Disorders of lipid metabolism are the main contributors to the risk of atherosclerotic cardiovascular (CV) diseases and are the leading cause of morbidity and mortality worldwide (Lloyd-Jones et al., 2004). Approximately, high cholesterol levels cause one-third of the burden of ischemic heart disease worldwide (Murray et al., 2003). The pathological process could begin since childhood and progress silently, initially manifesting as thickening of the intima and leading to the subsequent formation of atherosclerotic plaque through lipid accumulation in the more advanced stages, which is the pathological substrate for clinical manifestations (Falk, 2006; Guerri-Guttenberg et al., 2020).
Low-density lipoprotein (LDL) cholesterol is considered the primary treatment target for hyperlipidemia and represents the cornerstone of CV prevention (Mach et al., 2020; Stone et al., 2014; Virani et al., 2012). In addition, it is widely known that regular physical activity positively impacts lipid profile (Lippi et al., 2006; Merghani et al., 2016; Nasi et al., 2019). Therefore, athletes engaged in regular exercise programs are assumed to have a low overall CV risk because of favorable lipid profile, representing healthy lifestyle models (Isath et al., 2023).
However, these convictions have been challenged by several observational studies showing an unexpectedly high prevalence of dyslipidemia in professional athletes (based on genetic predisposition, fat-rich dietary, or type of sporting discipline practiced, i.e., lower isotonic component or sports requiring a higher body weight), reaching a prevalence ranging from 30% to 35% in both Olympic (D’Ascenzi et al., 2019; Di Gioia et al., 2023) and Paralympic athletes (Di Gioia et al., 2024).
This highlights how lipid profile abnormalities in the athletic population are underestimated (Lippi et al., 2006; Twisk et al., 2002) and, consequently, undertreated, exposing such individuals to a possible future development of atherosclerotic disease. Therefore, regardless the young age and a high level of physical fitness (Pelliccia et al., 2023), identifying dyslipidemia in athletes is crucial to implement early preventive measures through lifestyle modifications, nutraceuticals, or, in some cases, pharmacological treatment (Christou et al., 2017; Reamy & Thompson, 2004).
Moreover, competitive athletes are still underrepresented in clinical studies, and longitudinal data on the variation and evolution of blood lipids over time in athletes are currently lacking.
Hence, in the present study, we sought to evaluate how lipid profile changes over time in a large cohort of Olympic athletes of both sexes and different sporting disciplines.
Materials and Methods
In this observational retrospective study, we considered all athletes evaluated at the Institute of Sport Medicine and Science in a 10-year period (from 2012 London Summer Games to 2022 Beijing Winter Olympic Games). A total of 957 consecutive athletes were evaluated in the Olympic program. Data from at least 12 months follow-up were included in the present analysis. Athletes with follow-up shorter than 12 months were excluded.
Athletes practiced different sports disciplines, classified into four groups (D’Ascenzi et al., 2019):
- a.Power (strength disciplines): weightlifting, Greco-Roman wrestling, judo, javelin, shot-putting, bob, skeleton, snowboard, swimming (<800 m), alpine skiing, climbing, luge, and ski jumping.
- b.Skills (technical disciplines): archery, equestrian, golf, shooting, figure skating, sailing, curling, diving, surfing, and equestrian sports.
- c.Endurance (primarily dynamic components): cycling, rowing, canoeing, triathlon, long-distance running, long-distance swimming (>800 m), cross-country skiing, ice skating, pentathlon, biathlon, and Nordic combined.
- d.Mixed disciplines (alternate isometric and isotonic components): soccer, volleyball, basketball, tennis, water polo, rhythmic gymnastics, taekwondo, badminton, beach volley, softball, and fencing.
Body height and weight were obtained in each subject, and body mass index was calculated as weight (in kilograms)/height (in meters square).
Body composition and fat mass percentage were measured using Bioelectric Impedance Analysis (BIA 101 Quantum, Akern) using constant sinusoidal current at an intensity of 50 kHz and 400 μA. Bioelectric Impedance Analysis was performed between 8.30 a.m. and 10.00 a.m., at least 12 hr after the last training and before the exercise stress test scheduled in the athletes screening protocol. The same protocols, schedules, and instruments have been used since 2012.
Dyslipidemia was defined as LDL ≥115 mg/dl (Cholesterol Treatment Trialists Collaborators, 2012; Stone et al., 2014), high-density lipoprotein (HDL) <40 mg/dl for males or HDL <50 mg/dl for females (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, 2001). Hypertriglyceridemia: triglycerides (TG) values >150 mg/dl (Cholesterol Treatment Trialists Collaborators, 2012). At follow-up, we arbitrarily considered as relevant a variation of LDL of ±40 mg/dl (Andersson et al., 2024; Sabatine et al., 2018), HDL ±6 mg/dl (Kraus et al., 2002; März et al., 2017), or TG ±50 mg/dl.
A blood sample was drawn under an aseptic technique from a vein in the cubital fossa. All blood samples were collected at the same early morning hour and after at least 10 hr of fasting. Blood was drawn immediately after the athlete lay in a recumbent position and was transported immediately to an adjacent laboratory, where analysis was performed on the same day. Total cholesterol, LDL, HDL, and TG were dosed. All blood tests (from 2012 to 2022) were collected and analyzed in the same laboratory. We evaluated the quantification of serum lipids with the Roche Diagnostics Cobas 311 photometric analyzer (Roche Diagnostics).
The study design of the present investigation was evaluated and approved by the Review Board of the Institute of Sports Medicine and Science. All athletes included in this study were fully informed of the types and nature of the evaluation and signed the consent form, pursuant to Italian Law and Institute policy. All clinical data assembled from the study population are maintained in an institutional database. The work described was performed in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki).
Statistical Analysis
Categorical variables were expressed as frequencies and percentages in parenthesis and were compared using Fisher’s exact test or chi-square test, as appropriate. The normality criteria were checked for any continuous variable, which was presented as mean and SD and compared using the Student’s t test for independent data if normally distributed. A p value <.05 was considered statistically significant. Statistical analysis was performed with STATA Statistics for Windows (Special Edition, version 17).
Results
Follow-up was completed in 717 athletes (74.9%), with a mean follow-up duration of 55.6 months (10–134). The athlete’s age at study entry was 27.2 ± 4.8 years, with a slight prevalence of the male sex (392 athletes, 54.6%). Most of them were White; only 23 athletes (3.2%) were Afro-Caribbean. According to the sports category, athletes were divided as follows: 224 (31.2%) power, 97 (13.5%) skills, 196 (20.5%) endurance, and 199 (27.8%) mixed.
LDL Variation
Overall, there was no significant variation of LDL cholesterol at follow-up (t0: 97.6 ± 28.9 mg/dl, t1: 96.9 ± 27.4 mg/dl; p = .68). Specifically, of the overall cohort, 349 athletes (48.6%) had an increase of LDL at follow-up (mean + 15.1 mg/dl; from 89.1 to 104.3 mg/dl), 12 athletes (1.7%) had no changes, and 355 athletes (49.5%) had an LDL reduction (mean −16.2 g/dl; from 105.9 to 89.7 mg/dl).
Moreover, 18 athletes (2.5%) had an increase ≥40 mg/dl. Of these, none had LDL ≥115 mg/dl at t0. On the other hand, 20 athletes (2.8%) had a reduction of LDL ≥40 mg/dl. Of these, 18/20 (90%) had LDL ≥115 mg/dl at t0.
Overall, 38 athletes (5.3%) had clinically relevant LDL variation at follow-up.
HDL Variation
HDL cholesterol global variation at follow-up was not statistically significant (t0: 65.5 ± 16 mg/dl, t1: 67 ± 15.9 mg/dl; p = .07). Specifically, 390 athletes (54.4%) had an increase of HDL at follow-up (mean + 9.5 mg/dl; from 60.9 to 70.4 mg/dl), 34 athletes (5.2%) had no changes in HDL cholesterol, and 292 athletes (40.7%) had an HDL reduction (mean—8.9 mg/dl; from 71.7 to 62.7 mg/dl).
Of notice, 241 athletes (33.6%) had an increase ≥6 mg/dl. Of these, at t0, only 15/133 (11.3%) male athletes had HDL <40 mg/dl, and 8/108 (7.4%) female athletes had HDL <50 mg/dl. On the contrary, 167 athletes (23.3%) had a reduction of HDL ≥6 mg/dl. Of these, at t0, only 1/84 (1.2%) male athletes had HDL <40 mg/dl, and no female athlete had HDL <50 mg/dl.
Altogether, 408 athletes (56.8%) had clinically relevant HDL variation at follow-up.
TG Variation
No significant variation in TG was observed at follow-up (t0: 73.5 ± 35 mg/dl, t1: 73.8 ± 40 mg/dl; p = .88).
Namely, 355 athletes (49.5%) had an increase of TG at follow-up (mean + 25.5 mg/dl; from 61.3 to 86.8 mg/dl), 21 (2.9%) athletes had no changes in TG, and 340 athletes (47.4%) had a TG reduction at follow-up (mean −26 mg/dl; from 87.2 to 61.2 mg/dl).
Of the total cohort, 35 athletes (4.9%) had an increase of TG ≥50 mg/dl. None of these had TG >150 mg/dl at t0. Instead, 48 athletes (6.7%) had a reduction of TG >50 mg/dl. Of these, 14/48 (29.2%) athletes had TG >150 mg/dl at t0.
Overall, 83 athletes (11.6%) had relevant TG variation at follow-up.
Table 1 shows lipid variations according to increase or reduction, at follow-up, of fat mass percentage and weight. Loss or increase of both variables did not influence lipid profile variations. The only statistically significant reduction was observed in the TG profile (−7.2 mg/dl, −9.4%, p = .02) in athletes who lost weight at follow-up. Table 2 summarizes lipids and TG variations at follow-up according to sports category. No statistical differences were highlighted in lipid profile, TG, body weight, and fat mass percentage.
Lipid Profile at t0 and Follow-Up According to Increase or Reduction of Fat Mass or Body Weight
| n (%) | HDL t0 | HDL t1 | % | p | LDL t0 | LDL t1 | % | p | TG t0 | TG t1 | % | p | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ↑ Fat mass | 274 (38.4) | 66 ± 15.8 | 67.5 ± 15.8 | +2.2 | .29 | 100 ± 28 | 100.7 ± 28 | +0.7 | .76 | 71.7 ± 28.6 | 74.7 ± 28.6 | +4.2 | .35 |
| ↓ Fat mass | 384 (53.8) | 64.8 ± 16.3 | 66.5 ± 15.2 | +2.6 | .12 | 96.3 ± 29.7 | 94.6 ± 27.5 | −1.8 | .37 | 74.8 ± 39.8 | 73.7 ± 43.3 | −1.5 | .70 |
| ↑ Weight | 451 (63.2) | 65.3 ± 16.2 | 66.8 ± 16.1 | +2.3 | .17 | 96.9 ± 28.5 | 97 ± 27.2 | +0.1 | .93 | 71.9 ± 31.9 | 76.3 ± 45.3 | +6.1 | .09 |
| ↓ Weight | 232 (32.5) | 65.7 ± 15.9 | 67.6 ± 15.5 | +2.9 | .19 | 99.5 ± 30.4 | 96.1 ± 27.2 | −3.4 | .20 | 76.6 ± 41.8 | 69.4 ± 27.2 | −9.4 | .02 |
Note. Bold indicates data with statistical significance. HDL = high-density lipoprotein; LDL = low-density lipoprotein; TG = triglycerides.
Variations in Serum Lipid Profile and Anthropometric Parameters According to Sports Category
| Power n = 224 | p | Skills n = 97 | P | Endurance n = 196 | p | Mixed n = 199 | p | |
|---|---|---|---|---|---|---|---|---|
| HDL t0 | 62.9 ± 15.9 | .47 | 62 ± 17 | .61 | 71 ± 15.8 | .36 | 64.8 ± 14.7 | .06 |
| HDL t1 | 63.9 ± 15.7 | 60.8 ± 15.6 | 72.5 ± 15.9 | 68.1 ± 14.4 | ||||
| Δ, % | +1.6% | −1.9% | +2.1% | +5.1% | ||||
| LDL t0 | 97.2 ± 35.2 | .55 | 102.4 ± 31.4 | .37 | 92.9 ± 28.6 | .67 | 100.5 ± 29.4 | .64 |
| LDL t1 | 98.7 ± 43.4 | 98.5 ± 29.5 | 91.7 ± 26.7 | 99.2 ± 26.7 | ||||
| Δ, % | +1.5% | −3.8% | −1.3% | −1.3% | ||||
| TG t0 | 75.3 ± 27.4 | .83 | 76.3 ± 29 | .44 | 66.7 ± 27.7 | .36 | 76.7 ± 42.5 | .17 |
| TG t1 | 76.1 ± 26.9 | 81.5 ± 58.7 | 69.4 ± 30.7 | 71.6 ± 31 | ||||
| Δ, % | +1.1% | +6.8% | +4% | −6.6% | ||||
| ΔWeight, kg | +2.09 | +1.32 | +0.9 | +1.2 | ||||
| ΔFat mass, % | −0.82 | −0.19 | −0.88 | −1 |
Note. HDL = high-density lipoprotein; LDL = low-density lipoprotein; TG = triglycerides.
Dyslipidemic Athletes
Globally, 246 athletes (34.3%) were defined as dyslipidemic at t0. Specifically, 19 athletes (2.6%) had TG >150 mg/dl. Of these, only in one athlete (over 19, 5.3%) persisted TG >150 mg/dl at follow-up. At t1, 22 athletes (3.1%) had TG >150 mg/dl with a mean increase of 120 ± 116 mg/dl (21 new diagnoses).
Indeed, at t0 41 athletes (5.7%) had hypo-HDL (15/85, 17.6% females with HDL <50 mg/dl; 26/161, 16.1% males with HDL <40 mg/dl). Of these, in males, 9/26 (34.6%) had HDL <40 mg/dl also at follow-up; in females, 6/15 (40%) had HDL <50 mg/dl at follow-up. At t1, eight female athletes (1.1%) had HDL <50 mg/dl with two new female hypo-HDL diagnoses; 18 male athletes (2.5%) had HDL <40 mg/dl with nine new male diagnoses of hypo-HDL.
Moreover, 186 athletes (25.9%) had LDL ≥115 mg/dl. Of these, in 129 (69.3%) persisted an LDL ≥115 mg/dl at follow-up; at t1, 168 athletes (23.4%) had LDL ≥115 mg/dl, with 54 new diagnoses.
Table 3 and Figure 1 summarize the variations of lipid profile in dyslipidemic athletes.
Lipids, TG, Body Weight, and Fat Mass Variations at Follow-Up in Athletes With Dyslipidemia and Hypertriglyceridemia
| LDL ≥115 | p | Male HDL <40 | p | Female HDL <50 | p | TG >150 | p | |
|---|---|---|---|---|---|---|---|---|
| n (%) | 186 (25.9) | 26 (3.6) | 15 (2.1) | 19 (2.6) | ||||
| LDL t0 | 135.7 ± 18.3 | <.0001 | 100 ± 30.2 | .54 | 90.9 ± 30.4 | .88 | 101 ± 40.5 | .40 |
| LDL t1 | 124 ± 25 | 105.3 ± 32.7 | 89 ± 26.9 | 111.3 ± 33.8 | ||||
| Δ, % | −8.6% | +5.3% | −2.1% | +10.2% | ||||
| HDL t0 | 64.4 ± 17.1 | .45 | 36.1 ± 2.9 | .0001 | 43.5 ± 4.3 | .0004 | 49 ± 17.8 | .26 |
| HDL t1 | 65.8 ± 17.8 | 45.8 ± 11.4 | 52.8 ± 7.8 | 55.7 ± 18.3 | ||||
| Δ, % | +2.2% | +26.9% | +21.4% | +13.7% | ||||
| TG t0 | 84.4 ± 29.3 | .46 | 134.1 ± 90.4 | .27 | 69.1 ± 18.9 | .51 | 203.5 ± 77.8 | <.0001 |
| TG t1 | 87.1 ± 40.8 | 104.9 ± 101.3 | 64.1 ± 21.6 | 101.7 ± 28.3 | ||||
| Δ, % | +3.2% | −21.8% | −7.2% | −50% | ||||
| ΔWeight, kg | −2.3 | +2 | +0.6 | −0.2 | ||||
| ΔFat mass, % | 3.4 | −1.6 | −0.63 | −2.6 |
Note. Bold indicates data with statistical significance. HDL = high-density lipoprotein; LDL = low-density lipoprotein; TG = triglycerides.
—Variations of lipid parameters in athletes presenting altered values at t0. A significant reduction of LDL and TG was noted in those with values above the threshold at t0, while for HDL, a significant increase was observed in both male and female presenting lower values at t0. HDL = high-density lipoprotein; LDL = low-density lipoprotein; TG = triglycerides.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 34, 5; 10.1123/ijsnem.2023-0266
Globally, in athletes with LDL ≥115 mg/dl at t0, values reduced from 135.7 ± 18.3 to 124 ± 25 mg/dl, with a mean reduction of −11.7 mg/dl (−8.6%, p < .0001). Male athletes with HDL <40 mg/dl had an increase at follow-up from 36.1 ± 2.9 to 45.8 ± 11.4 mg/dl, with a mean increase of +9.6 mg/dl (+ 26.9%, p = .0001). Also, female athletes with HDL <50 mg/dl increased from 43.5 ± 4.3 to 52.8 ± 7.8 mg/dl with a mean increase of +9.3 mg/dl (+ 21.4%, p = .0004). Finally, athletes with hypertriglyceridemia decreased from 203.5 ± 77.8 to 101.7 ± 28.3 mg/dl, with a mean reduction of −101.7 mg/dl (−50%, p < .0001). There were no substantial changes in other subclasses.
Despite stable body weight, athletes who experienced an increase in HDL cholesterol and a decrease in TG also showed a reduction in fat mass percentage, with some athletes achieving up to a 2.6% decrease in fat mass percentage alongside TG reduction.
Altogether, 142 athletes (19.8%) had altered lipid profile (LDL ≥115 mg/dl or HDL <40 mg/dl in males and HDL <50 mg/dl in females or TG >150 mg/dl) at both blood tests.
They presented the following lipid disorders: 126 (88.7%) had LDL ≥115 mg/dl, eight male athletes had HDL <40 mg/dl (5.6%), five female athletes (3.5%) had HDL <50 mg/dl, one (0.7%) athlete had both LDL ≥115 m/dl and TG >150 mg/dl, one female athlete (0.7%) had both LDL ≥115 m/dl and HDL <50 mg/dl, and one male athlete (0.7%) had both LDL ≥115 m/dl and HDL <40 mg/dl.
Comparing athletes with altered lipid profile (142, 19.8%) to those with normal results at both tests (438, 61.1%), we found that dyslipidemic athletes were older, practiced more skill sports (on the contrary, endurance was less present), and a prevalence of male sex (Table 4).
Differences Between Athletes With Normal and Dyslipidemic Profiles at Both Blood Tests
| n | Dyslipidemic athlete | Normal lipid profile | p |
|---|---|---|---|
| 142 (19.8) | 438 (61.1) | ||
| Age, years | 28.9 ± 5.4 | 26.8 ± 4.3 | <.0001 |
| Power, n (%) | 48 (33.8) | 135 (30.8) | .51 |
| Skills, n (%) | 27 (19.0) | 56 (12.8) | .05 |
| Endurance, n (%) | 27 (19) | 134 (30.6) | .007 |
| Mixed, n (%) | 38 (26.8) | 113 (25.8) | .82 |
| Male, n (%) | 101 (71.1) | 228 (52.1) | .0003 |
| Black, n (%) | 3 (2.1) | 16 (3.6) | .24 |
| Follow-up, months | 53.8 ± 25.7 | 55.8 ± 27.1 | .49 |
| ΔWeight, kg | +1.69 | +1.26 | .38 |
| ΔFat mass, % | +0.05 | −1.11 | .008 |
Note. Bold indicates data with statistical significance. HDL = high-density lipoprotein; LDL = low-density lipoprotein; TG = triglycerides.
Figure 2 summarizes the number of dyslipidemic athletes at t0 and at follow-up and their lipid profile variation according to the altered parameters; in 129 (69.3%) of the athletes with elevated LDL at t0, altered values were also observed at follow-up. LDL hyperlipidemia was observed at both blood tests in 18% of whole population. Otherwise, the diagnosis was confirmed at follow-up in only a small percentage of athletes with hypo-HDL (15 athletes, nine males, six females 36.5%) or with elevated TG (one athlete, 5.3%).
—Dyslipidemic athletes at t0 and at follow-up and their lipid profile variation. HDL = high-density lipoprotein; LDL = low-density lipoprotein; TG = triglycerides.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 34, 5; 10.1123/ijsnem.2023-0266
Discussion
Dyslipidemia, which favors the atherosclerotic process, is an established and significant risk factor for CV disease (Boren et al., 2020; Ference et al., 2017). Therefore, reducing cholesterol levels is a cornerstone of CV prevention, and concurrently, the risk reduction of adverse events is related to the absolute reduction in LDL levels (Ference et al., 2012; Silverman et al., 2016).
However, scientific evidence from several cohorts of elite athletes has shown a surprisingly large prevalence of CV risk factors, of which dyslipidemia represented one of the most common (D’Ascenzi et al., 2019; Isath et al., 2023; Lippi et al., 2006; Pelliccia et al., 2017). These observations suggest that sports participation does not guarantee, per se, an optimal CV risk profile and that even young athletes are not immune from unfavorable lipid balance, suggesting the need to implement prevention strategies on a modifiable risk factor even in this selected population.
Furthermore, there is insufficient information regarding the follow-up of dyslipidemia among athletes and the longitudinal changes in blood lipid levels associated with regular training, dietary modifications, or pharmacological interventions.
Considering the overall population, our results demonstrate that there was no significant variation in lipid profile, since the LDL, HDL, and TG values were similar at t0 and follow-up. We observed a relevant variation at follow-up in only 5.3% of cases (n = 38) for LDL and 11.6% for TG (n = 83 athletes). Otherwise, half of the athletes (n = 408, 56.8%) presented significant variation of HDL at follow-up. This can be explained by the different training volumes and schedules between t0 and follow-up, since aerobic exercise training is the most effective lifestyle change capable to significantly increase HDL cholesterol (Mach et al., 2020).
Moreover, most of these variations were seen in those with altered values at t0 (Table 3, Figure 1). It is relevant to point out that in athletes found to have abnormal blood lipid values at t0, none had undergone nutraceutical or pharmacological treatment; therefore, the changes found at follow-up are likely attributable to attention by the athlete and/or the medical team to this issue, and to dietary, or lifestyle modification.
However, in LDL evaluation, despite a statistically significant reduction in LDL cholesterol at t1 (mean reduction of −11.7 mg/dl, −8.6%), most athletes with high LDL (129, 69.3%) at t0 continued to have values above the clinical threshold on the second blood sampling.
Weight and fat mass percentage modifications (increase or reduction) did not affect the lipid profile. The only statistically (but not clinically) significant reduction was observed in the TG profile (−7.2 mg/dl) in athletes who lost weight at follow-up. Furthermore, the type of sport also did not affect the changes in lipid profile observed over the follow-up period.
Almost one-fifth of athletes (142 athletes, 19.8%) showed at least one lipid alteration in both blood samples. Comparing these athletes with individuals with a consistently normal lipid profile, it appears that the dyslipidemic athletes were mainly male, older, with a slightly higher percentage of fat mass, and mainly practicing skills disciplines. In contrast, endurance sports, in line with data available in the literature, are associated to be protective versus dyslipidemia (Isath et al., 2023; Myers et al., 2002; Tsopanakis et al., 1986).
Hence, in line with established findings in the literature, our results validate that physical exercise has a favorable impact on the lipid profile, mainly by lowering TG and elevating HDL cholesterol levels, while exerting less influence on LDL cholesterol levels over time (Durstine et al., 2001; Kodama et al., 2007; Kokkinos et al., 1995; Lippi et al., 2006; Sarzynski et al., 2018; Tsopanakis et al., 1986; Wang & Xu, 2017).
While more attention has been given to the abnormally high LDL cholesterol values observed at baseline, particularly in the absence of nutraceutical and therapeutic interventions, it is notable that these values often remain elevated and persist above the normal range during follow-up in many athletes.
This evidence supports the notion that detecting LDL hypercholesterolemia in athletes should prompt the immediate initiation of pharmacological or nutraceutical treatment, especially in older male athletes and those engaged in sports characterized by low aerobic activity.
In conclusion, despite the protective effect of regular and intense exercise, even elite athletes are not immune to dyslipidemia and, in the absence of adequate interventions, remain dyslipidemic over the follow-up. Therefore, medical attention should be paid in older male athletes, especially those with LDL hypercholesterolemia, which require early and effective treatment. Specific prevention strategies include increase of isotonic exercises/training in athletes practicing skills disciplines, low-fat dietary, stricter body weight control, periodic blood test evaluation of lipid profile, and use of lowering lipid agents (nutraceuticals, etc.).
Limitations
We acknowledge that our study presents several limitations. First, the demographic characteristics of the analyzed population: small age range (515 athletes, 71.8% under 30 years old, from 16 to 47 years old), large sample size but still limited to a single center. It includes mainly Whites and athletes of exclusively Italian nationality, limiting generalizability. Then, a further limitation is the retrospective observational design of the present study with the lack of outcome data. Hence, upcoming prospective studies are required to evaluate the prognostic impact on CV health. Finally, body composition can be influenced by various factors such as intense training periods and dietary habits. Our study evaluated Olympic athletes throughout the year, including periods of peak training, championships, and postdetraining phases, such as vacations. However, in elite athletes, detraining periods are very limited (<3 weeks) and, as expected, except for HDL cholesterol levels, no significant differences were noted. After, also for the food habits, an individual daily history for each athlete was performed but no weekly diary or questionnaire was administered. Furthermore, while most athletes were re-evaluated at follow-up during the same period of the year, this was not possible for all athletes.
Conclusions
Identification of dyslipidemia in athletes is a clinical issue of recent growing interest and should not be underestimated.
Our study highlights the importance of closely monitoring athletes diagnosed with LDL hypercholesterolemia, especially among elite and Olympic athletes. Up to almost 70% of cases exhibit persistently abnormal levels over time, particularly in male athletes, those of older age, and those engaged in less aerobic disciplines. Therefore, establishing an early prevention strategy for these athletes, including nutraceutical or pharmacological treatment, should be considered.
Acknowledgments
Author Contributions: Conception and design of the study: Di Gioia, Buzzelli, Pelliccia. Acquisition of data, or analysis and interpretation of data: Zampaglione, Segreti, Fiore. Drafting the article or revising it critically for important intellectual content: Maestrini, Squeo, Lemme, Monosilio. Final approval of the version to be submitted: Di Gioia, Pelliccia. All authors have approved the final article. Funding/Grant: This research did not receive any specific grant from funding agencies in the public, commercial, or no-profit sectors.
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