Thirty volunteers were included in the investigation. At the initial evaluation, a medical history was recorded, 12/30 (40%) participants had hyperglycemia or diabetes, 19/30 (63%) had arterial hypertension, 18/30 (60%) had low HDL Cholesterol, 6/30 (20%) had high triglycerides, 24/30 (80%) had central obesity and 21/30 (70%) sedentary lifestyle. The median ± DS age was 66.63 ± 10,14 years old. The majority gender was male (22/30, 73%). The main characteristics of the population are listed in Table 1.
Table 1. Main characteristics of the study population.
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)
According to the main characteristics of the population of volunteers in this study, it was observed that they were mostly men and that in order of prevalence of the cardiovascular risk factors the most important were central obesity, arterial hypertension, low plasma HDL cholesterol levels, hyperglycemia or diabetes, high plasma triglyceride levels and sedentarism. The co-occurrence of cardiovascular risk factors is listed in Table 2.
Table 2. Co-occurrence of cardiovascular risk factors (CVF), defined as hyperglycemia or diabetes, arterial hypertension, low HDL Cholesterol, high triglycerides, central obesity and sedentarism, in each volunteer.
Cardiovascular Risk Factor
|
Volunteers
(N=30) (%)
|
1
|
5
|
17
|
2
|
8
|
27
|
3
|
13
|
43
|
4
|
3
|
10
|
5
6
|
1
21
|
3
70
|
Following the enrolment in the trial, the thirty volunteers were instructed to stop taking any dietary supplement for two weeks. After that period, blood samples were obtained, and they started drinking the wine enriched with resveratrol with the main meals (125 mL for women and 250 mL for men); the difference in the volume of the wine was determine according to the gender recommendations of daily alcohol intake [34]. The volunteers were followed throughout the intervention period. After a median of 105 ± 12.4 days of treatment, 3.5 months, the thirty participants completed the final visit where another blood samples were collected. Diet consumption, epigenetic age and body composition were evaluated, the following observations were found:
3.1. Diet control: The nutritional survey showed no significant differences during the period of the intervention. The Biozoom ® near-infrared scanner results did not reveal significant differences between the baseline and 3-month analyses (5.24 vs 5.22 reflectance units respectively). Those two results confirmed that the diet of the studied population did not vary in the consumption of fruit and vegetables [35,36].
3.2. Epigenetic age (EA): The comparison between the chronological age (CA) and EA at the beginning of the study showed a difference of 3.08 years, in favor of a lower EA. Data were analyzed with a paired t-test, chronological age median ± DS (66.63 ± 10.14 ) compared to baseline EA (63.55 ± 12.27 years) showed two-tailed p value= 0.0192 (considered very significant) with correlation coefficient (r): 0.6495. When CA 3.5 months (66.95 ± 10.30 years) was compared to post-treatment EA (60.31 ± 10.55 years), differences were two-tailed p value < 6 x 10 -10, correlation coefficient (r): 0.8079. It is very important to point out that the change between CA and EA post-treatment proved to be beneficial for EA, with a decrease highly significant two-tailed p-value and correlation coefficient. Regarding the comparison between ages at the end of the intervention, it was observed that it presented a difference of 6.64 years, also in favor of a lower EA, adding 3.56 years more, representing an additional benefit of 116%, taking into account the changes respect to chronological age before and post-treatment. We found that this reversal in epigenetic age basal vs 3.5 months was statistically significant, with p < 0.01 (Figure 1, Table 3).
Table 3. Chronological age (CA) and epigenetic age (EA) median ± standard error, initial and final analysis. Delta (Δ) represents the difference between CE and EA.
Analysis
|
Chronological Age,
(median ± SE)
|
Epigenetic Age,
(median ± SE)
|
Δ
|
p-value
|
Initial
|
66.63 ± 1,85
|
63.55 ± 2,24
|
-3,08
|
0.0192
|
Final
|
66.95 ± 1,88
|
60.31 ± 1,92
|
-6,64
|
6 x 10 -10
|
p-value
|
NS
|
< 0.01
|
|
|
Participants had all cardiovascular risk factors appropriately treated and a free diet, including regular wine consumption with meals. The latter and the lifestyle of each participant may have been part of the good result obtained in the baseline DNAm PhenoAge, in accordance with a study that demonstrated a decrease in biological age with a lifestyle and healthy diet [10]. It is necessary to distinguish between chronological age and biological age. Thus, people of different races and genders can be unified within a chronological age. However, the aging status may vary widely among individuals of similar chronological age, likely due to differences in health conditions, lifestyles and genetic determinants. The epigenetic age calculated by the DNAm PhenoAge biomarker reflects an individual's physiological and functional state [37]. Biological age can be older or younger than chronological age, reflecting aging and health status [38]. Epigenetic alterations such as histone modification, chromatin remodeling, and DNA methylation occur progressively in cells of aging individuals [39] and are associated with aging different phenotypes and the development of age-related diseases. Therefore, multiple damages to DNA or the repair system lead to an accumulation of DNA damage, resulting in epigenetic alterations that cause premature aging [40]. Since epigenetic alterations can be reset to a younger configuration, epigenetic age reversal is a potential therapeutical target to delay cell aging [41]. For each year of increase in DNAm PhenoAge, the risk of mortality from different causes increases [37]. Taking the percentages indicated in Table 4 (Column 1) as a reference, we hypothesize that the decrease in the epigenetic age after our intervention could decrease the risk of mortality for all causes and specific diseases, according to the number of years reversed between the initial and final analysis (Table 4, column 3).
Table 4. Prediction of the increased risk of mortality from all causes and specific causes related to the increase of 1 year of epigenetic age greater than the chronological age and the percentage of reduction associated with the results of EA found at the initial and final analysis in the studied population.
Mortality
|
% of increase by 1 year + EA
|
% of decrease related with initial
EA (- 3.08)
|
% of decrease related with the final EA (- 6.64)
|
All-causes
|
9
|
-27.72
|
-59.76
|
Aging-Related
|
9
|
-27.72
|
-59.76
|
Cardiovascular diseases
|
10
|
-30.80
|
-66.40
|
Cancer
|
10
|
-30.80
|
-66.40
|
Diabetes
|
20
|
-61.60
|
-132.80
|
Chronic lower
respiratory diseases
|
9
|
-27.72
|
-59.76
|
According to these results, the regular consumption of red wine enriched with Resveratrol decreased the epigenetic age in people with cardiovascular risk factors, so we also hypothesized that it would decrease the risk of mortality from chronic diseases.
3.3. Body Composition
3.3.1. Muscle mass: the measurement of the muscle mass using bioimpedance showed that the intervention produced a median increase of 300 g (Figure 2).
Results indicate that the median baseline muscle mass (kg) ± standard error was 32.70 ± 1.16 kg and 33.0 ± 1.23 kg after treatment, considered significant with t-test for paired data (two-tailed p value= 0.0189 and r= 0.9907). According to the literature, the natural evolution of aging is towards a decline in strength (dynapenia) and muscle mass (sarcopenia) from the age of 27 years old [42]. Beyond the age of 50, the loss of muscle mass has been calculated with a rate of decline of 1-2% per year [31]. Our research showed an average increase of 1% in muscle mass, which represents a statistically significant result with a p 0.019 (Table 5).
Table 5. Results of the initial and final non-invasive bioimpedance analysis (In Body 120) of the muscle and fat mass.
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)
3.3.2. Fat Mass: the measurement of the fat mass showed a decrease of median of 1.6 kg after the intervention period (p<0.0004), highly significant, representing 5.8 % (Figure 3; Table 5).
Results in Figure 3 indicate that the median ± standard error baseline fat mass was 29.30 ± 2.01 and 27.70 ± 1.96 kg after treatment, considered extremely significant with t paired t-test (two-tailed p value= 0.0004 and r= 0.9863). Among the mechanisms investigated, resveratrol can affect the metabolism of lipids by directly inhibiting the peroxisome proliferator-activated receptor gamma (PPARγ), a nuclear receptor expressed in adipose tissue, or indirectly through Sirtuin 1 (SIRT1), leading to decreased adipogenesis and an increased in lipolysis [43]. SIRT1 is also known to repress Nuclear Factor kappa-B (NF-κB) activity and thus reduce inflammation. Resveratrol modulates inflammation by directly interacting with cyclooxygenases (COX), which catalyze the formation of prostaglandins, bioactive lipids with hormone-like effects [44].
Sarcopenia in combination with excess body fat, known as sarcopenic obesity, is increasingly recognized as a major health problem in the aging population due to its association with an increased risk of cardiometabolic abnormalities [45-48].