Precision nutrition in sports science: an opinion on omics-based personalization and athletic outcomes

This is a narrative systematic review, not an original experiment. The authors examined existing peer-reviewed literature (2005–2024) to answer three questions:

1. How can omics technologies (genomics, nutrigenomics, metabolomics, proteomics, transcriptomics) be used to personalise nutrition for athletes?

2. What evidence supports these interventions for improving performance, recovery, and injury prevention?

3. What challenges and opportunities exist for translating omics findings into practical sports settings?

The review compared traditional "one-size-fits-all" dietary recommendations against molecularly tailored approaches. No specific intervention was tested; instead, the authors synthesised findings from candidate gene studies, genome-wide association studies (GWAS), metabolomic profiling studies, proteomic analyses, and a few small clinical trials.

The review analysed studies involving:

Elite and recreational athletes across endurance sports (e.g., marathon, cycling), strength sports (e.g., weightlifting), and team sports (e.g., football, basketball)

Sample sizes ranged from small pilot studies (n=10–30) to large GWAS cohorts (n=1,000–5,000+)

Populations were predominantly Caucasian (European ancestry), with limited representation from Asian, African, or Latin American athletes

Age range: approximately 18–45 years

Both sexes were included across studies, but sex-specific analyses were rarely reported

No single study population was analysed; the review aggregated data across hundreds of individual studies.

The review did not use instruments or scales directly. Instead, it evaluated studies that used:

**Genomics:** SNP genotyping arrays, whole-genome sequencing, candidate gene analysis (e.g., PPARGC1A, PPARD, ACTN3)

**Metabolomics:** Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy to measure metabolites (e.g., lactate, fatty acids, amino acids) in blood, urine, or saliva

**Proteomics:** Liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify protein expression changes in muscle biopsies or blood

**Transcriptomics:** RNA sequencing (RNA-seq) or microarrays to measure gene expression changes in response to exercise or diet

**Performance outcomes:** VO2max (mL/kg/min), time to exhaustion (minutes), power output (watts), recovery rate (heart rate recovery, creatine kinase levels), injury incidence (self-reported or clinical diagnosis)

**Study design:** This is a narrative systematic review with a structured literature search. The authors searched PubMed, Scopus, and Web of Science for peer-reviewed articles published between 2005 and 2024 using keywords like "precision nutrition," "sports genomics," "nutrigenomics in athletes," "metabolomics exercise," and "multi-omics sports science." They included clinical trials, systematic reviews, expert opinions, and observational studies. Duplicates and low-quality studies were excluded.

**No randomisation, blinding, or washout periods**—this is a synthesis of existing research, not an original experiment.

**Statistical approach:** The review is qualitative/narrative. No meta-analysis was performed, so no pooled effect sizes, confidence intervals, or p-values are reported for the review itself. Individual studies cited within the review reported their own statistics (e.g., GWAS p-values < 5×10⁻⁸, odds ratios for injury risk).

**What this design can and cannot prove:**

**Can prove:** This design can identify trends, gaps, and consensus across the literature. It can highlight which omics technologies show promise and which lack evidence. It can summarise methodological challenges and ethical considerations.

**Cannot prove:** It cannot establish causality. It cannot provide precise effect sizes for any intervention. It cannot determine whether omics-based nutrition outperforms standard nutrition in a head-to-head trial. The lack of meta-analysis means no quantitative synthesis of results across studies.

**Major methodological weaknesses:**

Narrative reviews are susceptible to selection bias—authors may unconsciously favour studies supporting their viewpoint

No pre-registered protocol or systematic review registration (e.g., PROSPERO)

No risk of bias assessment for included studies

The search strategy is described but not reproducible in full (no exact search strings, no PRISMA flowchart)

Inclusion of "expert opinions" alongside clinical trials mixes evidence levels

The review is labelled as an "Opinion" article, which signals lower evidentiary weight than a systematic review with meta-analysis

**Heritability of athletic performance:** Up to 70% of variance in elite athlete status is attributed to genetic factors (from twin and family studies). However, this estimate does not account for gene-environment interactions.

**Genetic markers identified:** Genes such as PPARGC1A (mitochondrial biogenesis), PPARD (lipid metabolism), and ACTN3 (muscle fibre type) are associated with endurance and power traits. Over 50,000 SNPs influencing cellular functions have been identified, but most have small individual effects.

**GWAS limitations:** Hundreds of performance-related SNPs have been found, but reproducibility is poor due to small sample sizes and ethnic homogeneity. The Athlome Project Consortium aims to address this with larger, diverse cohorts.

**Metabolomics applications:** Metabolomic profiling can identify real-time metabolic shifts during exercise (e.g., fatty acid oxidation, glycolysis). For example, lactate and branched-chain amino acid (BCAA) levels change predictably with exercise intensity and duration.

**Proteomics insights:** Protein expression changes during recovery and adaptation have been mapped. For instance, muscle biopsy studies show altered expression of mitochondrial proteins after endurance training.

**Nutrigenomics examples:** The APOA2 genotype influences fatty acid metabolism; individuals with lactase non-persistence show altered gut microbiota when consuming milk. Tailored supplementation for vitamin D, iron, or folate based on genetic polymorphisms may improve immune function and reduce fatigue.

**Practical translation challenges:** High testing costs, lack of standardised protocols, and absence of user-friendly tools for coaches and athletes are major barriers. No large-scale RCT has demonstrated that omics-guided nutrition outperforms standard sports nutrition for performance outcomes.

**Primary vs. secondary outcomes:** The review does not distinguish primary from secondary outcomes. All findings are presented as qualitative observations from the literature.

No quantitative effect sizes are reported in this review. The authors do not provide any numbers that translate into real-world performance changes (e.g., "athletes with the PPARGC1A genotype improved VO2max by X mL/kg/min"). The closest to an effect magnitude is the heritability estimate of 70%, but this is a population-level statistic, not an individual intervention effect.

For context from the cited literature (not from the review itself):

ACTN3 R577X genotype: Sprinters with the RR genotype may have a ~2–3% advantage in power output compared to XX homozygotes

Metabolomic profiling: Post-exercise metabolite changes can vary by 20–40% between individuals, suggesting personalised refuelling strategies could matter

Lactase non-persistence: Affects ~65% of the global population, with symptom severity varying widely

Without meta-analytic pooling, the review provides no actionable effect magnitudes for an n=1 experimenter.

**What the authors acknowledge:**

Methodological inconsistencies across studies (different platforms, sample sizes, statistical thresholds)

High costs of omics technologies limit scalability

Ethical concerns about genetic data privacy and potential discrimination

Lack of standardised protocols for translating omics data into dietary recommendations

The complexity of integrating multi-omics data (genomics + metabolomics + proteomics) into a single actionable plan

**What a critical reader would note:**

**No original data:** This is an opinion piece, not a systematic review with meta-analysis. The label "Systematic Review" in the journal metadata conflicts with the article's self-description as a "narrative review" and "Opinion."

**Selection bias risk:** The authors did not pre-register their review protocol, so it is unclear how studies were selected or excluded.

**No quantitative synthesis:** Without effect sizes or confidence intervals, the review cannot guide evidence-based decisions.

**Population limits:** Most genetic studies are in Caucasian populations. Findings may not generalise to Asian, African, or Latin American athletes.

**Industry funding:** One author (Tjandrawinata) is affiliated with a pharmaceutical/nutraceutical research centre, which could introduce bias toward positive findings for supplementation.

**Duration of studies:** Most cited studies are short-term (days to weeks). No long-term (months to years) data on omics-guided nutrition exists.

**Lack of blinding:** Few cited studies used double-blind designs for dietary interventions, introducing placebo effects.

**Publication bias:** The field likely over-represents positive findings; null results from small GWAS or metabolomics studies are rarely published.

For someone running their own n=1 experiment, this review provides a framework for what to test, but the evidence base is too weak to recommend specific omics-guided protocols. Here is how to approach it:

### What to test

**Genetically guided macronutrient ratios:** If you know your ACTN3 genotype (RR = power-oriented, XX = endurance-oriented), test whether a higher protein intake (~2.0 g/kg vs. 1.2 g/kg) improves power output or recovery over 4 weeks.

**Lactose tolerance:** If you suspect lactase non-persistence (common in Asian, African, and Southern European populations), test a 2-week period with dairy vs. lactose-free alternatives, measuring gastrointestinal symptoms and training performance.

**Metabolite-guided refuelling:** Use a continuous glucose monitor (CGM) or lactate meter to track post-exercise metabolite responses. Test whether personalised carbohydrate timing (e.g., 30 g immediately post-exercise vs. 60 g after 1 hour) affects next-day performance.

### Minimum meaningful duration

**Genetic testing:** One-time test (lifelong validity), but dietary interventions based on genetics should run for at least **4–6 weeks** to see adaptation effects.

**Metabolomic/proteomic tracking:** At least **2–4 weeks** of daily or post-exercise sampling to establish baseline variability.

**Dietary elimination/reintroduction:** **2 weeks elimination + 2 weeks reintroduction** (e.g., for lactose or gluten).

### What to measure

**Primary outcome:** Performance metric relevant to your sport (e.g., 5 km run time, max bench press weight, cycling power output in watts)

**Secondary outcomes:** Recovery rate (heart rate variability [HRV] measured daily via app or chest strap), subjective energy (1–10 scale daily), gastrointestinal symptoms (Bristol Stool Scale, bloating score 0–10), sleep quality (sleep duration and efficiency via wearable)

**Biomarkers (if accessible):** Fasted blood glucose, lactate post-exercise, creatine kinase (muscle damage marker) via finger-prick test

### Key confounds to control for

**Training load:** Keep training volume and intensity constant during the test period (e.g., same weekly mileage or gym sessions). Use a training log.

**Sleep:** Track sleep duration and quality; aim for consistent bedtimes. Poor sleep can mask dietary effects.

**Hydration:** Maintain consistent water intake (e.g., 30–40 mL/kg body weight daily).

**Supplement use:** Avoid new supplements during the test period. Continue only established ones at the same dose.

**Menstrual cycle (for women):** Track cycle phase; performance and metabolism vary across the menstrual cycle. Run the intervention across at least one full cycle (4–6 weeks).

**Placebo effect:** If testing a supplement or dietary change, use a blinded design if possible (e.g., have someone else prepare identical-looking meals or capsules).

### What a positive result would look like

**Performance:** A consistent improvement of ≥2–3% in your primary metric (e.g., 5 km time drops from 20:00 to 19:24 or faster) over the intervention period compared to baseline.

**Recovery:** HRV increases by ≥5–10% from baseline (e.g., from 65 ms to 70 ms average), or subjective recovery scores improve by ≥1 point on a 1–10 scale.

**Symptoms:** Gastrointestinal symptom scores drop by ≥50% (e.g., from 4/10 to 2/10) during the elimination phase and return during reintroduction.

**Statistical confidence:** For an n=1 experiment, use a simple moving average (7-day rolling mean) to smooth daily noise. A result is meaningful if the change exceeds the typical day-to-day variability (e.g., if your 5 km time varies by ±15 seconds normally, a 30-second improvement is likely real).

**Bottom line:** This review confirms that omics-based precision nutrition is a promising frontier, but the evidence is not yet strong enough to recommend specific protocols for self-experimenters. The most actionable takeaway is to test one variable at a time (e.g., lactose elimination, carbohydrate timing) using your own genetic or metabolic data as a hypothesis generator, not as a prescription. Run each test for 4–6 weeks, measure performance and recovery objectively, and control for training, sleep, and menstrual cycle. A positive result is a ≥2–3% improvement in your primary metric that exceeds your normal day-to-day variability.