The Biological
Architecture of You
Xelecta integrates three physiological axes — metabolic, cardio-respiratory, and sleep/neuroprotection — with a >200-SNP genomic layer. Every protocol suggestion is traceable to published research and reviewed by you before it changes anything.
200+
SNPs interrogated
5 min
CGM sampling interval
3
Physiological axes integrated
~40
SNPs driving protocol logic*
*The remaining SNPs are reported as informational only. Clinically actionable pharmacogenomic and nutrigenomic decision support follows CPIC and PharmGKB guidance where available. Individual results vary. See full disclaimer in our Terms of Service.
The Three-Axis Framework
Most platforms optimise one system. Xelecta integrates three — because metabolic, cardiovascular, and neurological/sleep health are coupled in ways a single biomarker can't capture.
Metabolic Resilience
Postprandial glucose dynamics are one of the most informative metabolic readouts available outside a research lab. Continuous glucose monitoring (CGM) captures how your physiology handles meals, sleep, exercise, and stress — across days rather than at a single fasted draw.
- Interstitial glucose tracking via FDA-cleared CGM (5-minute resolution)
- Glycemic variability metrics (CV%, MAGE) derived per-day and per-meal
- Time-in-range estimation against age-appropriate reference bands
- Postprandial curve classification: flat, spike-and-crash, or sustained
- Cross-axis correlation linking glucose excursions to sleep and HRV
Clinical Evidence
Hallberg et al. (Diabetes Therapy 2018) showed that continuous remote care plus nutritional ketosis produced HbA1c <6.5% off most glycemic medications in roughly 60% of programme completers at one year — among the strongest published examples of behaviour-plus-monitoring driving meaningful metabolic change. In a Chinese centenarian cohort, lower glycemic variability and improved time-in-range were associated with longevity (Yang et al., Front Nutr 2022). Note: large RCTs in healthy adults specifically validating CGM-driven wellness benefits are still pending.
All protocols are advisory only. Consult your physician before making health decisions.
Cardio-Respiratory Precision
Cardiovascular age can diverge from chronological age starting in midlife. Body composition trends, resting heart-rate variability, and vascular-stiffness estimates give a longitudinal picture that annual lab panels miss.
- Body composition via multi-frequency BIA, calibrated against DEXA reference cohorts (BIA is not equivalent to DEXA; agreement limits are documented in the device manual)
- Visceral-fat index — an established cardiometabolic risk marker
- Resting HRV (RMSSD, SDNN) from a wearable as an autonomic-tone proxy
- Vascular-age estimate from oscillometric / wearable PWV correlates (carotid–femoral PWV remains the gold standard)
- Weekly trends in fat-free mass and skeletal-muscle index
Clinical Evidence
Visceral adipose tissue is independently associated with cardiometabolic disease and CV events (Neeland et al., Circulation 2019); reductions in VAT improve cardiometabolic risk markers (Lee et al., JACC 2016). Reduced HRV predicts cardiac events and all-cause mortality in community-dwelling adults (Tsuji et al., Circulation 1994; Tsuji et al., Circulation 1996). Pulse-wave-velocity validity depends on the measurement method (2024 AHA Hypertension validation guidance).
All protocols are advisory only. Consult your physician before making health decisions.
Sleep & Neuroprotection
Sleep is one of the most consequential and modifiable inputs to long-term cognitive health. Deep-sleep duration, sleep continuity, and autonomic stability are linked to clearance of metabolic waste in the brain and to long-term cognitive trajectory.
- Wearable-derived sleep staging (deep / REM / light / wake) — moderate-to-substantial agreement with PSG (κ ≈ 0.5–0.65 in top devices); not a PSG replacement
- Autonomic-stability index from nocturnal HRV — not a substitute for direct cortisol measurement
- APOE-aware deep-sleep targets where APOE results are available through a CLIA-partner lab
- Light-exposure and sleep-hygiene suggestions integrated into the protocol
- Suggested adjustments — supplement timing, exposure, exercise placement — that you review and confirm
Clinical Evidence
Xie et al. (Science 2013) reported a ~60% expansion of brain interstitial space during natural sleep and anaesthesia in mice, with a corresponding increase in convective CSF–ISF exchange and β-amyloid clearance. In human cohorts, shorter sleep and lower sleep efficiency are associated with greater amyloid burden (Spira et al., JAMA Neurology 2013), and APOE ε4 carriers show greater sensitivity of cognitive trajectory to sleep quality (Lim et al., Sleep 2013). Direct in-vivo measurement of human glymphatic flow is an active area of research, not a deployable consumer metric.
All protocols are advisory only. Consult your physician before making health decisions.
200+ SNPs, ~40 Drive the Protocol
Xelecta interrogates more than 200 SNPs across pharmacogenomic, cardiometabolic, and methylation pathways. A defined ~40-SNP subset — drawn from CPIC and PharmGKB-curated annotations — drives current protocol decisions; the remainder are reported for context.
APOE
Apolipoprotein E — lipid transport & Alzheimer's risk stratification
MTHFR
Methylenetetrahydrofolate reductase — folate metabolism & homocysteine
COMT
Catechol-O-methyltransferase — dopamine catabolism & stress resilience
FTO
Fat mass and obesity-associated — adipogenesis regulation
BDNF Val66Met
Brain-derived neurotrophic factor — neuroplasticity & memory
CYP1A2
Cytochrome P450 1A2 — caffeine and pharmaceutical metabolism
PPARG
Peroxisome proliferator-activated receptor gamma — insulin sensitivity
ACE I/D
Angiotensin-converting enzyme — cardiovascular & VO₂max response
Genetic analysis is for wellness optimisation only and does not constitute medical diagnosis. Raw DNA data is decrypted only inside hardware-attested confidential-computing enclaves; only computed outputs leave the enclave. APOE results, where returned, follow an FDA-cleared CLIA-partner pathway.
The Xelecta Optimization Engine
The platform ingests data from all three axes — correlating CGM, sleep staging, HRV, and your genomic profile — to surface high-leverage suggestions. Every change is reviewed and confirmed by you before it takes effect.
Cross-axis correlation
Pattern detection across the CGM, body-composition, sleep, and HRV streams
Pharmacogenomic matching
Supplement choices guided by CYP450 and MTHFR metaboliser status, per CPIC where applicable
Suggested adjustments
Protocol suggestions surface when patterns emerge — you review and confirm before anything changes
21:04:12 CGM_stream glucose=112 mg/dL ↑
21:04:17 HRV_stream RMSSD=34ms (↓ from 44ms baseline)
21:04:17 correlator post-meal spike + HRV drop detected
21:04:18 genomics PPARG rs1801282 Pro12Ala → insulin sensitivity +
21:04:18 engine adjusting tomorrow AM protocol
21:04:18 → berberine +200mg pre-breakfast
21:04:18 → sleep target deep_sleep >90min
21:04:19 security enclave_verified genomic_data_ephemeral
References
Where the claims on this page come from
- Hallberg SJ et al. Effectiveness and safety of a novel care model for the management of type 2 diabetes at 1 year. Diabetes Therapy 2018. PubMed. 2-year follow-up: Athinarayanan SJ et al. Frontiers in Endocrinology 2019. PubMed.
- Yang Z et al. Effects of variability in glycemic indices on longevity in Chinese centenarians. Frontiers in Nutrition 2022. PMC.
- Glycemic variability and cognitive decline in T2D: systematic review and meta-analysis. PLOS ONE 2023. PLOS ONE.
- Neeland IJ et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Circulation 2019. AHA Journals.
- Lee JJ et al. Association of changes in abdominal fat quantity and quality with incident cardiovascular disease risk factors. JACC 2016. JACC.
- Tsuji H et al. Reduced heart rate variability and mortality risk in an elderly cohort: the Framingham Heart Study. Circulation 1994. AHA Journals. And: Tsuji H et al. Impact of reduced heart rate variability on risk for cardiac events. Circulation 1996. AHA Journals.
- Townsend RR et al. Recommendations for validation of noninvasive arterial pulse-wave-velocity measurement devices. Hypertension 2024. AHA Journals.
- Xie L et al. Sleep drives metabolite clearance from the adult brain. Science 2013. Science · PMC.
- Spira AP et al. Self-reported sleep and β-amyloid deposition in community-dwelling older adults. JAMA Neurology 2013. JAMA Network.
- Lim ASP et al. Modification of the relationship of the apolipoprotein E ε4 allele to the risk of Alzheimer disease and neurofibrillary tangle density by sleep. Sleep 2013. PubMed.
- Chinoy ED et al. Performance of multiple wearables for sleep tracking in healthy adults. Sensors 2024. MDPI. Multi-tracker validation: JMIR mHealth uHealth 2023. JMIR.
- Bilodeau B et al. BIA vs DXA body-composition assessment. PLOS ONE 2018. PMC. Multi-frequency BIA validation: European Journal of Clinical Nutrition 2025. Nature.
- Liang Y et al. Glucose-lowering effect of berberine on type 2 diabetes: systematic review and meta-analysis. Frontiers in Pharmacology 2022. Frontiers.
- CPIC — Clinical Pharmacogenetics Implementation Consortium guidelines. cpicpgx.org. PharmGKB clinical annotations. pharmgkb.org.
Ready to apply the science?
Start with the Metabolic Reset kit — ships immediately, dashboard access included.