Doping practices in international weightlifting: analysis of sanctioned athletes/support personnel from 2008 to 2019 and retesting of samples from the 2008 and 2012 Olympic Games

Kolliari-Turner, Alexander; Oliver, Brian E.; Lima, Giscard; Mills, John P.; Wang, Guan; Pitsiladis, Yannis; Guppy, Fergus

doi:10.1186/s40798-020-00293-4

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…However, many bans within weightlifting occur retrospectively following reanalysis of samples collected during major competitions. For example, Kollari-Turner et al (13) reported that a total of 61 weightlifters were identified to have adverse analytical findings of prohibited substances during the 2008 and 2012 OG. From this sample, a total of 34 of them were medallists.…”

Section: Discussionmentioning

confidence: 99%

Predicting Weight Category–Specific Performance Zones for Olympic, World, and European Weightlifting Competitions

Chavda,

Comfort,

Lake

et al. 2023

Journal of Strength and Conditioning Research

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Chavda, S, Comfort, P, Lake, JP, Bishop, C, and Turner, AN. Predicting weight category–specific performance zones for Olympic, World, and European weightlifting competitions. J Strength Cond Res 37(10): 2038–2045, 2023—Understanding the total likely required weight category to achieve a specific rank within a specific competition can aid in the long-term and short-term preparation and tactics for performance teams. The primary objective of this investigation was to develop a set of predictive models for new weight categories across 5 performance zones for 3 major weightlifting competitions. Performance total (Ptot) data for top 15 male athletes were obtained from the International Weightlifting Federation website from 1998 to 2020 across the Olympics, and World and European Championships. A second-order polynomial regression was conducted with 95% confidence, and predictive intervals were calculated. The average of the newly contested body mass was then used as the intercept. Predictions were compared against current performances of the new weight categories up to the 2020 Olympics. Results revealed that the models for all competition types varied in their predictive ability for each performance zone, across each new weight category. On average, predicted Ptot displayed a difference from actual Ptot of 3.65 ± 2.51% (12.46 ± 9.16 kg), 0.78 ± 3.29% (2.26 ± 10.08 kg), and −1.13 ± 3.46% (−4.32 ± 11.10 kg) for the Olympics, and World and European Championships, respectively. The results suggest that the predictive models may be a good indicator of future performances; however, the models may have greater efficacy in some weight categories and performance zones than others.

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Section: Discussionmentioning

confidence: 99%

Predicting Weight Category–Specific Performance Zones for Olympic, World, and European Weightlifting Competitions

Chavda,

Comfort,

Lake

et al. 2023

Journal of Strength and Conditioning Research

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“…Among the various sporting events, weightlifting was chosen for the present study as continuous performance data, such as total weight lifted in kilograms, can be obtained. In addition, there have been many anti-doping rule violations (ADRVs), both of which allow for relatively suitable analyses [6,7]. All of the data used in the current study were obtained from the publicly available dataset provided by the IWF website, and was granted an exemption by the Institutional Review Board of Yonsei University.…”

Section: Methodsmentioning

confidence: 99%

Importance of weightlifting performance analysis in anti-doping

Ryoo

Ryu

Kim³

et al. 2022

PLoS ONE

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We examined the potential roles of the athlete’s performance passport (APP) for doping detection by analyzing the relationship between weightlifting performance and sanction status. For the present study, performance data of ‘not-sanctioned’ (26740 datasets) and ‘sanctioned’ (289 datasets) male athletes were acquired from the website of the International Weightlifting Federation (www.iwf.net). One-way ANOVA, correlation analysis, and t-tests were used to analyze the relationship between athletes’ use of doping and their performances across age and body weight. Athletic performance was significantly greater for athletes in the sanctioned group than those of the same age group who were not sanctioned, and this performance difference between the two groups was the greatest in their late thirties at 20.6% (not-sanctioned 292.0kg vs. sanctioned 352.3kg) (p < 0.05). From the age group analysis, out of 289 sanctioned cases, 84 cases, which was the largest proportion, were found within the top 10–25% of their performances. When stratified by body weight, athletic performance was significantly greater for the sanctioned group than the not-sanctioned group, and this performance gap was the greatest in the bodyweight category of 96 at 18.6% (not-sanctioned 310.1kg vs. sanctioned 367.8kg) (p < 0.05). From the body weight category analysis, out of 289 sanctioned cases, 75 cases, which was the largest proportion, were found within the top 10–25% of their performances. Additionally, the mean difference in performance between not-sanctioned and sanctioned groups was the largest in the body weight category of 67kg in the ages of 15–19 at 20% (not-sanctioned 234.6kg vs. sanctioned 281.5kg). These results are interpreted to mean that in male weightlifters 1) sanctioned athletes were detected in all ranges of performances regardless of age and body weight, 2) there were even higher rates of sanctioned athletes who performed within the top 10–25% of each age group and body weight category, 3) there were significant differences in performance between not-sanctioned and sanctioned group for all body weight categories, excluding +109, in the ages of 15–19 and 20–24, 4) therefore, performance data can be effectively used to better target suspected athletes for doping testing.

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“…Due to these ergogenic effects, AAS are amongst the most widely detected doping substances in strength and power sports [ 8 , 9 ] and their detection accounts for 67% of all the medal-winning results impacted by Anti-Doping Rule Violations at the Summer Olympic Games 1968–2012 [ 10 ]. The next generation “omic” approach to detect doping is based on the premise that doping methods will cause profound and, therefore, detectable changes in the ways genes are expressed and thereby generate a unique “omic signature” of exposure to a specific doping practice [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

An observational human study investigating the effect of anabolic androgenic steroid use on the transcriptome of skeletal muscle and whole blood using RNA-Seq

et al. 2023

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Background The effects of Anabolic Androgenic Steroids (AAS) are largely illustrated through Androgen Receptor induced gene transcription, yet RNA-Seq has yet to be conducted on human whole blood and skeletal muscle. Investigating the transcriptional signature of AAS in blood may aid AAS detection and in muscle further understanding of AAS induced hypertrophy. Methods Males aged 20–42 were recruited and sampled once: sedentary controls (C), resistance trained lifters (RT) and resistance trained current AAS users (RT-AS) who ceased exposure ≤ 2 or ≥ 10 weeks prior to sampling. RT-AS were sampled twice as Returning Participants (RP) if AAS usage ceased for ≥ 18 weeks. RNA was extracted from whole blood and trapezius muscle samples. RNA libraries were sequenced twice, for validation purposes, on the DNBSEQ-G400RS with either standard or CoolMPS PE100 reagents following MGI protocols. Genes were considered differentially expressed with FDR < 0.05 and a 1.2- fold change. Results Cross-comparison of both standard reagent whole blood (N = 55: C = 7, RT = 20, RT-AS ≤ 2 = 14, RT-AS ≥ 10 = 10, RP = 4; N = 46: C = 6, RT = 17, RT-AS ≤ 2 = 12, RT-AS ≥ 10 = 8, RP = 3) sequencing datasets, showed that no genes or gene sets/pathways were differentially expressed between time points for RP or between group comparisons of RT-AS ≤ 2 vs. C, RT, or RT-AS ≥ 10. Cross-comparison of both muscle (N = 51, C = 5, RT = 17, RT-AS ≤ 2 = 15, RT-AS ≥ 10 = 11, RP = 3) sequencing (one standard & one CoolMPS reagent) datasets, showed one gene, CHRDL1, which has atrophying potential, was upregulated in RP visit two. In both muscle sequencing datasets, nine differentially expressed genes, overlapped with RT-AS ≤ 2 vs. RT and RT-AS ≤ 2 vs. C, but were not differentially expressed with RT vs. C, possibly suggesting they are from acute doping alone. No genes seemed to be differentially expressed in muscle after the long-term cessation of AAS, whereas a previous study found long term proteomic changes. Conclusion A whole blood transcriptional signature of AAS doping was not identified. However, RNA-Seq of muscle has identified numerous differentially expressed genes with known impacts on hypertrophic processes that may further our understanding on AAS induced hypertrophy. Differences in training regimens in participant groupings may have influenced results. Future studies should focus on longitudinal sampling pre, during and post-AAS exposure to better control for confounding variables.

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Doping practices in international weightlifting: analysis of sanctioned athletes/support personnel from 2008 to 2019 and retesting of samples from the 2008 and 2012 Olympic Games

Cited by 13 publications

References 13 publications

Predicting Weight Category–Specific Performance Zones for Olympic, World, and European Weightlifting Competitions

Predicting Weight Category–Specific Performance Zones for Olympic, World, and European Weightlifting Competitions

Importance of weightlifting performance analysis in anti-doping

An observational human study investigating the effect of anabolic androgenic steroid use on the transcriptome of skeletal muscle and whole blood using RNA-Seq

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