (Biosignal Analysis and Medical Imaging Group, Kuopio, Finland). The sample frequency was 250 Hz. HRV variables collected for analysis included time-domain and frequency-domain measurements. Time-domain components included the mean of R-R intervals, SDNN, and RMSSD. SDNN is a representative of overall variability and RMSSD is the primary time-domain measure for vagal-mediated control of the heart. The fast Fourier transformation method was used to perform power spectral analysis. Frequency-domain components included total power (TP), LF (0.04–0.15 Hz), and HF (0.15–0.40 Hz) bands in log-transformed power (TPln) (LFln) (HFln), normalized units (LFnu) (HFnu), and LF/HF power ratio. LF power represents both sympathetic and cardiac vagal modulation, while HF power indicates cardiac vagal modulation.

Resting metabolic rate

RMR was measured using indirect calorimetry (TrueOne 2400, Parvo Medics, Salt Lake City, UT) with a ventilated hood canopy and dilution pump. RMR and HRV data were collected simultaneously. Thus, participants rested in the seated position for ≥20 min, then supine position for 20–25 min with the canopy covering their head and the room dimly lit. Participants were instructed to minimize body movements and remain awake for the entirety of the measurement. RMR was determined from ~10 min of data toward the end of the procedure when 1.0%–1.2% fractional content of carbon dioxide (FECO₂) dilution was steadily maintained. RMR data were considered valid if coefficients of variation for resting energy expenditure, VO₂, and VCO₂ were <10% (Fullmer et al. 2015).

Anthropometry and body composition

Weight was measured in the fasted state (≥12 h) in a hospital gown with a calibrated scale (DigiTol 8510, Mettler Toledo, Columbus, OH) recorded to the nearest 1/10 kg. Body composition was measured using dual-energy X-ray absorptiometry (Hologic, Hologic Inc., Bedford, MA). Total fat mass, fat-free mass, visceral adipose tissue (VAT) mass and volume at level of L4, and bone mass were quantified (APEX version 5.6.0.5). A food frequency questionnaire (NutritionQuest, Berkley, CA) was collected at baseline, post–weight loss condition, and post–weight loss maintenance condition. Waist circumference was measured at the natural waist—the halfway point from the inferior border of the rib cage and the superior point of the iliac crest. Additionally, a fasting blood sample was obtained and sent to a clinical laboratory (LabCorp Inc., Burlington, NC) for analyses of glucose, insulin, and lipids (e.g., HDL, LDL, VLDL).

Maximal exercise test

Maximal exercise testing was conducted on a treadmill (Cardiac Science, TM65, David Medical Electronics, Bothell, WA) using a modified Balke protocol to determine VO₂ max and appropriate HR range for aerobic exercise training. For the warm-up, participants walked at 2.0 mph with 0% grade for 2 min. Subsequently, speed increased to 3.0 mph to begin the exercise test. During the test, grade was increased by 2.5% for every 2 min until volitional exhaustion. Gas exchange (i.e., VO₂, VCO₂) and pulmonary ventilation were measured continuously by indirect calorimetry (TrueOne 2400, Parvo Medics, Salt Lake City, UT). Blood pressure, HR, rating of perceived exertion (RPE) (Borg 1982), and electrocardiogram were monitored and recorded before, during, and after the exercise test. The electrocardiogram was cleared by the study physician prior to participants beginning the weight loss intervention.

OPTIFAST weight loss program (1–10 weeks)

After baseline measurements, participants began a 10-week OPTIFAST^® weight loss program (ECU Health Wellness Center, Greenville, NC) and supervised aerobic exercise training intervention at ECU. Participants received a nutrition assessment by a registered licensed dietitian/nutritionist certified by Nestle. Nestle did not have any role in the design or research funding of the present study. OPTIFAST is a comprehensive, medically supervised weight loss program that combines lifestyle education classes and medical monitoring with portion-controlled, nutritionally balanced meal replacement products (e.g., shakes, bars, and soups) (Ard et al. 2019). The major goal of the OPTIFAST program was to provide CWL (≥7%) in PREVAIL-P participants.

Each Nestle OPTIFAST product provided 160–170 kilocalories, 14 g protein (whey, casein, or soy), 3 g total fat, 0 g trans fat, ~20 g carbohydrate, 220 mg sodium, 470 mg potassium, <1 g lactose, and 10%–30% of the recommended daily intake for vitamins and minerals. All products were provided to participants free of charge. Participants’ nutrient goals were based on BMI. Participants consumed approximately five products per day (800–820 kilocalories·day⁻¹, protein 70 g). At Week 8, participants had the option of eliminating two daily products and introducing 350 kilocalories of food from a healthy food exchange list. During this time, caloric intake reached 1300–1500 kilocalories·day⁻¹. By Week 8, participants were transitioned, at an individual pace, to all self-prepared food except for one to two products daily, if desired.

The overall goal of the weekly lifestyle classes was to assist participants in meeting the weight loss goal and increase compliance with the weight loss program (e.g., eating cues, motivation to change, and mindful eating). At each class, participants weighed-in, completed a questionnaire (regarding number of products consumed, fluid intake, any physical changes), and received a didactic topic relevant to weight loss.

Aerobic exercise training

All exercise sessions were supervised by study staff and performed on a treadmill (Precor TRM 885, Precor Inc., Wood-inville, WA). In the weight loss condition, participants exercised 2–3 days per week at an HR associated with 50%–75% VO₂ max. The HR range for each participant was determined from the baseline maximal exercise test. At the first session of each week, participants were weighed on a calibrated scale; staff reminded participants to not alter their diet or engage in exercise outside of the study and asked about any changes in medications. During Week 1 of the weight loss program, weekly exercise volume was 300 MET-min and increased 50 MET-min each week until the maximum weekly volume was 700 MET-min, which is consistent with current guidelines (2018 Physical Activity Guidelines Advisory Committee 2018). Pre-exercise and post-exercise HR and blood pressures were recorded following 5-min seated rest periods. During exercise, participants were required to remain within their target HR range for the session, which was monitored continuously (Zephyr Bioharness 3, Medtronic, Annapolis, MD) to confirm exercise intensity. Every 10 min during exercise, HR and RPE were recorded.

Weight loss maintenance program (11–28 weeks)

Participants that achieved ≥7% weight loss following the 10-week OPTIFAST^® weight loss program with supervised aerobic exercise training were enrolled to the 18-week weight loss maintenance condition of the study. An adaptive randomization was used to balance the PA-REC and WM-REC groups by baseline BMI and magnitude of weight loss.

In the weight loss maintenance condition, PA-REC exercised 2–3 sessions per week at 550 MET-min per week, while WM-REC exercised 4–5 sessions per week at 970 MET-min per week. Exercise volume for WM-REC started at 600 MET-min per week and increased 75 MET-min each week until reaching the required exercise volume of 970 MET-min per week. MET-min exercised were calculated using the standard American College of Sports Medicine walking equation (American College of Sports Medicine 2010).

Statistical analysis

Frequency-domain HRV measures were transformed into the natural logarithm (ln) due to skewness in distribution (Early et al. 2018; Tarvainen et al. 2018). All statistical analyses were performed using IBM SPSS Statistics for Macintosh, version 28.0 (IBM Corp., Armonk, NY).

For the weight loss condition, intent-to-treat (all participants regardless of adherence or ≥7% weight loss) and per-protocol (only participants with ≥75% exercise adherence and ≥7% weight loss) analyses were conducted. The primary outcome, investigating the impact of caloric restriction and aerobic exercise training on HRV markers of cardiac autonomic function, was examined using paired sample t-tests. Associations between changes in participant characteristics with changes in HRV measurements were examined using bivariate Pearson and partial correlations (controlled for change in body composition and fitness). Stepwise linear regression analysis was used to predict variation of RMSSD and HFln (dependent variables) using fasting insulin as the independent variable.

For the weight loss maintenance condition, intent-to-treat (all randomized participants in the weight loss maintenance condition regardless of adherence) and per-protocol (only participants with ≥75% exercise adherence) analyses were conducted. Paired sample t-tests were utilized to investigate the secondary outcome, investigating the impact of aerobic exercise equivalent to weight maintenance guidelines on HRV markers of cardiac autonomic function. Analysis of covariance (ANCOVA) was used to compare mean time-domain and frequency-domain differences between groups with adjustment for age, sex, change in weight, change in fitness, and post-weight loss (Week 10) HRV. Bivariate Pearson correlations were utilized to determine associations between change in clinical and biochemical characteristics with a change in HRV measurements. Data are reported as adjusted least-squares mean or change from baseline (95% CI) unless otherwise noted. Statistical significance was set as p < 0.05.

Results

The consort diagram for the PREVAIL-P study is shown in Fig. 1. Initially, 39 participants began the weight loss condition. One participant withdrew due to lack of time for participation, one participant was lost to follow-up, and one was removed due to non-compliance. Three other participants failed to reach the weight loss goal and were only included in intent-to-treat analysis. Thirty-three participants achieved CWL (10.1 ± 2.5% weight loss) following the 10-week combined intervention. During post-weight loss data collection, HRV confidence data for one participant were <50%, deemed invalid, and excluded. Therefore, 32 participants had valid HRV data and were included in the per-protocol analysis of the weight loss condition.

Fig. 1.

CONSORT diagram.

Weight loss condition

Changes of CVD risk factors in the weight loss condition, from baseline to week 10, are shown in Table 1. The final 10-week measures of weight, waist circumference, BMI, caloric intake, fat-free mass, fat mass, body fat percentage, VAT mass and volume, and blood pressures all decreased relative to baseline (p < 0.01). Fasting blood glucose, insulin, non-HDL, VLDL, LDL, and triglycerides at 10 weeks were also decreased (all p < 0.01) compared to baseline measures. Lastly, relative VO₂ max (mL·kg⁻¹·min⁻¹) at 10 weeks was increased (p < 0.01) compared to the same measure at baseline.

Table 1.

Participant characteristics across the weight loss condition.

	Intent-to-treat (n = 39)		Per-protocol (n = 32)
	Baseline	Week 10	Baseline	Week 10
Age (years)	45.7 (42.2–49.2)	—	46.9 (43.6–51.3)	—
Female % (n)	82.1 (32)	—	87.5 (28)	—
African American % (n)	33.3 (13)	—	34.4 (11)	—
Height (cm)	165.8 (163.5–168.1)	—	166.3 (163.9–169.2)	—
Weight (kg)	95.5 (91.7–99.7)	86.1 (82.2–90.0)†	94.7 (89.7–98.7)	85.7 (81.0–88.9)‡
Waist circumference (cm)	97.9 (95.3–101.7)	89.5 (87.1–92.9)†	98.0 (94.2–100.6)	89.7 (86.7–91.6)‡
BMI (kg·m−2)	34.2 (33.3–35.6)	31.0 (29.8–32.2)†	34.2 (32.7–35.1)	31.0 (29.5–31.7)‡
Fat-free mass (kg)	53.6 (50.4–57.1)	50.3 (46.8–53.8)†	53.6 (49.6–57.4)	50.1 (46.2–53.5)‡
Fat mass (kg)	39.4 (37.8–41.8)	34.0 (31.9–36.0)†	39.7 (37.1–41.4)	34.2 (31.7–35.7)‡
Body fat (%)	41.6 (40.0–43.6)	39.5 (37.4–41.6)†	41.8 (39.5–43.6)	39.7 (37.3–41.7)‡
VAT mass (g)	683.9 (625.9–757.2)	559.9 (489.3–630.5)†	667.1 (600.1–733.3)	540.7 (477.6–603.8)‡
VAT volume (cm3)	739.3 (676.6–818.6)	605.2 (528.8–681.6)†	721.1 (649.7–792.6)	584.4 (516.2–652.6)‡
RMR (kcal·day−1)	1517.0 (1457.9–1630.9)	1407.6 (1320.3–1495.0)†	1524.9 (1421.1–1616.0)	1418.7 (1306.0–1498.6)‡
Caloric intake (kcal·day−1)	2161.6 (1849.8–2473.5)	807.6 (581.0–1034.1)†	2217.5 (1840.6–2594.4)	768.7 (526.6–1010.9)‡
SBP (mmHg)	123.2 (119.1–127.6)	114.2 (109.8–118.6)†	122.4 (116.8–126.6)	113.2 (108.0–116.3)‡
DBP (mmHg)	77.9 (75.0–80.9)	72.3 (69.3–75.2)†	77.3 (73.4–80.0)	71.6 (68.0–73.8)‡
Glucose (mg·dL−1)	96.5 (92.3–98.6)	85.2 (82.8–87.6)†	95.4 (91.8–98.4)	84.8 (82.1–86.5)‡
Insulin (μIU·mL−1)	17.8 (13.9–20.8)	9.3 (2.5–16.0)†	16.9 (12.9–19.1)	6.1 (4.4–7.0)‡
Triglycerides (mg·dL−1)	107.1 (90.8–126.3)	83.4 (72.8–94.1)†	108.7 (87.4–129.0)	83.6 (71.2–94.2)‡
Non-HDL (mg·dL−1)	134.6 (124.5–144.3)	122.0 (112.9–131.1)†	135.4 (124.2–146.6)	121.1 (111.5–130.7)‡
VLDL (mg·dL−1)	21.0 (18.2–25.4)	16.5 (14.2–18.7)†	21.8 (17.6–25.9)	16.7 (14.2–18.8)‡
LDL (mg·dL−1)	113.6 (103.4–121.8)	105.5 (97.1–114.0)†	112.4 (103.4–124.0)	104.3 (95.7–113.4)‡
HDL (mg·dL−1)	52.9 (48.5–56.5)	50.3 (46.1–54.6)*	52.5 (48.6–57.4)	50.2 (46.6–54.9)
VO2 max (L·min−1)	2.04 (1.85–2.17)	2.06 (1.90–2.22)	2.05 (1.81–2.20)	2.06 (1.88–2.21)
VO2 max (mL·kg−1·min−1)	21.6 (19.8–22.6)	24.0 (22.6–25.4)†	21.8 (19.8–23.1)	24.1 (22.6–25.7)‡
Antihypertension medication % (n)	17.9 (7)	15.4 (6)	21.9 (7)	18.8 (6)
ACE inhibitor % (n)	2.6 (1)	2.6 (1)	3.1 (1)	3.1 (1)
ARB % (n)	12.8 (5)	10.2 (4)	15.6 (5)	12.5 (4)
Diuretic % (n)	7.7 (3)	5.1 (2)	9.4 (3)	6.3 (2)
Medication use (years)	4.3 (1.6–6.9)	—	4.3 (1.6–6.9)	—
Lipid-lowering medication % (n)	2.6 (1)	2.6 (1)	3.1 (1)	3.1 (1)

Note: Paired sample t-test data are shown as mean (95% confidence interval) unless otherwise stated. ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; BMI, body mass index; DBP, diastolic blood pressure; RMR, resting metabolic rate; SBP, systolic blood pressure; VAT: visceral adipose tissue; VO₂ max, maximal oxygen consumption.

^*p < 0.05 versus baseline intent-to-treat population.

^†p < 0.01 versus baseline intent-to-treat population.

^‡p < 0.01 versus baseline per-protocol population.

Across the weight loss condition, we observed an increase in HRV indices of cardiac vagal modulation, including RMSSD (37.7 ± 24.0 vs. 47.9 ± 29.1 ms, p = 0.002), HFln (5.88 ± 1.34 vs. 6.32 ± 1.28, p = 0.001), and HFnu (45.5 ± 20.3 vs. 53.5 ± 17.6, p = 0.009). We also observed an increase in SDNN (48.2 ± 19.1 vs. 55.1 ± 25.9 ms, p = 0.03) and decreases in LFnu (54.4 ± 20.3 vs. 46.4 ± 17.6, p = 0.009) and LF/HF ratio (1.78 ± 1.57 vs. 1.08 ± 0.70, p = 0.005) following weight loss (Fig. 2). Respiration rate during HRV assessment did not change from baseline to Week 10 (0.22 [0.20–0.23] vs. 0.23 [0.21–0.25] Hz, respectively) (p > 0.05). Additionally, resting HR decreased (65 ± 9 vs. 60 ± 8 bpm, p < 0.001), thus, mean RR increased (941.9 ± 125.4 vs. 1027.9 ± 114.1 ms, p < 0.001). However, ΔTPln and ΔLFln did not reach significance (both p > 0.05).

Fig. 2.

Paired sample t-tests of time- and frequency-domain markers: (A) RMSSD, (B) HFln, (C) HFnu, (D) SDNN, (E) LFnu, (F) LF/HF, (G) LFln, and (H) TPln across the weight loss condition (n = 32). HFln, log-transformed high-frequency; HFnu, high-frequency normalized units; LFln, log-transformed low-frequency; LFnu, low-frequency normalized units; RMSSD, root mean square of successive differences of intervals; SDNN, standard deviation of RR intervals; TPln, log-transformed total power.

Improvements in RMSSD (r=−0.49, p = 0.007) and HFln (r=−0.52, p = 0.004) were inversely associated to change in fasting insulin concentrations. Change in HFln was also associated with Δglucose (r=−0.41, p = 0.02). When controlling for changes in weight, fat mass, and VO₂ max (L·min⁻¹) between ΔRMSSD and Δinsulin (r=−0.48, p = 0.016), ΔHFln and Δinsulin (r=−0.47, p = 0.024), and ΔHFln and Δglucose (r=−0.41, p = 0.043), the inverse relationships remained significant. No further correlations were observed between changes in HRV markers (i.e., ΔSDNN, ΔRMSSD, ΔHFln, ΔLFln, ΔLF/HF ratio) with CVD risk factors (i.e., Δweight, Δfat mass, ΔBMI, ΔVAT mass, ΔVAT volume, Δinsulin, change in lipid profile markers, ΔVO₂ max) (all p > 0.05). Linear regression revealed that change in insulin (independent variable) predicted a significant proportion of the variance in RMSSD (r² = 0.236, p = 0.007) and HFln (r² = 0.274, p = 0.004) changes (dependent variables) (Table 2).

Table 2.

Linear regression models for changes in HRV indices of parasympathetic modulation following the weight loss condition (n = 29).

	Independent variable	β	p
ΔRMSSD
Model R2 = 0.236	ΔInsulin	−1.230	0.007*
ΔHFln
Model R2 = 0.274	ΔInsulin	−0.051	0.004*

Note: HFln, log-transformed HF power; RMSSD, root mean square of successive differences of intervals.

^*p < 0.01

Weight loss maintenance condition

Due to the novel coronavirus (COVID-19) pandemic causing early termination of the PREVAIL-P study, follow-up HRV measures for 10 participants were not assessed. Thus, 18 participants with valid HRV data were included in the subset analysis of the weight loss maintenance condition. Also due to the coronavirus pandemic, follow-up blood lab chemistries were not assessed for three participants (PA-REC: n = 2; WM-REC: n = 1), thus leaving 15 participants with valid blood glucose, insulin, and lipid profile data.

Changes in CVD risk factors across the weight loss maintenance condition are shown in Table 3. Prior to the start of the weight loss maintenance condition (Week 10), no between-group differences were observed for age, weight, waist circumference, BMI, body composition, RMR, diastolic blood pressure, glucose, insulin, triglycerides, VLDL, HDL, or VO2 max measurements (all p > 0.05). However, VAT mass and volume, systolic blood pressure, non-HDL cholesterol, and LDL cholesterol were significantly greater in WM-REC than PAREC (all p < 0.05).

Table 3.

Change in clinical and biochemical characteristics following the weight loss maintenance condition.

	PA-REC (n = 10)		WM-REC (n = 8)
	Week 10	Follow-up	Week 10	Follow-up
Age (years)	46.0 (38.3–53.7)	—	51.9 (46.5–57.2)	—
Female % (n)	80.0 (8)	—	87.5 (7)	—
African American % (n)	40.0 (4)	—	25.0 (2)	—
Height (cm)	164.9 (158.7–171.1)	—	167.2 (159.1–175.3)	—
Weight (kg)	82.8 (74.1–91.5)	84.2 (74.1–94.2)	84.8 (72.7–96.8)	82.9 (72.6–93.3)
Waist circumference (cm)	86.8 (81.8–91.8)	87.2 (81.8–92.6)	91.1 (84.7–97.6)	89.2 (83.9–94.5)
BMI (kg·m−2)	30.2 (27.5–33.1)	30.7 (27.7–33.7)	30.1 (27.8–32.5)	29.6 (27.5–31.8)
Fat-free mass (kg)	50.0 (41.0–58.9)	51.2 (41.3–61.0)	48.9 (39.6–58.1)	49.7 (41.0–58.3)
Fat mass (kg)	31.3 (26.3–36.4)	31.3 (27.0–35.7)	34.8 (31.3–38.2)	33.2 (29.3–37.1)
Body fat (%)	37.9 (32.0–43.7)	37.5 (32.5–42.4)	40.9 (37.9–43.8)	39.3 (35.6–43.0)
VAT mass (g)	473.8 (339.0–608.6)	475.8 (366.6–585.0)	627.4 (514.7–740.0)*	578.8 (475.5–682.0)
VAT volume (cm3)	511.8 (366.1–657.5)	514.5 (397.0–632.5)	678.1 (556.5–799.7)*	625.5 (513.8–737.3)
RMR (kcal·day−1)	1348.4 (1153.9–1494.8)	1551.5 (1343.6–1751.8)†	1408.1 (1153.7–1662.5)	1436.0 (1254.6–1617.4)
Caloric intake (kcal·day−1)	914.1 (298.2–1529.9)	1788.9 (1307.2–2270.6)†	590.0 (147.7–1034.1)	1082.6 (625.1–1540.1)#,§
SBP (mmHg)	106.7 (98.8–114.6)	115.6 (107.2–123.9)*	122.4 (112.4–129.1)†	128.6 (117.3–139.9)#
DBP (mmHg)	69.5 (64.1–74.9)	74.4 (69.1–79.8)*	73.0 (67.1–77.9)	78.0 (67.7–88.3)
Glucose (mg·dL−1)	82.7 (78.7–86.7)	85.0 (79.5–90.5)	82.7 (77.2–91.6)	86.1 (81.9–90.4)
Insulin (μIU·mL−1)	4.9 (2.8–7.0)	5.3 (2.3–8.3)	5.5 (2.6–8.2)	8.6 (2.5–12.7)
Triglycerides (mg·dL−1)	85.9 (52.2–119.6)	95.3 (56.8–133.9)	80.1 (69.2–103.3)	95.3 (58.7–131.9)
Non-HDL (mg·dL−1)	111.9 (92.4–131.4)	125.1 (102.8–147.4)†	136.9 (117.9–153.8)*	143.6 (104.0–183.2)
VLDL (mg·dL−1)	17.1 (10.4–23.9)	19.1 (11.4–26.8)	16.0 (13.8–20.7)	19.1 (11.7–26.4)
LDL (mg·dL−1)	94.8 (79.6–110.0)	101.2 (84.0–118.3)	120.8 (101.0–136.3)*	124.5 (90.3–158.8)
HDL (mg·dL−1)	51.6 (39.4–63.8)	64.4 (47.6–81.3)†	54.9 (44.4–61.6)	58.7 (49.3–68.2)
VO2 max (L·min−1	2.04 (1.66–2.41)	2.00 (1.59–2.40)	1.87 (1.60–2.22)	2.02 (1.60–2.46)‡
VO2 max, mL·kg−1 ·min−1	24.8 (21.3–28.3)	24.3 (21.0–27.6)	22.4 (21.4–23.6)	24.8 (22.4–26.4)‡
Antihypertensive medication % (n)	10.0 (1)	0.0 (0)	50.0 (4)	37.5 (3)
ACE inhibitor % (n)	—	—	12.5 (1)	12.5 (1)
ARB % (n)	—	—	25.0 (2)	25.0 (2)
Diuretic % (n)	10.0 (1)	0.0 (0)	12.5 (1)	0.0 (0)
Medication use (years)	5.0	—	3.0 (0.0–7.7)	—
Lipid-lowering medication % (n)	0.0 (0)	—	12.5 (1)	12.5 (1)

Note: Paired sample t-test data are shown as mean (95% confidence interval) unless otherwise stated. ACE, angiotensin converting enzyme; ARB, angiotensin II receptor blocker; BMI, body mass index; DBP, diastolic blood pressure; PA-REC, physical activity recommendations group; RMR, resting metabolic rate; SBP, systolic blood pressure; VAT, visceral adipose tissue; VO₂ max, maximal oxygen consumption; WM-REC, weight maintenance recommendations group.

^*p < 0.05 vs. PA-REC Week 10.

^†p < 0.01 vs. PA-REC Week 10.

^#p < 0.05 vs. PA-REC Follow-up.

^‡p < 0.05 vs. WM-REC Week 10.

^§p < 0.01 vs. WM-REC Week 10.

Following the weight loss maintenance condition, no within-group or between-group differences were observed in weight, fat mass, body fat percentage, BMI, waist circumference, daily caloric intake, glucose, insulin, HDL, non-HDL, VLDL, or triglycerides (all p > 0.05) (Table 3). However, PA-REC increased RMR, blood pressures, non-HDL, and HDL cholesterol (all p < 0.05). WM-REC increased daily caloric intake, systolic blood pressure, and VO₂ max (absolute and relative terms) (all p < 0.05). Two participants (PA-REC: n = 1; WM-REC: n = 1) quit taking a diuretic for antihypertension treatment on Week 11 and Week 15, respectively.

Change of time-domain and frequency-domain indices in the weight loss maintenance condition is shown in Table 4. No within-group or between-group changes in time-domain or frequency-domain HRV parameters were observed from the weight loss maintenance condition (all p > 0.05). There was no group X time interaction for time-domain or frequency-domain HRV indices (all p > 0.05). We also observed no within-group or between-group differences in respiration rate during HRV assessment (all p > 0.05). Exercise intervention data from the weight loss and weight loss maintanence conditions are shown in Table 5.

Table 4.

Change in time-domain and frequency-domain HRV parameters following the weight loss maintenance condition.

	PA-REC (n = 10)		WM-REC (n = 8)
	Week 10	Follow-up	Week 10	Follow-up
Time-domain
SDNN (ms)	51.4 (33.5–69.3)	47.9 (36.7–59.1)	40.0 (29.9–50.1)	39.3 (29.8–48.8)
RMSSD (ms)	47.2 (26.2–68.1)	36.8 (22.9–50.7)	30.9 (18.5–43.2)	32.0 (15.6–48.4)
Frequency-domain
TPln (ms2 )	7.51 (6.85–8.16)	7.53 (6.90–8.16)	7.17 (6.71–7.63)	7.13 (6.57–7.70)
LFln (ms2)	5.84 (4.97–6.71)	5.73 (4.92–6.55)	5.78 (5.13–6.42)	5.82 (5.10–6.57)
HFln (ms2)	6.19 (5.13–7.25)	5.82 (4.64–7.00)	5.56 (4.82–6.31)	5.59 (4.70–6.48)
LFnu	43.0 (29.1–57.0)	48.3 (32.8–63.7)	54.9 (43.6–66.2)	55.6 (41.0–70.2)
HFnu	56.9 (42.9–70.9)	51.7 (36.2–67.1)	45.0 (33.7–56.2)	44.3 (29.9–58.7)
LF/HF ratio	0.95 (0.49–1.41)	1.33 (0.53–2.13)	1.42 (0.76–2.09)	1.58 (0.71–2.46)
Respiration
Breathing rate, Hz	0.25 (0.21–0.29)	0.26 (0.21–0.30)	0.23 (0.17–0.29)	0.21 (0.17–0.26)

Note: Paired sample t-test data are shown as mean (95% confidence interval) unless otherwise stated. HFln, natural logarithm of HF power; HFnu, normalized units of high-frequency power; LFln, natural logarithm of low-frequency power; LFnu, normalized units of low-frequency power; PA-REC, physical activity recommendations group; RMSSD, root mean square of successive differences of intervals; SDNN, standard deviation of R-R intervals; TPln, natural logarithm of total power; WM-REC, weight maintenance recommendations group.

Table 5.

Exercise intervention metrics across the weight loss condition and weight loss maintenance condition.

	Weight loss	Weight loss maintenance
	(n = 39)	PA-REC (n = 10)	WM-REC (n = 8)
Adherence (%)	91.9 (89.5, 94.3)	86.8 (79.8, 93.8)	84.6 (75.5, 93.7)
Compliance (%)	93.9 (91.3, 96.5)	86.6 (78.2, 94.9)	84.0 (74.0, 94.0)
%VO2max	62.6 (61.0, 64.1)	61.6 (58.0, 65.1)	60.2 (54.5, 66.0)
MET-min·week−1	481.5 (466.2, 496.7)	518.3 (482.9, 553.8)	823.6 (734.4, 912.8)*
Miles·week−1	5.6 (5.4, 5.9)	5.7 (5.1, 6.4)	9.2 (8.1, 10.3)*
Min·week−1	107.9 (102.2, 113.4)	99.1 (86.4, 111.9)	162.7 (146.4, 179.0)*

Note: Paired samples t-test data are shown as mean (95% confidence interval). MET, metabolic equivalent of task; PA-REC, physical activity recommendations group; %VO₂max, exercise intensity as percentage of maximal oxygen consumption; WM-REC, weight maintenance recommendations group.

^*P < 0.001 vs. PA-REC

No significant associations were observed between changes in HRV indices with Δweight, Δfat mass, Δfat-free mass, ΔVAT mass, ΔVAT volume, ΔBMI, Δwaist circumference, Δglucose, Δinsulin, or ΔVO₂ max (all p > 0.05). When PA-REC and WM-REC blood lab chemistry data were pooled (n = 15), ΔLDL and Δnon-HDL cholesterol were observed to have significant inverse associations with ΔRMSSD and ΔHFln, as well as a significant positive relationship with ΔLF/HF ratio (Fig. 3).

Fig. 3.

Scatterplot of Pearson correlations between (A) ΔRMSSD with ΔLDL and (B) Δnon-HDL cholesterol, (C) ΔHFln with ΔLDL and (D) Δnon-HDL cholesterol, (E) ΔLF/HF ratio with ΔLDL, and (F) Δnon-HDL cholesterol across the weight loss maintenance condition (n = 15). HDL; high-density lipoprotein cholesterol; HFln, log-transformed high-frequency power; LDL, low-density lipoprotein cholesterol; RMSSD, root mean square of successive differences of intervals.

Discussion

To our knowledge, this is the first study to evaluate the impact of CWL via combined caloric restriction and aerobic exercise on HRV parameters in adults with obesity. The primary findings of this study indicate that caloric restriction combined with exercise exerts improvements in cardiac autonomic function as measured by HRV in overweight and obese adults. As a result of the 10-week weight loss intervention, we observed increased cardiac vagal modulation and overall autonomic function, both of which are associated with a lower risk of fatal and non-fatal CVD, according to previous epidemiological data (Hillebrand et al. 2013). Therefore, incorporating aerobic exercise training with a hypocaloric diet for weight loss in obesity may have significant public health implications. In the following 18-week weight loss maintenance condition, we observed no regression in HRV parameters, suggesting that the adaptations of cardiac autonomic function gained from CWL were retained with regular aerobic exercise at volumes consistent with public health guidelines. Sustaining an aerobic exercise volume that meets the minimum physical activity recommendations may preserve cardiac autonomic health benefits associated with CVD risk reduction following weight loss.

After the weight loss condition, an inverse relationship between changes in fasting insulin concentrations and markers of cardiac vagal modulation (i.e., HF power and RMSSD) was found even after adjustment for changes in weight, body composition, and fitness. The Dose-Response to Exercise in postmenopausal Women (DREW) trial observed a relationship in decreased insulin changes and improvements in RMSSD and HF power within a similar population after a comparable 6-month aerobic exercise intervention (Earnest et al. 2010). We also observed that reductions in fasting insulin concentration accounted for a significant proportion of variance in changes in RMSSD and HFln after CWL. These data suggest decreased circulating insulin levels after CWL as a potential mechanism for elevated vagal-mediated changes in obesity (Rissanen et al. 2001; Sjoberg et al. 2011). While hyperinsulinemia has been shown to stimulate SNS activation through increased norepinephrine concentrations (Rowe et al. 1981), muscle sympathetic nerve activity (Anderson et al. 1991), and decreased baroreflex sensitivity (Muscelli et al. 1998), the mechanistic action and sites by which insulin affects the ANS are currently unknown (Straznicky et al. 2009, 2015). However, an increase in central SNS activity may result directly from insulin and the vasodilatory actions of insulin eliciting baroreceptor reflex-mediated SNS activation (Straznicky et al. 2009). These findings are clinically relevant as autonomic dysfunction is associated with CVD mortality and the prevalence of insulin resistance and type 2 diabetes (Kaufman et al. 2007; Stein et al. 2008; Thayer et al. 2010; Yoo et al. 2011; Hillebrand et al. 2013; Wulsin et al. 2015; Guarino et al. 2017; Triggiani et al. 2019).

To our knowledge, we are the first to demonstrate in a randomized weight loss maintenance study that regular aerobic exercise within current physical activity guidelines results in preservation of HRV benefits from CWL. Whether participants performed aerobic exercise at volumes meeting (PAREC) or exceeding guidelines (WM-REC), HRV indices were maintained from CWL to the end of the weight loss maintenance condition despite the WM-REC group performing a significantly greater weekly volume of exercise than PA-REC. Interestingly, previous weight loss studies have reported a significant regression in SNS activity markers following weight maintenance programs not meeting physical activity guidelines (~120 min·week⁻¹, moderate intensity) (Straznicky et al. 2011; Mai et al. 2018). Evidence from the DREW Study (Earnest et al. 2008) found a dose-dependent increase in vagally mediated HRV markers across different levels of aerobic exercise training in post-menopausal women. In this 6-month trial, only the groups meeting (100%) or exceeding (150%) the physical activity guidelines improved cardiac vagal markers of HRV, whereas exercise levels below the guidelines (50%) were inadequate. Given that weight gain is indicated to increase cardiac sympathetic activation and decrease cardiac vagal modulation (Gentile et al. 2007; Adachi et al. 2011; Müller et al. 2015) and an exercise volume threshold may exist for HRV improvement (Earnest et al. 2008; Straznicky et al. 2011; Mai et al. 2018), the clinical implications for maintaining exercise levels at or above physical activity recommendations are critical for sustaining improvements in cardiac autonomic function following CWL in overweight or obesity. Future studies are needed to examine the relationship between changes in cardiac autonomic function and aerobic exercise programs following weight loss.

Following the hypocaloric diet of the weight loss condition, participants returned to consuming meals of their choice in the weight loss maintenance condition, which may reintroduce foods with cholesterol to their diet. Cholesterol is found in the plasma membrane of cardiac myocytes. In excess, membrane cholesterol content has been shown to alter functionality and structure of cardiac ion channels and pumps (e.g., Na⁺–Ca²⁺ exchanger, sarcoendoplasmic reticulum Ca²⁺ ATPase) and decrease membrane permeability for electrolytes important in normal cardiac function (Jeremy and McCarron 2000; Goonasekara et al. 2010). After the weight loss maintenance condition, changes in blood lipids (i.e., LDL, non-HDL) were observed to have an inverse relationship with markers of cardiac vagal modulation (i.e., RMSSD, HF power) and a positive relationship with autonomic balance (i.e., LF/HF ratio) in the pooled data. Such cholesterolmediated disturbances in membrane permeability and cardiac excitability increase the risk of cardiac autonomic dysfunction, endothelial dysfunction, atherosclerosis, and heart failure (Andrews et al. 1987; Jeremy and McCarron 2000; Saini et al. 2004; Goonasekara et al. 2010; Lambert et al. 2013). While there are currently no weight maintenance studies on cardiac autonomic function (measured as HRV) available for comparison of results, Kepez et al. (2015) also observed a negative correlation between RMSSD and serum LDL levels in patients without apparent CVD. Kepez and colleagues reported significantly declined RMSSD, LF power, HF power, and LF/HF ratio in the hyperlipidemia group compared to the control group. In contrast, Koskinen et al. (1995) reported no significant differences in HRV parameters (i.e., RMSSD, HF power, LF power) between a hypercholesterolemic and normocholesterolemic group. Clearly, further research is necessary to elucidate the effects of elevated atherogenic lipoproteins on cardiac vagal modulation and autonomic balance in a weight loss maintenance program. However, based on these results, increases in non-HDL (e.g., LDL) cholesterol during weight loss maintenance may be related to decrements in cardiac autonomic function and elevated risk of CVD development.

Strengths of the present study include the utilization of a validated medical weight loss program with rigorous methodology and directly supervised exercise sessions to confirm exercise-related energy expenditure. Furthermore, we collected HRV data following the methodology recommended by the Task Force for Pacing and Electrophysiology (1996). Lastly, we randomized participants into groups of different exercise volumes in the weight loss maintenance condition, which is a limitation of weight maintenance research noted in recent guidelines (Jensen et al. 2014). Limitations include that the sample size of the weight loss maintenance condition (n = 18) was less than originally designed due to the coronavirus (COVID-19) pandemic. Also, SNS activity was not collected via more rigorous methodology (i.e., microneurography of muscle sympathetic nerve activity) and breathing rates were not controlled during HRV assessment. The absence of a control group makes it difficult to conclude the changes observed following weight loss and weight loss maintenance were attributed to the interventions. Lastly, 32 females and 7 males were in the intent-to-treat analysis, thus, we were unable to assess sex differences.

Future studies are needed to compare the impact of caloric restriction versus exercise training versus a combined program for weight loss on HRV markers for determination of an optimal intervention for improving cardiac autonomic function, thus reducing CVD risk in overweight/obesity. Also, future directions should include control groups during weight loss and weight loss maintenance and assess the time-course effects of long-term weight loss maintenance with a nocontact follow-up period on HRV following weight loss.

Conclusion

CWL from a combined hypocaloric diet and aerobic exercise training intervention elicited significant improvements in resting cardiac vagal modulation in obesity. The major novel finding was that exercise training, within recommended guidelines coupled with weight loss maintenance, retained cardiac autonomic function benefits from weight loss. Our data suggest circulating insulin may influence the improvement of vagal-mediated markers of cardiac autonomic function. Weight loss from diet and exercise represents an effective, non-pharmacological intervention to delay or prevent the onset of autonomic dysfunction and reduce the risk for CVD in obesity.

Novelty.

• Caloric restriction and exercise exert significant improvements in cardiac autonomic function as measured by HRV in overweight and obesity.

• Aerobic exercise training, within recommended guidelines coupled with weight loss maintenance, retains cardiac autonomic function benefits from weight loss in previously obese individuals.

Data availability

The results of the current study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. Data generated or analyzed during this study are available from the corresponding author upon reasonable request.