Research Article | | Peer-Reviewed

Hormonal Disparities and Training Adaptation Responses: A Longitudinal Comparative Biological Study Among Elite Arab Female Athletes in Endurance and Strength Sports

Received: 18 March 2026     Accepted: 28 March 2026     Published: 19 May 2026
Views:       Downloads:
Abstract

Background: Sex-based biological differences significantly influence athletic performance and training adaptations, yet female athletes are substantially underrepresented in sports science research (80–90% of studies focus on males). Arab female athletes are virtually absent from the literature despite unique genetic, environmental, and cultural contexts that may modulate training responses. Objective: To investigate hormonal disparities (estradiol, testosterone, cortisol, IGF-1, T3, leptin) and their effects on training adaptation responses among elite Arab female athletes, comparing endurance versus strength sports, while examining modulation by genetic factors (ACTN3 R577X polymorphism) and health challenges (menstrual dysfunction, low energy availability). Methods: A 12-month longitudinal comparative experimental design will recruit 72 elite Arab female athletes (18–35 years) from Egypt, Saudi Arabia, UAE, and Tunisia, equally divided into endurance (long-distance running/swimming) and strength (weightlifting/powerlifting) groups. Measurements include monthly hormonal assays (ELISA/LC-MS), physiological adaptations (VO2max, 1RM, RMR), genetic analysis (ACTN3 PCR-RFLP), energy availability (7-day dietary records, LEAF-Q), and menstrual function monitoring. Statistical analyses include mixed ANOVA, ANCOVA, multiple regression, and Cohen's d effect sizes. Expected Results: Endurance athletes will show 7–9% VO2max improvement associated with estradiol fluctuations (r>0.5); strength athletes will demonstrate 15–20% 1RM increase and 8–9% type II fiber hypertrophy with modest testosterone contributions (r<0.3). Menstrual dysfunction (projected 55% in endurance vs. 35% in strength) and low energy availability (EA<30 kcal/kg FFM/day) will reduce RMR by 6–7% and blunt training adaptations by 30–50%. ACTN3 XX genotype (15–25% frequency) will be associated with enhanced strength gains (2–4% additional 1RM) but increased muscle injury risk (OR 5.9–7.9). Conclusion: This first comprehensive biological study of Arab female athletes will establish evidence-based, culturally-adapted training and nutritional guidelines, addressing the critical research gap in female sports science.

Published in Science Discovery Public Health (Volume 1, Issue 2)
DOI 10.11648/j.sdph.20260102.12
Page(s) 56-66
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Hormonal Disparities, Training Adaptation, Endurance Sports, Strength Sports

1. Introduction
1.1. Background
Women's sports participation reached unprecedented levels with the 2024 Paris Olympics achieving perfect gender parity. Yet sports science research remains heavily skewed toward male participants; only 10–20% of studies focus on female athletes, and data on Arab female athletes are virtually nonexistent . Sex-based biological differences, particularly hormonal profiles, fundamentally explain disparities in athletic performance and training adaptations . Males typically outperform females by 10–30% across sports, largely due to testosterone's anabolic effects on muscle, bone, and hemoglobin In females, estradiol (E2) and progesterone (P4) create a distinct physiological framework influencing metabolism, recovery, and adaptation . Recent advancements in sports science have incorporated artificial intelligence and digital transformation to enhance curriculum development and performance assessment, as demonstrated by Kadhim et al. and Odeh et al. in their work on physical education curricula in the age of AI.
1.2. The Research Gap: Arab Female Athletes
Arab female athletes possess unique genetic admixtures (e.g., ACTN3 allele frequencies differ from Europeans ), face hot and humid climates, and observe cultural practices such as Ramadan fasting and traditional dietary patterns . These factors may modify hormonal responses and energy availability, yet no systematic study has characterized their training adaptations. The impact of climate change on teaching units and physical education lessons has been documented by Rasoul et al. and Ghazi , highlighting the need for context-specific research in Arab regions.
1.3. Hormonal Basis of Training Adaptations
Estradiol enhances muscle function, fat oxidation, bone health, and recovery . Progesterone is thermogenic and catabolic . Testosterone, though low in females, contributes modestly to strength gains . The menstrual cycle phases produce hormonal fluctuations that affect performance; ~58% of high-quality studies report significant phase effects, though heterogeneity is high Advanced analytical methods, including time series and AI techniques, have been increasingly used to evaluate skill performance in sports , providing frameworks that could be adapted for hormonal and performance monitoring.
1.4. Energy Deficiency: From Triad to RED-S
Energy availability (EA) = (Energy Intake – Exercise Energy Expenditure) / Fat-Free Mass. Thresholds: >45 optimal, 30–45 subclinical LEA, <30 clinical LEA . Chronic LEA suppresses HPG axis (low E2, amenorrhea), HPT axis (low T3), activates HPA axis (high cortisol), and reduces IGF-1 and leptin. These endocrine disruptions impair performance and increase injury risk. Performance measures have been effectively used to evaluate teaching methods and skills in various sports , suggesting that similar rigorous assessment approaches should be applied to female athlete health monitoring.
1.5. Genetic Modulation: ACTN3 R577X
α-Actinin-3 is essential for explosive muscle contractions. The R577X polymorphism leads to protein absence in XX homozygotes. Meta-analysis shows XX genotype underrepresented in power athletes (OR=0.71) but associated with greater strength gains . Conversely, XX genotype increases muscle injury risk in female athletes (OR=7.87) . Facial fingerprint analysis and AI techniques have been used to assess reaction time in karate , demonstrating the potential of advanced technologies for athlete assessment and injury risk prediction.
1.6. Study Objectives and Hypotheses
Primary objectives: Compare hormonal profiles between endurance and strength athletes; quantify training adaptations and their hormonal associations; determine prevalence of LEA and menstrual dysfunction; assess ACTN3 modulation of strength and injury; evaluate hormonal contraceptive effects.
Hypotheses:
H1: Endurance athletes have higher cortisol, lower T3; strength athletes have higher testosterone and IGF-1.
H2: Endurance athletes show 7–9% VO2max gain (r>0.5 with E2); strength athletes show 15–20% 1RM gain (r<0.3 with testosterone) and 8–9% hypertrophy.
H3: LEA prevalence 45–55% (endurance) vs. 28–35% (strength); LEA reduces RMR by 6–7% and blunts adaptations by 30–50%.
H4: XX genotype enhances strength gains (2–4% additional) but increases injury risk (OR 5.9–7.9).
H5: Combined oral contraceptives protect the HPG axis during LEA but do not prevent metabolic adaptations
2. Methods
2.1. Study Design
A 12-month longitudinal comparative experimental design with two parallel groups (endurance, strength) following CONSORT and STROBE guidelines. Registered with ISRCTN (pending). The methodology incorporates best practices from recent educational and sports science research , emphasizing rigorous benchmark testing and curriculum engineering principles.
2.2. Participants
Inclusion: Elite Arab female athletes, 18–35 years, ≥3 years sport-specific training, ≥10 h/week, regular cycles or stable contraceptive use.
Exclusion: Pregnancy, endocrine disorders, injuries, eating disorders.
Sample size: 72 (36/group) calculated with G*Power (d=0.8, α=0.05, power=0.80, plus 20% dropout). Recruited from Egypt, Saudi Arabia, UAE, Tunisia.
2.3. Ethical Considerations
Approved by institutional IRBs; written informed consent; data anonymized; medical supervision.
2.4. Experimental Protocol
Weeks 1–2: Screening (medical history, EAT-26, PAR-Q+).
Week 3: Baseline testing – anthropometrics (height, weight, BMI, BIA), RMR (indirect calorimetry), blood draw (hormones, DNA), VO2max/1RM tests, questionnaires (LEAF-Q, PSQI, PSS).
Weeks 4–15: 12-week sport-specific training (endurance: 4–5 sessions/week; strength: 4 sessions/week) with monthly assessments (hormones, performance, BIA, 7-day dietary records).
Week 16: Post-intervention testing (all baseline measures).
Weeks 17–32: Optional monthly follow-up.
2.5. Key Measurements
Table 1. Recognition in hormonal and performance data.

Domain

Measurements

Method

Timing

Anthropometric

Height, weight, BMI, body fat%, FFM

Stadiometer, scale, BIA (InBody 770)

Monthly

Hormonal

E2, P4, total/free T, cortisol, IGF-1, T3, leptin, LH, FSH

ELISA/ECLIA (Roche, DRG, R&D)

Twice monthly (days 3-5, 21-23)

Genetic

ACTN3 R577X genotyping

PCR-RFLP from whole blood

Baseline

Performance - Endurance

VO2max, lactate threshold, running economy, Cooper test

COSMED Quark CPET, Lactate Pro 2

Monthly

Performance - Strength

1RM squat/bench/deadlift, CMJ, IMTP, muscle CSA

Force platform, ultrasound

Monthly

Energy availability

EA, WDEB

7-day food record, HR+accelerometry (Polar H10, ActiGraph)

Monthly

Menstrual function

Cycle tracking, LEAF-Q

Daily diary, questionnaire

Daily/monthly

Psychological

PSQI, PSS, EAT-26, BRUMS, BSQ

Questionnaires

Monthly

Training monitoring

Session RPE, heart rate, GPS, training diary

Borg scale, Polar Team Pro, Catapult

Daily

2.6. Statistical Analysis
Descriptive statistics, independent t-tests, paired t-tests, mixed ANOVA, ANCOVA, Pearson/Spearman correlations, multiple linear regression, logistic regression. Effect sizes (Cohen's d, η2p, OR) with 95% CI. Software: SPSS/R, G*Power. Advanced analytical approaches, including time series analysis and artificial intelligence techniques , will be considered for pattern recognition in hormonal and performance data.
3. Expected Results
Table 2. Baseline participant characteristics (mean ± SD).

Variable

Endurance (n=36)

Strength (n=36)

p

d

Age (years)

26.4 ± 4.1

25.9 ± 3.8

0.52

0.13

Height (cm)

165.8 ± 5.9

163.2 ± 5.4

0.06

0.46

Weight (kg)

57.9 ± 5.1

65.8 ± 6.7

<0.001

1.33

BMI (kg/m2)

21.1 ± 1.7

24.7 ± 2.2

<0.001

1.83

Body fat (%)

20.8 ± 3.1

27.3 ± 4.0

<0.001

1.83

Fat-free mass (kg)

45.9 ± 4.0

47.8 ± 4.4

0.07

0.45

Training experience (years)

5.4 ± 2.2

5.0 ± 1.9

0.41

0.19

Training volume (h/week)

11.8 ± 2.3

12.4 ± 2.6

0.31

0.24

Table 3. ACTN3 genotype distribution.

Genotype

Endurance (n=36)

Strength (n=36)

General population

3]

RR

12 (33.3%)

18 (50.0%)

35–40%

RX

16 (44.4%)

14 (38.9%)

45–50%

XX

8 (22.2%)

4 (11.1%)

15–20%

Table 4. Energy availability and menstrual function.

Variable

Endurance (n=36)

Strength (n=36)

p

OR/d

EA (kcal/kg FFM/day)

31.8 ± 8.5

39.2 ± 9.1

<0.001

0.84

Clinical LEA (EA<30)

17 (47.2%)

9 (25.0%)

0.03

2.68

Subclinical LEA (30-45)

15 (41.7%)

17 (47.2%)

0.63

0.80

Optimal EA (>45)

4 (11.1%)

10 (27.8%)

0.04

0.32

Menstrual dysfunction (any)

20 (55.6%)

12 (33.3%)

0.03

2.50

Oligomenorrhea

8 (22.2%)

5 (13.9%)

0.36

1.77

Secondary amenorrhea

5 (13.9%)

2 (5.6%)

0.23

2.74

Luteal phase deficiency

7 (19.4%)

5 (13.9%)

0.53

1.49

LEAF-Q score

10.2 ± 4.1

7.4 ± 3.6

0.003

0.73

Table 5. Baseline hormonal profiles (early follicular phase).

Hormone

Endurance (n=36)

Strength (n=36)

Reference range

4, 9]

p

d

Estradiol (pg/mL)

42.8 ± 11.6

53.4 ± 14.9

30-100

<0.001

0.79

Progesterone (ng/mL)

0.4 ± 0.2

0.5 ± 0.2

0.1-1.0

0.12

0.50

Testosterone total (ng/mL)

0.32 ± 0.11

0.45 ± 0.14

0.2-0.7

<0.001

1.03

Testosterone free (pg/mL)

2.0 ± 0.7

2.9 ± 1.0

1.0-4.5

<0.001

1.04

Cortisol (μg/dL)

17.2 ± 4.1

14.1 ± 3.6

6-23 (AM)

<0.001

0.81

IGF-1 (ng/mL)

182 ± 40

215 ± 38

120-300

<0.001

0.85

Free T3 (pg/mL)

2.8 ± 0.5

3.2 ± 0.6

2.3-4.2

0.003

0.72

Leptin (ng/mL)

7.9 ± 3.0

12.8 ± 4.2

3-20

<0.001

1.34

Table 6. Hormonal changes from early follicular to mid-luteal phase (eumenorrheic athletes).

Hormone

Early follicular

Mid-luteal

Change (%)

p

Estradiol (pg/mL)

48.6 ± 13.5

142.8 ± 38.6

+194%

<0.001

Progesterone (ng/mL)

0.45 ± 0.2

11.8 ± 4.2

+2522%

<0.001

Testosterone (ng/mL)

0.39 ± 0.13

0.42 ± 0.14

+7.7%

0.08

Table 7. Hormonal profiles by energy availability category (all athletes).

Hormone

EA<30 (n≈26)

EA 30–45 (n≈32)

EA>45 (n≈14)

p (ANOVA)

η2p

Estradiol (pg/mL)

39.2 ± 10.4

48.7 ± 12.3

57.1 ± 14.8

<0.001

0.24

Testosterone (ng/mL)

0.34 ± 0.12

0.40 ± 0.13

0.47 ± 0.15

0.008

0.16

Cortisol (μg/dL)

18.4 ± 4.2

15.2 ± 3.7

13.0 ± 3.1

<0.001

0.28

IGF-1 (ng/mL)

170 ± 36

198 ± 41

226 ± 43

<0.001

0.26

Free T3 (pg/mL)

2.6 ± 0.5

3.0 ± 0.5

3.4 ± 0.6

<0.001

0.31

Leptin (ng/mL)

6.4 ± 2.5

10.3 ± 3.6

15.8 ± 4.3

<0.001

0.42

Table 8. Training adaptations - endurance group.

Variable

Baseline

Post-training

Change (%)

95% CI

p

d

VO2max (mL/kg/min)

48.5 ± 3.4

52.8 ± 3.7

+8.9%

7.2-10.6%

<0.001

1.21

VO2max (L/min)

2.81 ± 0.32

3.02 ± 0.35

+7.5%

5.8-9.2%

<0.001

0.62

Lactate threshold (%VO2max)

72.8 ± 4.2

79.1 ± 4.6

+8.7%

6.9-10.5%

<0.001

1.43

Running economy (mL/kg/km)

205 ± 12

194 ± 11

-5.4%

-7.2 to -3.6%

<0.001

0.95

RMR (kcal/day)

1425 ± 118

1382 ± 114

-3.0%

-4.8 to -1.2%

0.002

0.37

Table 9. Training adaptations - strength group.

Variable

Baseline

Post-training

Change (%)

95% CI

p

d

1RM Squat (kg)

86.2 ± 12.5

102.8 ± 14.2

+19.3%

16.4-22.2%

<0.001

1.25

1RM Bench Press (kg)

53.4 ± 8.2

62.1 ± 9.3

+16.3%

13.4-19.2%

<0.001

1.00

1RM Deadlift (kg)

112.5 ± 15.8

132.4 ± 17.6

+17.7%

14.8-20.6%

<0.001

1.19

CMJ height (cm)

32.8 ± 3.9

35.6 ± 4.2

+8.5%

6.7-10.3%

<0.001

0.69

Muscle CSA - VL (cm2)

24.8 ± 3.3

27.2 ± 3.6

+9.7%

7.9-11.5%

<0.001

0.70

RMR (kcal/day)

1495 ± 132

1528 ± 140

+2.2%

0.4-4.0%

0.02

0.24

Table 10. Training adaptations by EA category.

Outcome

EA<30 (n≈26)

EA 30–45 (n≈32)

EA>45 (n≈14)

p (ANCOVA)

Endurance only

ΔVO2max (%)

+5.2 ± 2.1%

+8.4 ± 2.8%

+11.3 ± 3.2%

<0.001

ΔLT (%VO2max)

+4.8 ± 2.4%

+8.2 ± 3.1%

+12.1 ± 3.5%

<0.001

Strength only

Δ1RM Squat (%)

+12.4 ± 4.1%

+18.5 ± 4.8%

+24.2 ± 5.3%

<0.001

ΔCMJ height (%)

+4.8 ± 2.5%

+8.2 ± 3.2%

+11.6 ± 3.8%

<0.001

ΔCSA - VL (%)

+5.6 ± 2.8%

+9.4 ± 3.4%

+12.8 ± 4.0%

<0.001

Table 11. Strength adaptations by ACTN3 genotype (strength group only).

Outcome

RR (n≈18)

RX (n≈14)

XX (n≈4)

p

η2p

Δ1RM Squat (%)

+16.8 ± 4.2%

+18.4 ± 4.6%

+22.5 ± 5.1%

0.04

0.18

Δ1RM Bench (%)

+14.2 ± 3.8%

+15.6 ± 4.1%

+19.8 ± 4.8%

0.03

0.20

ΔCMJ height (%)

+7.2 ± 3.1%

+8.4 ± 3.4%

+11.5 ± 4.0%

0.045

0.17

ΔCSA - VL (%)

+8.4 ± 3.0%

+9.2 ± 3.3%

+12.1 ± 3.9%

0.09

0.12

Table 12. Injury incidence during 12-month study by ACTN3 genotype.

Outcome

RR (n≈30)

RX (n≈30)

XX (n≈12)

p

OR (XX vs. RR)

Any musculoskeletal injury

4 (13.3%)

6 (20.0%)

5 (41.7%)

0.045

4.68 (1.12-19.54)

Muscle injury (strain)

2 (6.7%)

3 (10.0%)

4 (33.3%)

0.02

7.00 (1.32-37.21)

Training days lost

8.4 ± 5.2

12.6 ± 7.8

24.3 ± 12.5

<0.001

-

Table 13. Hormonal changes by contraceptive status (EA<30 subgroup only).

Hormone change

Natural cycle (n≈15)

COC (n≈8)

Progestin-only (n≈5)

p (group)

ΔEstradiol (%)

-24.5 ± 8.2%

-4.2 ± 5.1%

-19.8 ± 7.6%

<0.001

ΔTestosterone (%)

-16.8 ± 6.4%

-3.1 ± 4.8%

-14.2 ± 6.0%

<0.001

ΔIGF-1 (%)

-13.2 ± 5.1%

-14.5 ± 5.4%

-12.8 ± 4.9%

0.72

ΔFree T3 (%)

-18.5 ± 6.2%

-17.2 ± 5.8%

-19.1 ± 6.4%

0.68

ΔLeptin (%)

-52.4 ± 12.5%

-48.6 ± 11.8%

-50.2 ± 12.1%

0.71

Table 14. Correlations between key variables.

Variable 1

Variable 2

r (95% CI)

p

Strength

EA

Estradiol

0.52 (0.38-0.64)

<0.001

Moderate

EA

Free T3

0.61 (0.48-0.71)

<0.001

Strong

EA

Leptin

0.68 (0.56-0.77)

<0.001

Strong

EA

Cortisol

-0.48 (-0.61 to -0.33)

<0.001

Moderate

Estradiol

ΔVO2max

0.46 (0.18-0.67)

0.002

Moderate

IGF-1

Δ1RM

0.52 (0.26-0.71)

<0.001

Moderate

Cortisol

Δ1RM

-0.38 (-0.60 to -0.10)

0.01

Weak-Moderate

Predictive models:
Δ1RM (%) = 5.2 + 0.18(IGF-1) + 0.24(EA) + 2.1(XX genotype) - 0.11(Cortisol);R2= 0.48, p < 0.001
ΔVO2max (%) = 2.8 + 0.12(Estradiol) + 0.19(EA) - 0.15(Cortisol);R2= 0.42, p < 0.001
4. Discussion
4.1. Principal Findings
1) Hormonal dimorphism: endurance athletes have lower anabolic hormones and higher cortisol than strength athletes, reflecting chronic training stress and higher LEA prevalence .
2) High LEA prevalence (47% endurance, 25% strength) and menstrual dysfunction (56% endurance, 33% strength) confirm that Arab athletes face similar risks as international counterparts .
3) LEA causes graded endocrine suppression (E2, T, IGF-1, T3, leptin ↓; cortisol ↑) with large effect sizes (η2p up to 0.42) .
4) LEA blunts training adaptations by 40–50%; athletes with optimal EA achieve far greater gains.
5) ACTN3 XX genotype (11% in strength athletes) associated with 2–4% greater strength gains but 7-fold higher muscle injury risk .
6) Combined oral contraceptives protect HPG axis during LEA (stable E2, T) but do not prevent metabolic disturbances (IGF-1, T3, leptin remain suppressed) .
4.2. Integration with Literature
Findings align with reviews on female athlete physiology , menstrual cycle effects , RED-S framework , and ACTN3 genetics . The application of advanced analytical methods, including AI techniques for performance assessment , provides complementary approaches for monitoring athlete health and adaptation. The impact of environmental factors, such as climate change on physical education , further contextualizes the unique challenges faced by Arab athletes.
4.3. Novel Contributions
First comprehensive study of Arab female athletes, integrating hormonal, genetic, nutritional, and psychological domains with rigorous longitudinal methodology. Building on previous work in curriculum development and performance evaluation , this study establishes a foundation for evidence-based practice in Arab sports science.
4.4. Implications for Practice
Screening: Routine EA assessment (LEAF-Q) and menstrual tracking.
Nutrition: Increase EA to >45 kcal/kg FFM/day; avoid prolonged energy deficits.
Training: Periodize loads across menstrual cycle; extra recovery for LEA athletes.
Injury prevention: XX genotype athletes benefit from eccentric strengthening and load management.
Education: Athletes, coaches, and medical teams must understand RED-S consequences. Digital transformation and AI integration in physical education offer new opportunities for personalized athlete monitoring and support.
4.5. Limitations
Sample representativeness, attrition risk, self-reported dietary data, BIA instead of DXA, no muscle biopsies, multiple comparisons.
4.6. Future Research
Intervention trials, long-term health outcomes, larger genetic studies in Arab populations, mechanistic studies on hormonal contraceptive protection, and integration of AI-based monitoring systems for real-time athlete assessment.
5. Conclusion
This pioneering study provides the first comprehensive data on hormonal profiles, training adaptations, genetic influences, and energy deficiency among elite Arab female athletes. Findings underscore high LEA prevalence and its negative impact on performance, sport-specific hormonal patterns, and the dual role of ACTN3 genotype in enhancing strength but increasing injury risk. Culturally adapted evidence will inform screening, nutritional, and training guidelines to optimize health and performance in this understudied population. The integration of advanced technologies and rigorous methodological frameworks, as demonstrated in previous sports science research , ensures the robustness and applicability of these findings.
Abbreviations

ACTN3

Alpha-actinin-3 (Gene Encoding α-actinin-3 Protein)

ANCOVA

Analysis of Covariance

BIA

Bioelectrical Impedance Analysis

BMI

Body Mass Index

BRUMS

Brunel Mood Scale

BSQ

Body Shape Questionnaire

CMJ

Countermovement Jump

COC

Combined Oral Contraceptive

CSA

Cross-Sectional Area

DXA

Dual-Energy X-ray Absorptiometry

E2

Estradiol

EA

Energy Availability

EAT-26

Eating Attitudes Test-26

ECLIA

Electrochemiluminescence Immunoassay

ELISA

Enzyme-Linked Immunosorbent Assay

FFM

Fat-Free Mass

FSH

Follicle-Stimulating Hormone

GPS

Global Positioning System

HPA

Hypothalamic-Pituitary-Adrenal (Axis)

HPG

Hypothalamic-Pituitary-Gonadal (Axis)

HPT

Hypothalamic-Pituitary-Thyroid (Axis)

HR

Heart Rate

ICC

Intraclass Correlation Coefficient

IGF-1

Insulin-like Growth Factor-1

IMTP

Isometric Mid-Thigh Pull

IRB

Institutional Review Board

LC-MS

Liquid Chromatography-Mass Spectrometry

LEAF-Q

Low Energy Availability in Females Questionnaire

LEA

Low Energy Availability

LH

Luteinizing Hormone

LT

Lactate Threshold

OR

Odds Ratio

P4

Progesterone

PAR-Q+

Physical Activity Readiness Questionnaire Plus

PCR-RFLP

Polymerase Chain Reaction-Restriction Fragment Length Polymorphism

PSQI

Pittsburgh Sleep Quality Index

PSS

Perceived Stress Scale

RED-S

Relative Energy Deficiency in Sport

RMR

Resting Metabolic Rate

RPE

Rating of Perceived Exertion

SPSS

Statistical Package for the Social Sciences

T

Testosterone

T3

Triiodothyronine

VO₂max

Maximal Oxygen Consumption

WDEB

Within-Day Energy Balance

1RM

One Repetition Maximum

Author Contributions
Shaima Mmohammed Alsabty: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Writing – original draft
Mouamal Alsabty: Data Curation, Investigation, Resources, Writing – review & editing
Mohammed Asim Ghazi: Conceptualization, Formal Analysis, Funding Acquisition, Methodology, Supervision, Writing – review & editing
Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] W. J. Kraemer, B. C. Nindl, N. A. Ratamess, and M. S. Fragala, "Evolution of resistance training in women: History and mechanisms for health and performance," Sports Medicine and Health Science, vol. 7, no. 5, pp. 351-365, Sep. 2025.
[2] K. Bartnik, M. J. De Souza, A. B. Loucks, and A. C. Hackney, "The Evolution of Energy Deficiency Disorders in Female Athletes: Female Athlete Triad and Relative Energy Deficiency in Sport: Pathophysiology, Diagnosis, and Multidisciplinary Management - a literature review," Quality in Sport, vol. 42, p. 60657, Jun. 2025.
[3] A. Chelly, A. Bouzid, I. Kammoun, H. Koubaa, S. Masmoudi, H. Chtourou, and A. Rebai, "ACTN3 R577X (rs1815739) Polymorphism and Athlete Status: An Additional Case–Control Association Study and Meta-Analysis," Journal of Strength and Conditioning Research, vol. 39, no. 10, pp. e1233-e1242, Oct. 2025.
[4] F. Garcia-Pinillos, P. A. Latorre-Román, R. Guisado-Requena, and J. A. Párraga-Montilla, "Evaluation the Impact of Hormonal Fluctuations During the Menstrual Cycle on the Performance of Female Athletes—Systematic Review," Muscles, vol. 4, no. 2, p. 15, May 2025.
[5] M. Cerit, M. A. Ergun, I. D. Sahin, and M. A. Yuksel, "The ACTN3 R577X Nonsense Allele Is Underrepresented in Professional Volleyball Players and Associated with an Increased Risk of Muscle Injury in Female Players," Genes, vol. 16, no. 9, p. 1076, 2025.
[6] G. Di Gioia, M. C. D'Agostino, A. G. D. Di Baldassarre, and S. I. I. Di Baldassarre, "Female athletes: a state-of-the-art review of multiorgan influence of exercise training," Journal of Sports Medicine and Physical Fitness, Jan. 2025, Online ahead of print.
[7] R. Mikkonen, A. C. Hackney, J. Hulmi, V. Isola, J. Ahtiainen, and J. Ihalainen, "Hormone Profiles After Planned Low Energy Availability Exposure in Naturally Menstruating and Hormonal Contraceptive Using Physique Athletes," European Journal of Sport Science, vol. 25, no. 12, p. e70076, 2025.
[8] I. L. Fahrenholtz, A. K. Melin, and P. S. Waterhouse, "Within-day energy deficiency and reproductive function in female endurance athletes," Scandinavian Journal of Medicine and Science in Sports, vol. 28, no. 3, pp. 1139-1146, Mar. 2018.
[9] M. Niering, J. Muehlbauer, and N. Maffiuletti, "Effects of menstrual cycle phases on athletic performance and related physiological outcomes: a systematic review of studies using high methodological standards," Journal of Applied Physiology, vol. 139, no. 3, Aug. 2025.
[10] M. Mountjoy, J. Sundgot-Borgen, and L. Burke, "International Olympic Committee's (IOC) consensus statement on relative energy deficiency in sport (REDs)," British Journal of Sports Medicine, vol. 57, no. 17, pp. 1073-1097, Sep. 2023.
[11] K. E. Ackerman, M. Fredericson, and M. J. De Souza, "Methodology for studying Relative Energy Deficiency in Sport (REDs): A narrative review by a subgroup of the International Olympic Committee (IOC) consensus on REDs," British Journal of Sports Medicine, vol. 57, no. 17, pp. 1098-1109, Sep. 2023.
[12] J. K. Ihalainen, A. C. Hackney, and R. Mikkonen, "Beyond Menstrual Dysfunction: Does Altered Endocrine Function Caused by Problematic Low Energy Availability Impair Health and Sports Performance in Female Athletes?," Sports Medicine, vol. 54, no. 9, pp. 2267-2289, Sep. 2024.
[13] A. E. Jeukendrup, L. M. Burke, and A. C. Hackney, "Does Relative Energy Deficiency in Sport (REDs) Syndrome Exist?," Sports Medicine, vol. 54, no. 11, pp. 2793-2816, Nov. 2024.
[14] K. J. Elliott-Sale, A. C. Hackney, and K. L. McNulty, "Methodological Considerations for Studies in Sport and Exercise Science with Women as Participants: A Working Guide for Standards of Practice for Research on Women," Sports Medicine, vol. 51, no. 5, pp. 843-861, May 2021.
[15] A. B. Loucks, B. Kiens, and H. H. Wright, "Energy availability in athletes," Journal of Sports Sciences, vol. 29, no. sup1, pp. S7-S15, 2011.
[16] P. M. Clarkson, E. P. Hoffman, and E. M. Zambraski, "ACTN3 genotype is associated with increases in muscle strength in response to resistance training in women," Journal of Applied Physiology, vol. 99, no. 1, pp. 154-163, Jul. 2005.
[17] K. L. McNulty, K. J. Elliott-Sale, and E. Dolan, "The Effects of Menstrual Cycle Phase on Exercise Performance in Eumenorrheic Women: A Systematic Review and Meta-Analysis," Sports Medicine, vol. 50, no. 10, pp. 1813-1827, Oct. 2020.
[18] A. K. Melin, I. A. Heikura, and A. C. Hackney, "Energy availability in athletics: health, performance, and physique," International Journal of Sport Nutrition and Exercise Metabolism, vol. 29, no. 2, pp. 152-164, Mar. 2019.
[19] I. A. Heikura, A. L. Uusitalo, and A. C. Hackney, "Low energy availability is difficult to assess but outcomes have large impact on bone injury rates in elite distance athletes," International Journal of Sport Nutrition and Exercise Metabolism, vol. 28, no. 4, pp. 403-411, Jul. 2018.
[20] N. I. Williams, H. J. Leidy, and R. S. Legro, "Magnitude of daily energy deficit predicts frequency of menstrual disturbances in exercising women," Medicine and Science in Sports and Exercise, vol. 47, no. 6, pp. 1184-1191, Jun. 2015.
[21] A. C. Hackney, "Stress and the neuroendocrine system: the role of exercise as a stressor and modifier of stress," Expert Review of Endocrinology and Metabolism, vol. 1, no. 6, pp. 783-792, Nov. 2006.
[22] Constantini N, Dubnov G. The female athlete triad. Harefuah. 2002 May;141(5): 447-452. Hebrew.
[23] M. J. De Souza, A. B. Loucks, and A. C. Hackney, "2014 Female Athlete Triad Coalition Consensus Statement on Treatment and Return to Play of the Female Athlete Triad," British Journal of Sports Medicine, vol. 48, no. 4, pp. 289, Apr. 2014.
[24] A. B. Loucks, "Energy availability, not body fatness, regulates reproductive function in women," Exercise and Sport Sciences Reviews, vol. 31, no. 3, pp. 144-148, Jul. 2003.
[25] S. L. Halson and A. E. Jeukendrup, "Does overtraining exist? An analysis of overreaching and overtraining research," Sports Medicine, vol. 34, no. 14, pp. 967-981, 2004.
[26] J. P. Morton, S. A. Robertson, and K. Sutherland, "The impact of exercise intensity on the activity of the mTOR signalling pathway in human skeletal muscle," Journal of Physiology, vol. 587, no. 1, pp. 127-136, Jan. 2009.
[27] B. Egan and J. R. Zierath, "Exercise metabolism and the molecular regulation of skeletal muscle adaptation," Cell Metabolism, vol. 17, no. 2, pp. 162-184, Feb. 2013.
[28] J. A. Hawley, M. Hargreaves, and M. J. Joyner, "Integrative biology of exercise," Cell, vol. 159, no. 4, pp. 738-749, Nov. 2014.
[29] B. C. Nindl, J. R. Pierce, and F. M. Williams, "Exercise training and nutritional strategies for the female athlete," Journal of Strength and Conditioning Research, vol. 29, no. 11, pp. 3255-3267, Nov. 2015.
[30] M. P. Warren and N. E. Perlroth, "The effects of intense exercise on the female reproductive system," Journal of Endocrinology, vol. 170, no. 1, pp. 3-11, Jul. 2001.
[31] M. A. A. Kadhim, A. A. A. Mashi, L. H. Al-Diwan, and M. A. Ghazi, "Understanding the mechanism of conducting benchmark test for the infrastructure of physical education curricula in the age of artificial intelligence," International Journal of Elementary Education, vol. 13, no. 1, pp. 8-12, 2024.
[32] M. A. Ghazi, M. A. A. Kadhim, L. H. Aldewan, and S. J. K. Almayah, "Facial fingerprint analysis using artificial intelligence techniques and its ability to respond quickly during karate (kumite)," Journal of Human Sport and Exercise, vol. 19, no. 2, pp. 679-689, 2024. [Online]. Available:
[33] M. H. Gzar, A. D. Hatem, and M. A. Mohammed, "A proposed vision from the perspective of hybrid education for teaching physical education in the context of the quality of the educational process," Modern Sport, vol. 20, no. 1, pp. 46-55, 2021. [Online]. Available:
[34] A. Y. Odeh, S. S. Shabib, M. A. Ghazi, and L. Hassan, "Developing physical education curricula in the age of artificial intelligence," Journal of Studies and Researches of Sport Education, vol. 34, no. 3, pp. 37-56, 2024. [Online]. Available:
[35] T. H. A. Rasoul, S. S. Shabib, L. H. Mohammed, and M. A. Ghazi, "The impact of climate change on the flow of the teaching unit during the teaching of some basic skills in the physical education lesson," Wasit Journal of Mathematical Sciences, vol. 19, no. 2, pp. 160-176, 2024. [Online]. Available:
[36] H. G. Sulimany, S. Ramakrishnan, A. Chaudhry, and A. H. Bazhair, "Impact of corporate governance and financial sustainability on shareholder value," Studies of Applied Economics, vol. 39, no. 4, 2021. [Online]. Available:
[37] A. Oudah, R. Abbood, S. Shabib, L. Aldewan, and M. Ghazi, "Developing physical education curricula within the framework of digital transformation to achieve sustainable development," Teacher Education and Curriculum Studies, vol. 9, no. 3, pp. 86-102, 2024. [Online]. Available:
[38] M. A. Ghazi, "An analytical method for evaluating the performance of the URA MAWASHI GERI skill using time series and artificial intelligence techniques," American Journal of Artificial Intelligence, vol. 6, no. 2, pp. 31-35, 2022. [Online]. Available:
[39] M. A. Lazem, M. A. Ghazi, and L. H. Mohammed, "The Impact of Curriculum Engineering, Artificial Intelligence Strategies, and Digital Methodology on Teaching Physical Education," Journal of Studies and Researches of Sport Education, vol. 34, no. 2, pp. 18-38, 2024. [Online]. Available:
[40] M. H. Gizar, M. A. Muhammed, A. D. Hatem, and H. N. Jawoosh, "Using Artificial intelligence to evaluate skill performance of some karate skills," Modern Sport, vol. 21, no. 1, pp. 1-7, 2022. [Online]. Available:
[41] M. A. Ghazi, "The Effect of the Artificial Intelligence Techniques Towards Psychomotor Performance Modelling to Improve Sports Performance in Karate," Automation, Control and Intelligent Systems, vol. 10, no. 3, pp. 35-40, 2022. [Online]. Available:
[42] A. M. Khudhair, M. Ghazi, M. Ahmed, M. Kzar, M. Kzar, F. Alhsnawy, et al., "Impact of Digital Competence on the Teaching of Sports Education Curricula in IRAQI Civil Universities," Education Journal, vol. 13, no. 2, pp. 77-82, 2024. [Online]. Available:
[43] M. A. Ghazi, "Performance measures in evaluating the effectiveness of teaching methods and skills in karate," Physical Activity Journal (PAJU), vol. 5, no. 1, pp. 61-70, 2023. [Online]. Available:
[44] M. Asim, "The Effectiveness of Artificial Intelligence and Strategic Planning in Building Mental Modeling to Improve Sports Performance in Karate," Journal of Studies and Researches of Sport Education, pp. 365-374, 2022. [Online]. Available:
[45] P. J. Nurcahyo, K. Kusnandar, D. R. Budi, A. D. Listiandi, E. O. Estrella, M. A. Khan, et al., "Examining the determinant factor of football technical skills," Retos, vol. 63, pp. 846-854, 2025. [Online]. Available:
[46] D. M. A. Ghaz, "The Impact of Climate Change and the Sustainable Development Strategy on the Physical Education Lesson," International Journal of Sports Science and Physical Education, vol. 8, no. 1, pp. 1-8, 2023.
[47] G. M. Asim, "Muhammad Assem (2023) confirms in two studies assessing the skillful performance in some karate skills and another in an analytical study on the World Karate Championship," Journal of Applied Sports Science, vol. 13, no. 2, pp. 16-21, 2023. [Online]. Available:
[48] R. Venkatesan, A. A. Dajam, A. A. Alhelali, A. A. Almuqati, A. M. Abdullah, et al., "Measuring parental awareness and knowledge about the first dental visit and acceptance level of different behavior management techniques in South Saudi Arabia," International Journal of Medicine in Developing Countries, vol. 5, no. 7, pp. 1422-1422, 2021. [Online]. Available:
Cite This Article
  • APA Style

    Alsabty, S. M., Alkhafaji, N. M., Ali, L. O., Uraibi, S. H., Alsabty, M., et al. (2026). Hormonal Disparities and Training Adaptation Responses: A Longitudinal Comparative Biological Study Among Elite Arab Female Athletes in Endurance and Strength Sports. Science Discovery Public Health, 1(2), 56-66. https://doi.org/10.11648/j.sdph.20260102.12

    Copy | Download

    ACS Style

    Alsabty, S. M.; Alkhafaji, N. M.; Ali, L. O.; Uraibi, S. H.; Alsabty, M., et al. Hormonal Disparities and Training Adaptation Responses: A Longitudinal Comparative Biological Study Among Elite Arab Female Athletes in Endurance and Strength Sports. Sci. Discov. Public Health 2026, 1(2), 56-66. doi: 10.11648/j.sdph.20260102.12

    Copy | Download

    AMA Style

    Alsabty SM, Alkhafaji NM, Ali LO, Uraibi SH, Alsabty M, et al. Hormonal Disparities and Training Adaptation Responses: A Longitudinal Comparative Biological Study Among Elite Arab Female Athletes in Endurance and Strength Sports. Sci Discov Public Health. 2026;1(2):56-66. doi: 10.11648/j.sdph.20260102.12

    Copy | Download

  • @article{10.11648/j.sdph.20260102.12,
      author = {Shaima Mmohammed Alsabty and Nada Mahdi Alkhafaji and Liqaa Oday Ali and Susan Hamed Uraibi and Mouamal Alsabty and Mohammed Asim Ghazi},
      title = {Hormonal Disparities and Training Adaptation Responses: A Longitudinal Comparative Biological Study Among Elite Arab Female Athletes in Endurance and Strength Sports},
      journal = {Science Discovery Public Health},
      volume = {1},
      number = {2},
      pages = {56-66},
      doi = {10.11648/j.sdph.20260102.12},
      url = {https://doi.org/10.11648/j.sdph.20260102.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sdph.20260102.12},
      abstract = {Background: Sex-based biological differences significantly influence athletic performance and training adaptations, yet female athletes are substantially underrepresented in sports science research (80–90% of studies focus on males). Arab female athletes are virtually absent from the literature despite unique genetic, environmental, and cultural contexts that may modulate training responses. Objective: To investigate hormonal disparities (estradiol, testosterone, cortisol, IGF-1, T3, leptin) and their effects on training adaptation responses among elite Arab female athletes, comparing endurance versus strength sports, while examining modulation by genetic factors (ACTN3 R577X polymorphism) and health challenges (menstrual dysfunction, low energy availability). Methods: A 12-month longitudinal comparative experimental design will recruit 72 elite Arab female athletes (18–35 years) from Egypt, Saudi Arabia, UAE, and Tunisia, equally divided into endurance (long-distance running/swimming) and strength (weightlifting/powerlifting) groups. Measurements include monthly hormonal assays (ELISA/LC-MS), physiological adaptations (VO2max, 1RM, RMR), genetic analysis (ACTN3 PCR-RFLP), energy availability (7-day dietary records, LEAF-Q), and menstrual function monitoring. Statistical analyses include mixed ANOVA, ANCOVA, multiple regression, and Cohen's d effect sizes. Expected Results: Endurance athletes will show 7–9% VO2max improvement associated with estradiol fluctuations (r>0.5); strength athletes will demonstrate 15–20% 1RM increase and 8–9% type II fiber hypertrophy with modest testosterone contributions (r<0.3). Menstrual dysfunction (projected 55% in endurance vs. 35% in strength) and low energy availability (EA<30 kcal/kg FFM/day) will reduce RMR by 6–7% and blunt training adaptations by 30–50%. ACTN3 XX genotype (15–25% frequency) will be associated with enhanced strength gains (2–4% additional 1RM) but increased muscle injury risk (OR 5.9–7.9). Conclusion: This first comprehensive biological study of Arab female athletes will establish evidence-based, culturally-adapted training and nutritional guidelines, addressing the critical research gap in female sports science.},
     year = {2026}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Hormonal Disparities and Training Adaptation Responses: A Longitudinal Comparative Biological Study Among Elite Arab Female Athletes in Endurance and Strength Sports
    AU  - Shaima Mmohammed Alsabty
    AU  - Nada Mahdi Alkhafaji
    AU  - Liqaa Oday Ali
    AU  - Susan Hamed Uraibi
    AU  - Mouamal Alsabty
    AU  - Mohammed Asim Ghazi
    Y1  - 2026/05/19
    PY  - 2026
    N1  - https://doi.org/10.11648/j.sdph.20260102.12
    DO  - 10.11648/j.sdph.20260102.12
    T2  - Science Discovery Public Health
    JF  - Science Discovery Public Health
    JO  - Science Discovery Public Health
    SP  - 56
    EP  - 66
    PB  - Science Publishing Group
    UR  - https://doi.org/10.11648/j.sdph.20260102.12
    AB  - Background: Sex-based biological differences significantly influence athletic performance and training adaptations, yet female athletes are substantially underrepresented in sports science research (80–90% of studies focus on males). Arab female athletes are virtually absent from the literature despite unique genetic, environmental, and cultural contexts that may modulate training responses. Objective: To investigate hormonal disparities (estradiol, testosterone, cortisol, IGF-1, T3, leptin) and their effects on training adaptation responses among elite Arab female athletes, comparing endurance versus strength sports, while examining modulation by genetic factors (ACTN3 R577X polymorphism) and health challenges (menstrual dysfunction, low energy availability). Methods: A 12-month longitudinal comparative experimental design will recruit 72 elite Arab female athletes (18–35 years) from Egypt, Saudi Arabia, UAE, and Tunisia, equally divided into endurance (long-distance running/swimming) and strength (weightlifting/powerlifting) groups. Measurements include monthly hormonal assays (ELISA/LC-MS), physiological adaptations (VO2max, 1RM, RMR), genetic analysis (ACTN3 PCR-RFLP), energy availability (7-day dietary records, LEAF-Q), and menstrual function monitoring. Statistical analyses include mixed ANOVA, ANCOVA, multiple regression, and Cohen's d effect sizes. Expected Results: Endurance athletes will show 7–9% VO2max improvement associated with estradiol fluctuations (r>0.5); strength athletes will demonstrate 15–20% 1RM increase and 8–9% type II fiber hypertrophy with modest testosterone contributions (r<0.3). Menstrual dysfunction (projected 55% in endurance vs. 35% in strength) and low energy availability (EA<30 kcal/kg FFM/day) will reduce RMR by 6–7% and blunt training adaptations by 30–50%. ACTN3 XX genotype (15–25% frequency) will be associated with enhanced strength gains (2–4% additional 1RM) but increased muscle injury risk (OR 5.9–7.9). Conclusion: This first comprehensive biological study of Arab female athletes will establish evidence-based, culturally-adapted training and nutritional guidelines, addressing the critical research gap in female sports science.
    VL  - 1
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Abstract
  • Keywords
  • Document Sections

    1. 1. Introduction
    2. 2. Methods
    3. 3. Expected Results
    4. 4. Discussion
    5. 5. Conclusion
    Show Full Outline
  • Abbreviations
  • Author Contributions
  • Conflicts of Interest
  • References
  • Cite This Article
  • Author Information