Healthcare | Free Full-Text | The Healthspan Project: A Retrospective Pilot of Biomarkers and Biometric Outcomes after a 6-Month Multi-Modal Wellness Intervention
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1. Introduction
The wellness industry has surged in recent years, with the wellness economy representing 5.2% of the global impact in 2022 and market projections exceeding USD 8.7 trillion by 2027 [1]. This interest in wellness and proactive care can be explained in part by an upward shift in life expectancy [2], which has spawned a concomitant increase in the age-related disease burden, including cancer, heart disease, diabetes, and arthritis [3]. Thus, the burdens of an aging population have brought to the forefront a desire for preventive wellness interventions that reduce the strain of chronic morbidities. This concept has been more succinctly described as closing the gap between lifespan, the total years of one’s life, and healthspan, the years spent disease-free [4].
Traditional medicine has focused on ad hoc treatment as disease symptoms arise [5,6,7]. Such a disease-centric model of healthcare is undoubtedly effective in treating acute and infectious diseases. However, as chronic morbidities and aging-related conditions eclipse the prevalence of acute illness, this disease-centric model fails to address the changing landscape of medical care. Healthcare needs have shifted from acute diseases with simple, one-to-one etiologies toward chronic conditions with complex, multifaceted etiologies, a complexity that is poorly addressed by traditional medicine [5]. A new wellness-centric and proactive model of healthcare is needed to focus on comprehensive wellbeing that helps people expand their healthspans and live disease-free.
As the concept of healthy aging gains public interest, the need for scientific research is pressing. In particular, there is great interest in identifying interventions or protocols to improve proactive health and lengthen one’s healthspan [7], and health-conscious individuals are turning to a wide array of lifestyle interventions and complementary therapies [8]. Many such therapies, though perhaps replete with anecdotal evidence, lack a robust basis in the scientific literature. Indeed, interventions to treat comprehensive wellbeing and healthspan are under-researched, leaving crucial gaps in the scientific literature.
Additionally, as part of the drive to bring a wellness-centric approach to healthcare, there is great need to bring together synergistic combinations of therapies that can adequately address the multifaceted etiology of modern healthcare challenges. Given the interconnectedness of many chronic diseases, as well as the prevalence of comorbidities, the future of medicine needs to involve a multi-modal model of healthcare.
This present study aims to analyze a multi-modal pilot intervention, the Healthspan Project, and its impacts on measures of overall wellness including standard blood-based biomarkers, body composition metrics, and cardiovascular fitness. It was hypothesized that participants would improve in all three categories with a dose-dependent effect of adherence.
2. Materials and Methods
2.1. Participants
Healthy adults over the age of 18 years participated in this study. Participants completed a health history intake form to screen for any contraindications or exclusion criteria. Participants were excluded if they reported a diagnosed chronic disease or untreated high/low blood pressure. Participants were also excluded if they were pregnant or breastfeeding. After the screening, all participants completed an informed consent form and waiver. Data were examined retrospectively and determined to be exempt by the Norwich University (Northfield, VT, USA) Institutional Review Board and Research Ethics Committee (HHS IORG #0004914, IRB #00005859).
2.2. Healthspan Therapies
Participants were enrolled in a 6-month multi-modal protocol at a commercial location. The multi-modal aspect indicates that participants engaged in more than one therapy as part of the protocol, which included simultaneous weekly treatments of whole-body cryotherapy (3 min per session, Zimno Tech, Wrocław, Dolnośląskie, Poland), infrared sauna (60 min per session, Sunlighten, Overland Park, KS, USA), photobiomodulation (11 min per session, PlatinumLED, Kailua, HI, USA), compression (30–60 min per session, Hyperice, Irvine, CA, USA), mild hyperbaric oxygen therapy (60 min per session, OxyHealth, Santa Fe Springs, CA, USA), and intravenous (IV) and intramuscular (IM) micronutrient therapy (Empower Pharmacy, Houston, TX, USA); they were also asked to participate in moderate exercise. The IV micronutrients included personalized combinations of vitamin C (500 mg), B vitamins (100 mg of Thiamine HCl, 2 mg of Riboflavin, 100 mg of Niacinamide, 2 mg of Dexpanthenol, and 2 mg of Pyridoxine HCl), and glutathione (400 mg), and the IM micronutrients included vitamin D (100,000 IUs), arginine (100 mg), ornithine (50 mg), lysine (50 mg), citrulline (50 mg), and a combination of methionine, choline, and inositol (2 mL), and B12 (1000 mcg). A summary of the intervention showing the average number of therapy sessions received per participant is reported in Table 1. Note that while all participants received therapies, only 16 have enumerated records for the amount of therapy received, which is summarized in the table. Participants self-reported engaging in regular physical activity, although this activity was not tracked during the study.
2.3. Serum Biomarkers
Blood draws were conducted after an overnight fast at baseline and then incrementally at follow-up visits to track changes. The mean time between consecutive visits was 155 days (SD: 78 days), with the minimum being 17 and the maximum being 286 days. On average, there were 2.48 (SD 2.18) blood draw visits, with the total number of blood draws ranging from a minimum of 1 for the ten participants lost to follow-up to a maximum of 9 blood draws. For the 15 participants with at least one follow-up, there were on average 3.18 (SD: 2.35) total visits. The blood biomarkers that were measured consisted of metabolic analytes including fasting glucose, HbA1c, hsCRP, and serum lipids, including cholesterol (LDL and HDL) and triglycerides. All blood was drawn from the median cubital vein with a 20 gauge IV catheter needle. Drawn samples were centrifuged for 20 min at ambient temperature according to standard protocols (Drucker Diagnostics, Port Matilda, PA, USA) and sent to Quest Diagnostics (Secaucus, NJ, USA) for analysis.
2.4. Body Composition
Body composition metrics were measured using an Inbody570 machine which calculates body composition from bioelectrical impedance (InBody USA, Cerritos, CA, USA). There were three overarching categories, fat mass, lean muscle mass, and water content, with individual data points including weight, body mass index (BMI), body fat mass (BFM), percent body fat, visceral fat level, arm circumference, and BFM and BFM% for left/right arms, left/right legs, and trunk. Raw impedance values were not analyzed, and the visceral fat level was re-encoded as a numeric value.
2.5. Heart Rate Variability
Heart rate variability (HRV) data were recorded during sleep using an OURA ring (Oulu, Finland), which reports HRV using 5 min average intervals of the root mean square of successive differences (rMSSD) (ms) [9]. To calculate the rMSSD, successive differences between heart beats are squared and then averaged across regular intervals, in this case, 5 min. Then, the square root of the average squared difference is taken, yielding the final rMSSD value. Thus, the data consist of rMSSD measurements recorded every five minutes. The longest sleep segment per date was chosen to account for any naps that do not represent a true nighttime sleep segment. For participants who had HRV data formatted as arrays, the array values were unpacked into a flat dataframe for use in a downstream analysis. For participants with tabular HRV data, a sleep segment label was appended based on time gaps larger than 5 min to differentiate separate continuous sleep periods. Once the HRV data were pre-processed, they were summarized for each day of data collection by taking the overall average in the hour before waking of the longest sleep segment to follow the previous literature establishing morning as the optimal time for HRV measurement [10,11].
2.6. Statistical Analysis
Summary statistics, analyses, and statistical modeling were carried out in R v4.3.1 [12]. For data processing, the following packages were used from tidyverse v2.0.0: readr, readxl (file i/o), dplyr, hms, lubridate, tibble, stringr (wrangling), and ggplot2 (visualizations) [13].
Due to the small sample size, there was not enough power to separate individual therapy effects. Instead, overall participation in the protocol was studied by modeling time as the number of days since a participant began the program. For before and after comparisons, paired t-tests were conducted on the difference for all complete pairs of before and after data. Normality was tested using the Shapiro–Wilk test, and, where appropriate, the Wilcoxon signed-rank test was performed. Since test results, that is, the decision to accept or reject the null of no differences in means, were not different between the paired t-tests and the signed-rank tests, the t-test values were chosen.
For the subset analyses, namely body composition and HRV, linear mixed-effects models were built using the nlme package v3.1-162 [14]. Mixed (random)-effects models were chosen due to repeated measures for each participant.
For body composition metrics, each variable was first adjusted for participant-specific means and then modeled as a function of days from the start, age, sex, and height as the main fixed effects. All available metrics for fat mass, lean muscle mass, and water content, as described in Section 2.4, were modeled. In addition, biomarkers that were significantly different from start to end, namely LDL cholesterol, hsCRP, and HbA1c, were coded as binary levels (normal or elevated), with thresholds set as >100 mg/dL, >3.0 mg/L, and >5.7% for LDL, hsCRP, and HbA1c, respectively. These thresholds were chosen as they are above optimal ranges and indicate a higher health risk [15,16,17]. After inspection, HbA1c was always normal, so it was removed from consideration. The models were then fit with the main fixed effects as above, and hsCRP and LDL cholesterol levels were added as their main effects and interactions with days from the start. In all models, hsCRP and LDL were not significant after correcting for multiple comparisons, so they were removed. The final mixed-effects model for each variable consisted of days from start, age, sex, and height as fixed effects and participant as random. To correct for multiple testing, p-values were adjusted using the FDR method, and the threshold for significance was defined as an adjusted p-value (FDR)
For HRV, the data were first processed and averaged as described in the Heart Rate Variability section above. The average HRV was then modeled in a similar fashion as the body composition metrics. The response variable was the average HRV adjusted for participant-specific means, with the main effects being days from start, age, and sex. LDL cholesterol, hsCRP, and HbA1c levels were added with the same thresholds as for the body composition analysis, and HbA1c was subsequently removed as all participants had normal HbA1c values. In addition to the final overall model, one smaller model was fit to estimate the effect size for only those participants with both cardiovascular risk factors, i.e., elevated LDL cholesterol and hsCRP. The threshold for significance was set at p < 0.05.
4. Discussion
Overall, participants in the Healthspan protocol saw significant improvements in their health across blood metabolic biomarkers. In addition, the body composition subset showed a benefit for fat mass loss, while the subset for HRV showed a benefit for those with cardiovascular risk factors.
In the biomarker analysis, participants had significantly decreased LDL cholesterol and hsCRP. Only HbA1c increased with a very small effect size. Although it is impossible to determine the exact cause of this small increase, it could be due to external factors such as diet and physical activity, which were not monitored. As for the lipid and hsCRP results, these decreases are particularly meaningful given the prevalence and long progression of cardiovascular diseases through the human lifespan [19]. Decreases in LDL cholesterol can help reduce major vascular events [20], and having low hsCRP, defined as 21]. The hsCRP results are particularly notable given that the participants in the present study saw their hsCRP decrease on average from above the 2 mg/L threshold at the start of the study to below this threshold at the end of the study.
For body composition, participants saw beneficial effects, namely a loss of total body weight, total body fat, and body fat in each target area. The ability to study body composition on a granular and targeted level is crucial in assessing overall health impact. Indeed, while health risk is not necessarily correlated with overall weight, it is associated with body composition, especially visceral fat and abdominal adiposity [22,23]. Body composition analyses were complex and cost-prohibitive in the past [24,25], but the emergence of bioelectrical impedance analysis technology, such as the Inbody used in the present study, have enabled their wider-scale implementation [26]. The results from this study demonstrate the importance of body composition measurements by showing significant decreases in all metrics of body fat while showing no effect on lean muscle mass. In other words, participants lost not only weight but the specific components of weight that contribute to health risk while maintaining healthy muscle.
The HRV analysis also showed positive benefits to cardiovascular health over time for specific subsets. In particular, those with elevated LDL cholesterol, elevated hsCRP, or both saw their HRV increase on average. Generally, low HRV is associated with decreased health and increased mortality risk, while higher HRV often indicates better fitness and adaptability [27,28]. While there are several methods of measuring HRV, the metric studied here, namely rMSSD, is regarded as a reliable measure with low influence or error from respiratory effects [29]. Additionally, the study of HRV via wearables like the Oura ring is a new and emerging field of research with great potential. While the importance of HRV in monitoring health and fitness has long been established, research and applications in the field have been limited by the need to collect data via electrocardiogram [30]. The advent of wearable devices that can accurately track HRV enables the broad, public study of this important health metric [31]. The present study advances the field of HRV research by using wearables data to demonstrate a statistically significant improvement in HRV over the course of a real-world multi-modal wellness intervention. In addition to showing the statistical increase in HRV through Healthspan, it is also vital to consider the clinical relevance of changes in HRV. The smallest beneficial change in HRV as measured via rMSSD has been proposed to be +3% [32]. In this study, benefits for the three groups with cardiovascular risk factors showed increases of between 10 and 35% on average. Thus the effect of Healthspan on HRV was not only statistically significant but also evidenced a clinically meaningful change.
As people shift towards a wellness-centric model that addresses whole-body health in concert, a model of combining therapies—both in practice and research—is becoming crucial. Through the multi-modal design of the Healthspan protocol, this study sought to improve not just a single aspect of health but rather to benefit overall wellness with a multi-pronged approach which mimics real-world practice. Each therapy in Healthspan has unique yet complementary effects. Cryotherapy is known to act on inflammation and may help with glucose metabolism [33,34]. Sauna therapy continues the anti-inflammatory focus and also adds beneficial impacts for cardiovascular health and lipid metabolism [35,36,37,38]. Photobiomodulation has been proposed to beneficially impact mitochondrial energy production, and it is also used to improve cognitive function, muscle recovery, and pain relief [39,40,41,42,43]. Muscle recovery and pain relief are further addressed by compression therapy, which helps to increase blood flow and reduce lymphedema [44,45,46,47]. The final component of the Healthspan protocol was micronutrient therapy in the form of both IV and IM supplementation. Micronutrient therapy can have a variety of effects based on the specific micronutrients administered, with benefits including, though not limited to, antioxidant protection, glucose homeostasis, amino acid synthesis, reduced hypertension, and improved cognitive function [48,49,50,51,52]. These micronutrients can work both alone and in conjunction with the other therapies in the Healthspan protocol, further enhancing the wellness-centric approach to health.
Altogether, these results suggest that the multi-modal Healthspan protocol is effective in improving overall health with analyses of blood biomarkers, body composition, and HRV. It is probable that the combination of therapies had a synergistic effect resulting in greater wellness gains than any of the therapies in isolation. For example, a study by de Brito et al. found the combination of cryotherapy and antioxidant vitamin therapy showed a greater impact on exercise recovery than either cryotherapy or vitamins alone [53]. In particular, an increase in antioxidant capacity was seen only when the therapies were combined, suggesting a synergistic effect of the association. As another example of synergistic effects, cryotherapy in combination with compression was found to be superior in pain relief for those recovering from anterior cruciate ligament surgery as compared to cryotherapy alone [54]. Photobiomodulation may also benefit from synergy with other modalities, in particular micronutrient therapy. A study of participants with Hashimoto thyroiditis found that the combination of photobiomodulation and specific micronutrients resulted in better thyroid function than supplementation alone [55].
Altogether, the therapy modalities in the Healthspan protocol each show the potential for benefits in the existing literature. The current study builds upon the underlying mechanistic studies to show that these therapies have a quantifiable real-world impact. A strength of this study is the fact that it was performed in a free-living, real-world environment in which participants incorporated the Healthspan protocol into their daily lives. One benefit is the immediate applicability of the results outside of clinical practice. In particular, no rigid treatment structure is required, and Healthspan is immediately applicable for the general public.
However, such a design also has drawbacks. A distinct limitation is the lack of control over outside variables. Participants were free to go about their lives without adhering to a controlled environment, and participation in therapies was not entirely structured. Additionally, the follow-up time was not standardized. This could affect start-to-end comparisons since participants had different durations of therapy. For example, if an effect takes time to manifest, participants with a very short follow-up period might shrink the overall start-to-end difference toward zero, thus reducing the power to find true effects. In addition, although participants were instructed to perform moderate exercise, this activity was not tracked or monitored. Given the design, the specific effects of different therapies could not be separated. A related limitation is sample size, especially for the biometric data. Because of the small sample, the generalizability of results is uncertain. However, the benefit of this pilot study is in showing potential effects and providing a direction for future research. A future study could employ a larger sample size in order to focus on teasing apart therapy-specific effects. It is also possible that certain therapies are more effective in specific combinations. Further study is warranted, perhaps with a factorial design, to determine the optimal combinations and orderings of therapies.
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