Study population
Table 1 shows a summary of the main characteristics of the studied population. Of the 72 participants, 37 (51%) were male and 35 (49%) were female. The average age was similar between men (48.7 years, range 23–79) and women (46.0 years, range 20–85). There were no significant gender differences in comorbidities such as hypertension, hypercholesterolemia, obesity, or asthma.
Regarding body composition, a significant BMI difference of 2.2 points was observed between men and women (25.7 kg/m2 vs. 23.5 kg/m2, p = 0.007). Soft lean mass and fat-free mass were also higher in men (p < 0.001), while women showed an increase in fat percentage (p < 0.001). No differences were found in body fat mass (p = 0.19), visceral fat area (p = 0.23), or waist-hip ratio (o = 0.90). Finally, muscle quality, as measured by the 50 kHz phase angle, was higher in men than in women (p < 0.001).
In terms of physical performance, women showed a faster gait speed at a fast pace (2.30 m/s) compared to men (2.12 m/s; p = 0.009). Contrarily, men exhibited significantly higher grip strength (40.1 kg) than women (23.0 kg, p < 0.001). There were no differences in the gait speed at usual pace or in the chair test.
Table 1
Main characteristics of the participants of the study.
Participants characteristics |
| Total | Male | Female | p |
N | 72 | 37 | 35 | |
Age (years) | 47.4 ± 1.68 | 48.7 ± 2.32 | 46.0 ± 2.46 | 0.422 |
Comorbidities |
Hypertension | 5 (6.94%) | 3 (8.11%) | 2 (5.71%) | 0.527 |
Hypercholesterolemia | 9 (12.0%) | 6 (16.2%) | 3 (8.57%) | 0.268 |
Obesity | 4 (5.56%) | 3 (8.11%) | 1 (2.86%) | 0.328 |
Asthma | 5 (6.94%) | 3 (8.11%) | 2 (5.71%) | 0.527 |
Body composition |
BMI (kg/m2) | 24.7 ± 0.42 | 25.7 ± 0.53 | 23.5 ± 0.61* | 0.007 |
Body Fat Mass (kg) | 16.8 ± 0.82 | 15.7 ± 1.08 | 17.9 ± 1.23 | 0.191 |
Soft Lean Mass (kg) | 51.2 ± 1.33 | 59.1 ± 1.38 | 42.6 ± 1.13 * | < 0.001 |
Fat Free Mass (kg) | 54.3 ± 1.40 | 62.6 ± 1.45 | 45.2 ± 1.20 * | < 0.001 |
Fat percentage (%) | 23.6 ± 1.00 | 19.8 ± 1.11 | 27.7 ± 1.41 * | < 0.001 |
Visceral fat area (cm2) | 77.2 ± 4.41 | 71.1 ± 5.33 | 84.0 ± 7.06 | 0.225 |
Waist Hip Ratio | 0.90 ± 0.01 | 0.90 ± 0.01 | 0.90 ± 0.01 | 0.895 |
50 kHz body phase angle | 5.73 ± 0.09 | 6.16 ± 0.10 | 5.27 ± 0.09 * | < 0.001 |
Physical performance |
4-m gait speed, usual pace (m/s) | 3.40 ± 0.06 | 3.32 ± 0.08 | 3.47 ± 0.08 | 0.196 |
4-m gait speed, fast pace (m/s) | 2.21 ± 0.03 | 2.12 ± 0.04 | 2.30 ± 0.05* | 0.009 |
Chair test (s) | 8.68 ± 0.25 | 8.30 ± 0.31 | 9.12 ± 0.38 | 0.071 |
Grip Strength (kg) | 32.1 ± 1.29 | 40.1 ± 1.09 | 23.0 ± 1.12* | < 0.001 |
GDF-15 levels |
GDF-15 (pg/mL) | 451 ± 25.1 | 464 ± 38.3 | 439 ± 32.4 | 0.648 |
Student’s t-test: * statistical difference between men and women (p-value < 0.05).
GDF-15 levels increase with age and are associated with different proxies of biological age
We first determined the levels of circulating GDF-15 in the serum samples of all participants, analyzing the differences between gender and various age groups. GDF-15 levels significantly increased with age, with individuals over 60 showing the highest levels (Fig. 1a). Particularly, the 60 and above group had greater levels of GDF-15 compared to the 20–29, 30–39, and 40–49 age groups, while no changes were found with the 50–59 age group. Additionally, there were no global differences between men and women (Fig. 1b), only in the group of individuals over 60 years men showed higher levels of GDF-15 than women (p = 0.04). A positive correlation was observed between age and GDF-15 levels (r = 0.475, p < 0.001; Fig. 1c). This correlation was stronger in men (r = 0.622, p < 0.001), while in women, it did not reach statistical significance (r = 0.319, p = 0.062).
Next, we evaluated the association of circulating GDF-15 levels with different established epigenetic clocks. As shown in Fig. 2, GDF-15 was positively associated with the biological age estimated with different methods. Specifically, the stronger correlation was seen with the DNAm PhenoAge (r = 0.569, p < 0.001; Fig. 2a). The significance was maintained in both genders, although the correlation was higher in men (r = 0.681, p < 0.001) than in women (r = 0.44, p = 0.008). We also found a strong correlation between GDF-15 levels and the aging rate (r = 0.502, p < 0.001), calculated as the ratio between the PhenoAge and the chronological age (data not shown). The epigenetic clocks defined by Horvath, Hannun, and Zhang also showed a significant overall correlation with GDF-15 (r = 0.439, r = 0.508, and r = 0.514, respectively, p < 0.001; Fig. 2b-d). However, when analyzed by gender, this association was only seen in our male cohort (r = 0.565, r = 0.660, and r = 0.670, respectively, p < 0.001). Similarly, the GrimAge and the Skin Blood Clock also showed a significant global correlation with GDF-15 levels (r = 0.502 and r = 0.522, p < 0.001, respectively). Taking gender into account, these correlations were only found in men (r = 0.631 and r = 0.658), whereas in women they were not statistically significant (r = 0.349, p = 0.068, and r = 0.339, p = 0.078, respectively). No associations were found with other proxies of biological age, such as the extrinsic epigenetic age acceleration or the age acceleration residual. These results highlight the association between GDF-15 circulating levels and different measures of biological age, especially in men.
We further explored the relationship between GDF-15 levels and several epigenetically estimated biomarkers, such as telomere length, GDF-15, FGF-21, HGF, which have been previously associated with aging (Table 2). The levels of GDF-15 measured in our cohort and the estimated levels of GDF-15 based on DNA methylation showed a significant positive correlation (r = 0.534, p < 0.001). Additionally, a negative correlation was found between the levels of circulating GDF-15 and telomere length (r = -0.476, p < 0.001). Unfortunately, we were only able to measure telomere length in half of the participants of our cohort and did not observe the same trend. Serum GDF-15 also correlated with the estimated levels of FGF21 (r = 0.456, p < 0.001) and weakly with the estimated levels of HGF (r = 0.251, p = 0.035).
Table 2
Correlation coefficients between GDF-15 levels (log-transformed) and epigenetically estimated biomarkers.
GDF-15 vs. | r | p |
Estimated GDF-15 | 0.534 | < 0.001* |
Estimated Telomere length | -0.476 | < 0.001* |
Estimated FGF21 | 0.456 | < 0.001* |
Estimated HGF | 0.251 | 0.035* |
Pearson’s correlation: * significant correlation between variables (p-value < 0.05).
Finally, we also analyzed the association of GDF-15 levels with other proxies of biological age, including self-perceived health and the frailty category (Fig. 3). Interestingly, the self-perceived health category, rated from 1 (fair) to 4 (excellent), showed a slight but significant negative correlation with the levels of GDF-15 (r = -0.265, p = 0.024). In this line, the frailty category, ranging in our cohort from 1 (fit) to 4 (living with very mild frailty), was positively correlated with circulating GDF-15 levels (r = 0.261, p = 0.027).
GDF-15 is related to pulmonary function and physical function tests
To evaluate the connection of GDF-15 with functional parameters, we tested whether its levels are associated with pulmonary and physical function tests. A significant negative correlation was observed between GDF-15 and the forced vital capacity (r = -0.346, p = 0.003, Fig. 4a) and the forced expiratory volume (r = -0.367, p = 0.002, Fig. 4b). When analyzed by gender, these associations were slightly higher in men (r = -0.534, p < 0.001; r = -0.533, p < 0.001, respectively) than in women (r = -0.456, p = 0.006; r = -0.392, p = 022, respectively). We also found an overall negative correlation with the grip strength normalized by body weight (r = -0.366, p = 0.002, Fig. 4c), that was only significant in men (r = -0.608, p < 0.001).
Additionally, we found a positive correlation between the 4-m gait speed at a fast pace with the levels of GDF-15 (r = 0.308, p = 0.009, Fig. 4d). Again, this association was only observed in men when taking gender into account (r = 0.389, p = 0.017).
GDF-15 levels are associated with body fat mass and muscle quality
Table 3 shows the correlations between the levels of GDF-15 and some measurements assessing body composition. We found a significant positive association between GDF-15 and several parameters related to the fat mass and cardiovascular risk, including the body mass index (r = 0.267, p = 0.024), the waist-hip ratio(r = 0.267, p = 0.024), the body fat mass (r = 0.349, p = 0.003), and the percentage of body fat (r = 0.302, p = 0.010), the visceral fat area (r = 0.341, p = 0.004), the fat mass index (r = 0.310, p = 0.008) and the obesity degree (r = 0.261, p = 0.028). Interestingly, these correlations were stronger in men, but were not observed in women. Other parameters of body composition, such as soft lean mass or fat free mass, showed no correlation with the levels of GDF-15. As parameters of body composition are strongly affected by gender and age, Supplemental Table 1 summarizes the lineal model adjusted for these variables. After adjusting for gender and age, GDF-15 levels do not show any significant associations with body composition, suggesting these variables are confounding factors. However, age is positively correlated with increased body fat mas and percentage, visceral fat area, and fat mass index, and negatively associated with fat free mass, soft lean mass, skeletal muscle index, and muscle quality. On the other hand, females show lower BMI, fat free mass, soft lean mass, skeletal muscle index, and muscle quality, but higher body fat percentage, visceral fat area, and fat mass index when compared to males.
Finally, we observed a negative correlation between GDF-15 and muscle quality as estimated with InBody 50 kHz, which was greater in men (r = -0.500, p = 0.001) and non-significant in women (r = -0.128, p = 0.472). However, no association was found between GDF-15 and the skeletal muscle index or creatinine levels (shown in Supplemental Table 2). Interestingly, certain amino acids that contribute to muscle function also correlated with GDF-15 levels (Supplemental Table 2), particularly branched-chain amino acids (BCAAs, r = 0.256, p = 0.032), glutamate (r = 0.331, p = 0.009), lysine (r = 0.282, p = 0.028), proline (r = 0.286, p = 0.026), and taurine (r = 0.287, p = 0.025). These associations were stronger in men, except for taurine, which showed a strong correlation with GDF-15 only in women (r = 0.458, p = 0.014). After adjusting for gender and age, BCAAs, leucine, proline, and valine were the only amino acids remained significantly correlated with GDF-15 (Supplemental Table 3).
Table 3
Correlation coefficients between GDF-15 levels (log-transformed) and parameters of body composition.
| Total (N = 72) | Males (N = 37) | Females (N = 35) |
| R | p | r | p | r | p |
BMI | 0.267 | 0.024* | 0.386 | 0.018* | 0.152 | 0.392 |
Waist-hip ratio | 0.267 | 0.024* | 0.429 | 0.008* | 0.090 | 0.614 |
Body fat mass (kg) | 0.349 | 0.003* | 0.500 | 0.002* | 0.198 | 0.262 |
Body fat (%) | 0.302 | 0.010* | 0.460 | 0.004* | 0.258 | 0.141 |
Visceral fat area (cm2) | 0.341 | 0.004* | 0.512 | 0.001* | 0.203 | 0.250 |
Fat mass index | 0.310 | 0.008* | 0.472 | 0.003* | 0.205 | 0.245 |
Obesity degree | 0.261 | 0.028* | 0.362 | 0.027* | 0.161 | 0.363 |
50 kHz body phase angle | -0.256 | 0.031* | -0.500 | 0.001* | -0.128 | 0.472 |
Pearson’s correlation: * significant correlation between variables (p-value < 0.05).
GDF-15 is associated with circulating metabolic and inflammatory markers
Next, we analyzed the correlations between GDF-15 and parameters of glycemic control (Table 3). There was a significant association with the blood glucose levels (r = 0.311, p = 0.008), and when examined by gender, this correlation was only observed in men (r = 0.474, p = 0.003). Additionally, a significant correlation between glycosylated hemoglobin and GDF-15 levels was found in both genders (r = 0.536, p < 0.001 for men, and r = 0.371, p = 0.028, for women).
Lastly, as GDF-15 has been linked to systemic inflammation, we tested whether its levels are associated with circulating inflammatory markers. First, we observed a positive correlation between the levels of C-reactive protein (r = 0.243, p = 0.041) and the C-reactive protein-to-albumin ratio (r = 0.252, p = 0.034). Interestingly, these associations were not found in men, only in women (r = 0.357, p = 0.035; r = 0.356, p = 0.036, respectively). Other markers of inflammation, including D-dimer, plasminogen activity, urate, or the platelet-lymphocyte and neutrophil-lymphocyte ratios did not show any associations with serum GDF-15.
On the other hand, we calculated the kynurenine-tryptophan ratio in plasma to estimate the activity of 2,3-dioxygenase (IDO). This ratio revealed a positive correlation with the levels of GDF-15 (r = 0.235, p = 0.049), particularly in men (r = 0.385, p = 0.027). Lastly, phospholipase 2 activity in plasma, estimated by the ratio of lysophosphatidylcolines to total phosphatidylcolines, showed a significant negative correlation with GDF-15 only in men (r = -0.438, p = 0.011).
Table 4
Correlation coefficients between GDF-15 levels (log-transformed) and circulating inflammatory markers.
| Total (N = 72) | Males (N = 37) | Females (N = 35) |
| r | p | r | p | r | p |
Glucose (mg/dL) | 0.311 | 0.008* | 0.474 | 0.003* | 0.024 | 0.892 |
HbA1c (mmol/mol) | 0.462 | < 0.001* | 0.536 | < 0.001* | 0.371 | 0.028* |
CRP (mg/dL) | 0.243 | 0.041* | 0.248 | 0.145 | 0.357 | 0.035* |
CRP-albumin ratio | 0.252 | 0.034* | 0.249 | 0.143 | 0.356 | 0.036* |
D-dimer (ng/mL) | 0.147 | 0.248 | 0.226 | 0.205 | -0.115 | 0.538 |
Plasminogen activity (%) | -0.006 | 0.973 | -0.096 | 0.670 | 0.119 | 0.639 |
Urate (mg/dL) | 0.095 | 0.429 | 0.083 | 0.627 | 0.117 | 0.504 |
Platelet-lymphocyte ratio | -0.121 | 0.313 | -0.074 | 0.664 | -0.157 | 0.369 |
Neutrophil-lymphocyte ratio | 0.039 | 0.744 | 0.076 | 0.657 | 0.025 | 0.888 |
IDO activity (Kyn/Trp) | 0.253 | 0.049* | 0.385 | 0.027* | 0.096 | 0.627 |
Phospholipase 2 activity | -0.179 | 0.167 | -0.438 | 0.011* | -0.278 | 0.153 |
Pearson’s correlation: * significant correlation between variables (p-value < 0.05). CRP: C-reactive protein.
Finally, we also analyzed the correlation of GDF-15 levels with some inflammation-related markers that were predicted through DNA methylation patterns. Table 5 summarizes the main significant associations found in our cohort. We found an overall correlation between GDF-15 and the estimated count of naïve CD8 cells (r = -0.318, p = 0.008), the estimated levels of PAI-1 (r = 0.420, p < 0.001), TIMP-1 (r = 0.443, p < 0.001), CCL11 (r = 0.269, p = 0.034), and IL-6 (r = 0.289, p = 0.023). When taken gender into account, women only showed the correlation between GDF-15 and the estimated levels of PAI-1 (r = 0.451, p = 0.010), while in men GDF-15 levels were associated with the estimated number of naïve CD8 cells (r = -0.384, p = 0.021), the estimated levels of PAI-1 (r = 0.414, p = 0.011), and TIMP-1 (r = 0.613, p < 0.001).
Table 5
Correlation coefficients between GDF-15 levels (log-transformed) and estimated inflammatory markers.
| Total (N = 72) | Males (N = 37) | Females (N = 35) |
| r | p | r | p | r | p |
Estimated naive CD8 | -0.318 | 0.008* | -0.384 | 0.021* | -0.238 | 0.190 |
Estimated PAI-1 | 0.420 | < 0.001* | 0.416 | 0.011* | 0.451 | 0.010* |
Estimated TIMP-1 | 0.443 | < 0.001* | 0.613 | < 0.001* | 0.242 | 0.181 |
Estimated CCL11 | 0.269 | 0.034* | 0.259 | 0.139 | 0.285 | 0.142 |
Estimated IL-6 | 0.289 | 0.023* | 0.274 | 0.117 | 0.310 | 0.108 |
Pearson’s correlation: * significant correlation between variables (p-value < 0.05).