DIFFERENTIAL ANCESTRAL ADAPTATION HYPOTHESIS (DAAH)

Author: Lucas Dragases

A Theoretical Framework for Individual Metabolic Variance

Based on Ancestral Dietary History

Working Paper — Version 1.0

2026

Abstract

Prevailing nutritional science operates largely under the assumption that evidence-based dietary guidelines can be applied universally across human populations. However, substantial inter-individual variance in metabolic response to dietary macronutrients — particularly carbohydrates — suggests that a single optimal diet does not exist for all humans. The Differential Ancestral Adaptation Hypothesis (DAAH) proposes that this variance is not random but is systematically predicted by the duration and intensity of a population's agricultural history. Because agriculture represents a maximum of 10,000–12,000 years of human dietary practice — a period insufficient for complete genomic adaptation — individuals whose lineages experienced shorter exposure to high-carbohydrate diets retain metabolic profiles calibrated for protein- and fat-dominant nutrition. This paper synthesizes convergent evidence from paleoanthropology, population genetics, gastroenterology, and metabolic epidemiology to formalize this hypothesis into a testable theoretical framework. We identify measurable biological markers — including craniodental morphology, salivary amylase gene copy number (AMY1), intestinal transit time, and glycemic variability — that cluster into two primary metabolic phenotypes: the Carnivore-Calibrated Profile (CCP) and the Carbohydrate-Calibrated Profile (CarCP), with a third hybrid category. We propose a research agenda to empirically validate this framework and discuss its potential implications for personalized nutrition medicine.

Keywords: evolutionary nutrition, ancestral diet, AMY1, metabolic mismatch, craniodental morphology, personalized nutrition, carbohydrate tolerance, dietary adaptation

1. Introduction

The modern nutritional landscape is characterized by a paradox: despite decades of dietary research and public health guidance, rates of metabolic disease — including type 2 diabetes, obesity, and metabolic syndrome — continue to rise globally. A consistent finding across this research, frequently underreported in population-level analyses, is the dramatic inter-individual variance in metabolic response to identical dietary inputs (Zeevi et al., 2015; Dahl et al., 2020). Two individuals consuming the same meal may exhibit markedly different glycemic responses, gastrointestinal outcomes, and long-term health trajectories.

Conventional explanations for this variance focus on microbiome composition, physical activity, sleep quality, and psychological stress. While these factors are relevant, they fail to account for a deeper layer of biological heterogeneity: the divergent evolutionary histories that different human lineages have experienced with respect to dietary macronutrients. The transition from hunter-gatherer subsistence to agricultural food systems — which occurred approximately 10,000–12,000 years ago in the Fertile Crescent, and considerably more recently in many other regions — introduced a dramatic shift in the macronutrient landscape available to human metabolism. The primary question this paper addresses is whether this transition was sufficient, in evolutionary timescales, to produce uniform metabolic adaptation across all human populations.

We propose that the answer is clearly negative. The Differential Ancestral Adaptation Hypothesis (DAAH) formalizes the position that human populations — and by extension, individuals — exist on a spectrum of metabolic adaptation to dietary carbohydrates, determined primarily by the duration and intensity of their ancestral agricultural exposure. This spectrum has measurable biological correlates and produces predictable inter-individual differences in health outcomes under modern dietary conditions.

2. Theoretical Foundation

2.1 The Evolutionary Timeline Problem

Homo sapiens as a species has existed for approximately 200,000–300,000 years. For the vast majority of this period — roughly 95% of total human evolutionary history — subsistence relied on hunting and gathering, with dietary profiles dominated by animal protein, fat, and seasonal plant foods. High-starch, grain-based diets became available only with the Neolithic agricultural revolution, beginning approximately 10,000–12,000 years before present (BP) in Southwest Asia (Zohary et al., 2012).

Critically, this transition was not simultaneous across global populations. Agricultural adoption reached Central Europe approximately 7,500 years BP, Scandinavia around 5,000–6,000 years BP, and many indigenous populations in the Americas, Australia, and Sub-Saharan Africa have experienced intensive exposure to grain-based diets only within the past 200–500 years — a period representing as few as 10–20 human generations (Diamond, 1997; Bellwood, 2005).

The theoretical importance of this timeline is foundational. Genetic adaptation is constrained by generation time, population size, selection coefficient, and the polygenic complexity of the trait under selection. Lactase persistence — one of the most frequently cited examples of rapid human dietary adaptation — required approximately 7,000–8,000 years to spread through Northern European populations and remains absent in approximately 65% of the global adult population (Itan et al., 2009). If a single, relatively simple genetic trait required this duration to partially propagate, the polygenic architecture underlying comprehensive carbohydrate metabolism adaptation — encompassing amylase expression, insulin sensitivity, intestinal morphology, hepatic fructose processing, and microbiome composition — cannot plausibly have reached completion across all human lineages within the available timeframe.

2.2 Core Proposition

DAAH proposes the following core proposition, which is both falsifiable and testable:

The duration and intensity of a human lineage's exposure to high-carbohydrate agricultural diets is a primary determinant of individual metabolic adaptation to dietary carbohydrates. This adaptation is incomplete and heterogeneous across global populations, producing measurable inter-individual variance in carbohydrate processing capacity that is reflected in identifiable biological markers and predicts differential health outcomes under high-carbohydrate dietary conditions.

3. Convergent Evidence

3.1 Craniodental Morphology as a Dietary Adaptation Marker

Among the most consistently documented morphological changes associated with the agricultural transition is the systematic alteration of craniofacial and dental architecture. Bioarchaeological studies comparing pre-agricultural and post-agricultural skeletal populations reveal a coherent pattern: hunter-gatherer populations exhibited longer, narrower mandibles with adequate spacing for full dental eruption, minimal malocclusion, and pronounced canine development. Post-agricultural populations demonstrate shorter, broader mandibles, significantly elevated rates of dental crowding, and increased malocclusion prevalence (Pinhasi et al., 2015; Lieberman, 2011; Corruccini, 1984).

The proposed mechanism is well-supported: mandibular development is responsive to mechanical loading during growth. The shift from mechanically demanding whole-food diets to softened, processed grain-based foods reduced masticatory stress during the critical developmental window, resulting in underdeveloped jaw architecture relative to genetically determined tooth size. This epigenetic-developmental interaction explains why the phenotypic change occurred rapidly — within generations — rather than requiring full genetic substitution (Mew, 2004; Kahn et al., 2020).

Weston A. Price's systematic photographic documentation across fourteen traditional versus modernized populations (1939) provided early evidence that this pattern was not confined to archaeological specimens but reproducible in living populations transitioning to agricultural diets. Contemporary orthodontic epidemiology confirms that malocclusion rates have increased substantially in modernized populations, with prevalence estimates of 60–75% in industrialized nations versus approximately 5–10% in pre-industrial skeletal series (Proffit et al., 2018).

DAAH proposes that craniodental morphology in living individuals — specifically mandibular width, inter-dental spacing, and canine prominence — represents a probabilistic proxy for ancestral dietary history, reflecting the interaction between genetic predisposition and developmental environment. This relationship is not deterministic but constitutes a valid epidemiological signal when evaluated at the population level and interpreted probabilistically at the individual level.

3.2 Salivary Amylase Gene Copy Number (AMY1)

The salivary amylase gene (AMY1) encodes the enzyme responsible for initiating starch digestion in the oral cavity. Humans exhibit remarkable copy number variation (CNV) in AMY1, ranging from 2 to 15+ copies per diploid genome, with a population mean of approximately 6–7 copies in high-starch populations versus 4–5 in low-starch populations (Perry et al., 2007). This copy number variation directly determines salivary amylase protein concentration, which varies up to eightfold between individuals.

Perry et al. (2007) demonstrated in a landmark Nature Genetics paper that AMY1 copy number correlates significantly with the traditional dietary reliance on starch in ancestral populations. High-starch agricultural populations show higher mean AMY1 copy numbers than populations with low historical starch intake, including pastoralists and hunter-gatherers. This correlation has been replicated across multiple independent cohorts (Mandel et al., 2010; Boehlke et al., 2015; Carpenter et al., 2015).

AMY1 copy number has downstream consequences for carbohydrate metabolism. Lower AMY1 expression is associated with elevated postprandial glycemic response to starchy foods, altered satiety signaling, and — in some studies — increased risk of obesity and metabolic syndrome under high-carbohydrate dietary conditions (Falchi et al., 2014; Bonnefond et al., 2017). Critically, AMY1 CNV represents heritable genetic variance, not environmentally induced epigenetic modification, and thus constitutes a direct measure of ancestral genetic adaptation to dietary starch.

DAAH positions AMY1 copy number as the most direct currently available genetic marker for individual position on the ancestral adaptation spectrum. Individuals with low AMY1 copy numbers — disproportionately represented in populations with shorter agricultural histories — are predicted to experience greater metabolic dysfunction under high-carbohydrate dietary conditions than individuals with high copy numbers, independent of other lifestyle variables.

3.3 Gastrointestinal Morphology and Transit Time

Comparative gastroenterology provides additional convergent evidence for population-level variation in digestive architecture. Intestinal length, particularly of the large intestine, correlates broadly with dietary strategy across species: herbivores maintaining longer colons for fermentative extraction of plant carbohydrates, carnivores exhibiting shorter, simpler colonic anatomy optimized for rapid protein and fat transit (Stevens and Hume, 1998).

Within human populations, variation in colonic microbiome composition — which is both heritable and influenced by ancestral diet — produces functionally distinct fermentative capacities. Populations with long agricultural histories maintaining high-fiber, high-carbohydrate diets show greater abundance of Prevotella species and other saccharolytic bacteria, whereas populations with hunting and pastoral traditions show Bacteroides-dominant profiles with greater proteolytic capacity (De Filippo et al., 2010; Smits et al., 2017).

Gastrointestinal transit time — the duration from ingestion to elimination — is a clinically relevant metric that varies substantially between individuals and correlates with dietary composition. High-carbohydrate diets producing fermentative gas, osmotic water retention, and variable motility patterns result in characteristically different transit profiles than low-carbohydrate, protein-fat dominant diets, which produce compact, rapid transit in individuals with the appropriate enzymatic and microbial profile. Paradoxically, the direction of this effect reverses depending on an individual's metabolic calibration: individuals with low carbohydrate-processing capacity may experience prolonged, dysregulated transit on high-carbohydrate diets and normalized, efficient transit on low-carbohydrate diets — the exact inverse of clinical expectations derived from population-average data.

3.4 Population-Level Metabolic Epidemiology

The differential susceptibility of distinct human populations to metabolic disease under identical modern dietary conditions constitutes perhaps the most epidemiologically powerful evidence for DAAH. Several patterns are particularly illustrative:

South Asian populations, despite millenia of agricultural history with rice and legume-based diets, exhibit among the highest global prevalences of type 2 diabetes and insulin resistance — at lower body mass indices than European populations (Misra and Khurana, 2011). This suggests that agricultural history alone is insufficient; the type, processing, and glycemic load of ancestral staple foods interacts with the adaptation timeline.

East Asian populations with long histories of rice cultivation show, on average, more favorable metabolic responses to high-carbohydrate diets when physically active, consistent with higher AMY1 copy numbers and adapted insulin dynamics (Maki et al., 2019). However, rapid adoption of Western dietary patterns produces significant metabolic disruption, suggesting that adaptation is real but fragile against extreme deviation.

Arctic and sub-Arctic populations — including Inuit, Yupik, and circumpolar Siberian groups — maintained dietary profiles of 90%+ animal-derived calories for thousands of years. These populations show markedly impaired glucose tolerance upon introduction of carbohydrate-rich modern diets, with disproportionate rates of hyperglycemia and metabolic syndrome (Young et al., 2000; Ebbesson et al., 2005). Genetic analysis has identified specific adaptive variants in fatty acid metabolism genes in these populations, representing a mirror-image adaptation to carnivorous diets.

Central and Northern European populations — including Slavic lineages — occupied an intermediate position: agricultural adoption occurred 5,000–7,500 years BP, but with lower-starch staple crops (root vegetables, game, dairy) than East Asian or Middle Eastern agricultural societies. DAAH predicts that this population cluster should show higher variance in carbohydrate tolerance than either East Asian or Arctic populations, with a meaningful subpopulation retaining metabolic profiles better suited to low-carbohydrate diets.

4. Proposed Metabolic Phenotype Classification

Based on the convergent evidence above, DAAH proposes a three-category classification system for individual metabolic phenotypes, reflecting position on the ancestral adaptation spectrum. These categories are probabilistic, not deterministic, and intended as clinical heuristics rather than rigid taxonomies.

4.1 Carnivore-Calibrated Profile (CCP)

Individuals exhibiting the CCP are predicted to show: narrower mandibular architecture with greater inter-dental spacing; lower AMY1 copy number (estimated below population median); Bacteroides-dominant intestinal microbiome; rapid, efficient gastrointestinal transit on protein-fat dominant diets with dysregulated transit on high-carbohydrate diets; elevated postprandial glycemic variability on standard mixed diets; and subjective reports of superior energy stability, satiety, and gastrointestinal comfort on low-carbohydrate dietary patterns. This profile is predicted to be disproportionately prevalent in lineages with shorter agricultural exposure histories.

4.2 Carbohydrate-Calibrated Profile (CarCP)

Individuals exhibiting the CarCP are predicted to show: broader mandibular architecture with reduced inter-dental spacing; higher AMY1 copy number (above population median); Prevotella-enriched intestinal microbiome with strong saccharolytic capacity; stable glycemic regulation on moderate-carbohydrate diets; and equivalent gastrointestinal function across dietary patterns. This profile is predicted to be disproportionately prevalent in lineages with extended high-starch agricultural histories.

4.3 Hybrid Profile (HP)

A substantial proportion of individuals will exhibit mixed markers reflecting ancestral admixture, partial adaptation, or intermediate lineage history. This category requires individualized assessment of subjective response to dietary manipulation rather than reliance on morphological or genetic markers alone.

5. Proposed Research Agenda

DAAH generates several directly testable empirical predictions. We propose the following research priorities to evaluate the hypothesis:

5.1 Correlation Study: AMY1 CNV and Craniodental Morphology

The most immediate testable prediction is that AMY1 copy number and mandibular morphological indices (inter-canine width, dental crowding score, canine prominence index) will show significant positive correlation in a population-representative sample, after controlling for orthodontic treatment history and childhood dietary environment. A sample of n=300–500 adult subjects with documented lineage history and no orthodontic intervention would constitute an appropriate primary test.

5.2 Intervention Study: Dietary Phenotype and Metabolic Response

A crossover dietary intervention study — in which subjects classified by DAAH phenotype markers undergo standardized high-carbohydrate and low-carbohydrate dietary periods with continuous glycemic monitoring — would provide direct evidence for the predictive validity of the classification system. The primary outcome would be differential glycemic variability and gastrointestinal symptom burden by phenotype category.

5.3 Population Genomics Review

Systematic review and meta-analysis of existing population genomics datasets — including the 1000 Genomes Project and HGDP — could test whether AMY1 copy number distribution correlates with estimated duration of agricultural exposure across sampled populations, refining the population-level predictions of DAAH.

6. Discussion

DAAH does not propose that any single dietary pattern is universally optimal or suboptimal. Rather, it formalizes the position that dietary optimality is individual-specific, predictable from biological markers, and rooted in evolutionary history. This framework is explicitly non-prescriptive at the population level: it provides a mechanism for personalizing dietary recommendations rather than replacing one universal prescription with another.

The normalization of gastrointestinal dysfunction in industrial societies warrants particular attention. Symptoms including irregular transit, excessive fermentative gas production, bloating, and prolonged defecation time are frequently categorized as normal variants of digestive function. From an evolutionary perspective, however, a digestive system optimized for survival would be expected to process food rapidly, completely, and without significant discomfort — reducing time in vulnerable postures and minimizing energy expenditure on inefficient fermentation. The widespread prevalence of these symptoms in populations consuming high-carbohydrate diets is consistent with the metabolic mismatch predicted by DAAH for individuals with CCP profiles.

The implications for personalized medicine are significant. If DAAH is validated, dietary guidance could be meaningfully individualized using a combination of genetic markers (AMY1 CNV), morphological assessment, and structured dietary response monitoring — without requiring expensive or invasive clinical procedures for initial screening.

8. Proposed Diagnostic Protocol for Phenotype Classification

A theoretically robust framework requires a corresponding clinical protocol by which individual phenotype classification can be reliably and reproducibly determined. The following three-tier diagnostic protocol is proposed, structured so that each tier provides independent value while the combination of all three maximises classification accuracy. The protocol is designed to be accessible without specialist equipment at the screening level, with optional laboratory confirmation for individuals or researchers requiring higher precision.

8.1 Tier 1 — Morphological Screening (Day 1, approx. 15 minutes)

The first tier constitutes a rapid, non-invasive visual assessment of craniodental morphology. As established in Section 3.1, mandibular architecture and dental arrangement represent developmental proxies for ancestral dietary history. The following parameters are assessed by self-examination or clinical observation:

Mandibular width index: The inter-canine width (distance between upper canine tips) is measured or visually estimated relative to the width of the lower face. A narrower arch with greater anterior tooth spacing is consistent with the CCP signal; a broader arch with tight dental contact is consistent with CarCP.

Canine prominence: Upper and lower canine teeth are assessed for relative length and sharpness compared to adjacent premolars. Pronounced, well-defined canines extending noticeably below the occlusal plane represent a CCP morphological signal.

Crowding and contact: The degree of inter-dental contact and rotational crowding of the anterior teeth is noted. Marked crowding — teeth overlapping or rotated — is a CarCP-associated developmental outcome reflecting mandibular compression under agricultural dietary conditions during growth.

Importantly, self-reported orthodontic history must be recorded at this stage. Individuals with a history of fixed orthodontic appliance treatment should have their morphological scores interpreted with reduced weighting, as appliance treatment systematically alters the dental markers assessed in this tier. In such cases, greater diagnostic weight is assigned to Tiers 2 and 3.

Morphological screening is scored on a five-point scale per parameter, generating a composite Morphological Index Score (MIS) ranging from 3 (strongly CarCP) to 15 (strongly CCP). A score of 8–10 indicates the Hybrid Profile. MIS alone is insufficient for definitive classification but provides the initial probabilistic prior that subsequent tiers refine.

8.2 Tier 2 — Structured Dietary Response Monitoring (14 days)

The second diagnostic tier constitutes the primary evidence base for phenotype classification. Fourteen days of structured dietary alternation with systematic self-monitoring of physiological response provides the most ecologically valid data available without laboratory equipment. This period is divided into two sequential seven-day dietary phases.

Phase A — Low-carbohydrate baseline (Days 1–7): The subject adheres to a diet consisting exclusively of animal-derived foods (meat, fish, eggs, animal fats) with total carbohydrate intake below 20g per day. This phase establishes the individual's baseline physiological response to protein-fat dominant nutrition. No caloric restriction is imposed; ad libitum consumption is encouraged to prevent confounding by energy deficit.

Phase B — Controlled carbohydrate reintroduction (Days 8–14): The subject reintroduces a standardized set of whole-food carbohydrate sources — specifically rice, oats, and root vegetables — at moderate quantities (150–250g carbohydrate per day). Processed sugars, refined flour, and mixed ultra-processed foods are excluded to isolate the metabolic response to ancestrally plausible carbohydrate sources rather than modern food engineering.

Throughout both phases, the subject records the following parameters twice daily using a standardized Response Monitoring Log:

Gastrointestinal transit quality: Stool consistency assessed using the Bristol Stool Scale (1–7), estimated transit time based on dietary markers, and presence or absence of bloating, flatulence, cramping, or urgency. This parameter carries the highest diagnostic weight within Tier 2, as gastrointestinal transit is the most sensitive and immediate physiological indicator of dietary compatibility.

Postprandial energy and cognitive clarity: Subjective rating (1–10) of energy level and mental clarity at 60 and 120 minutes post-meal. Marked postprandial fatigue or cognitive fog following carbohydrate meals — in contrast to stability following protein-fat meals — is a primary CCP indicator.

Satiety duration: Time elapsed from meal completion to first subjective experience of hunger. Protein-fat meals sustaining satiety for 5+ hours in contrast to carbohydrate meals requiring re-feeding within 2–3 hours constitutes a CCP signal consistent with lower AMY1-mediated insulin dynamics.

Sleep quality and morning wellbeing: Subjective rating of sleep quality and morning energy state. Phase-associated differences in sleep architecture are increasingly recognized as metabolic indicators.

Stool volume assessment: This parameter, while rarely included in clinical nutritional protocols, is proposed as a significant diagnostic signal within DAAH. From an evolutionary perspective, a digestive system processing food with high efficiency should produce compact, low-volume output with rapid, effortless transit. Markedly elevated stool volume on a carbohydrate-dominant phase compared to a protein-fat phase — reflecting fermentative residue and osmotic water retention — constitutes strong evidence of carbohydrate mismatch consistent with the CCP profile.

Phase comparison scores are computed for each parameter, generating a Dietary Response Differential (DRD) index. A DRD showing consistently superior gastrointestinal, energetic, and cognitive function during Phase A versus Phase B constitutes primary evidence for CCP classification. The inverse pattern supports CarCP. Minimal phase-dependent differences indicate HP.

8.3 Tier 3 — Laboratory Confirmation (Optional)

For individuals, clinicians, or researchers seeking biochemical confirmation of phenotype classification, the following laboratory investigations are recommended as adjuncts to Tiers 1 and 2:

AMY1 copy number analysis: Saliva-based DNA testing for salivary amylase gene copy number is available through commercial genomic services. A result below the population median (approximately 5–6 copies) constitutes molecular support for CCP classification; above-median results support CarCP. This test provides the most direct available genetic correlate of the DAAH framework.

Continuous glucose monitoring (CGM): A 14-day CGM period, ideally overlapping with the Tier 2 dietary protocol, provides objective glycemic variability data independent of self-report. Greater glycemic excursions during Phase B relative to Phase A supports CCP classification. CGM data also enables calculation of the Time in Range metric and postprandial glucose area under the curve for each dietary phase.

Fasting insulin and HOMA-IR: A single fasting blood panel measuring insulin concentration and fasting glucose, enabling calculation of the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), provides a baseline metabolic index. Elevated HOMA-IR in individuals reporting better subjective response on Phase A than Phase B strengthens CCP classification.

Gut microbiome sequencing: Stool microbiome profiling during each dietary phase provides data on the relative abundance of saccharolytic versus proteolytic bacterial communities. A Prevotella-dominant profile under carbohydrate feeding with CCP subjective responses may indicate microbiome-phenotype concordance; a Bacteroides-dominant profile under protein-fat feeding with CCP responses indicates full marker convergence.

8.4 Classification Algorithm and Scoring

Final phenotype classification is determined by weighted integration of all available tier data. Tier 2 dietary response monitoring carries 60% of the classification weight, reflecting its status as the most direct and ecologically valid evidence. Tier 1 morphological screening contributes 25%, and Tier 3 laboratory data — when available — contributes the remaining 15%, distributed proportionally across available tests.

Individuals with converging CCP signals across all three tiers — narrow mandible, pronounced canines, markedly superior gastrointestinal function on Phase A, low AMY1 copy number, elevated glycemic variability on Phase B — are classified as definitive CCP. Individuals with convergent CarCP signals across tiers are classified as definitive CarCP. Discordant or intermediate scores across tiers are classified as HP, with specific discordance patterns noted for clinical guidance.

The classification system explicitly accounts for the probabilistic nature of individual markers. No single marker is treated as definitively diagnostic. Classification confidence is reported as a probability range rather than a binary determination, acknowledging the continuous nature of the underlying biological spectrum.

8.5 Limitations of the Diagnostic Protocol

Several limitations of this protocol require explicit acknowledgment. First, the 14-day dietary monitoring period, while clinically practical, may be insufficient to capture the full metabolic adaptation to dietary phase transitions, particularly for individuals transitioning from long-term high-carbohydrate habits whose gut microbiome and enzyme expression require longer adjustment periods. Second, self-reported symptom ratings introduce subjective bias; future validation studies should incorporate objective biomarker measurement throughout the monitoring period. Third, the morphological screening tier is compromised in individuals with orthodontic treatment history, representing a significant practical limitation given the high prevalence of orthodontic intervention in industrialized populations. Fourth, AMY1 copy number testing, while increasingly affordable, remains unavailable or cost-prohibitive in many clinical contexts. Ongoing reduction in genomic testing costs is expected to address this limitation progressively.

7. Conclusion

The Differential Ancestral Adaptation Hypothesis proposes a biologically grounded, empirically testable framework for understanding individual variance in dietary carbohydrate metabolism. It synthesizes convergent evidence from multiple independent scientific domains — paleoanthropology, population genetics, gastroenterology, and metabolic epidemiology — into a coherent theoretical model that generates specific, falsifiable predictions.

The central claim — that the duration of ancestral agricultural exposure is a primary determinant of individual carbohydrate metabolism capacity, reflected in measurable biological markers — is consistent with established evolutionary biology and does not require novel mechanisms. It requires only the recognition that evolutionary adaptation is incomplete, heterogeneous across populations, and detectable through existing and emerging measurement tools.

If validated, DAAH would provide the foundation for a paradigm shift in nutritional medicine: from population-averaged dietary guidelines to individually calibrated dietary prescriptions grounded in biological ancestry. The hypothesis invites empirical challenge and welcomes collaborative investigation.

Appendix A — DAAH Response Monitoring Log (14-Day Protocol)

The following log is completed twice daily — immediately after the first substantial meal (Morning Entry) and in the evening before sleep (Evening Entry). All ratings use the specified scales. Entries should be completed within 30 minutes of the relevant time point to minimise recall bias. Days 1–7 constitute Phase A (protein-fat dominant, below 20g carbohydrate daily). Days 8–14 constitute Phase B (controlled carbohydrate reintroduction, 150–250g carbohydrate from whole-food sources only).

PHASE A — Days 1 to 7: Eat freely from beef, lamb, pork, poultry, fish, seafood, eggs, butter, tallow, lard, hard cheese. No grains, legumes, fruit, starchy vegetables, or sweeteners. No caloric restriction — eat until fully satisfied. Water, black coffee, and plain tea permitted.

PHASE B — Days 8 to 14: Maintain protein intake from Phase A. Add whole-food carbohydrates: white rice, oats, potatoes, sweet potatoes, lentils, whole fruit. Target 150–250g carbohydrate per day. Avoid refined sugar, white flour, bread, pasta, pastries, and all processed foods throughout both phases.

Days 1, 4, and 7 (Phase A) and Days 8, 11, and 14 (Phase B) are shown as representative examples. A full printed protocol provides one page per day for all 14 days.

DAY 1 — Phase A — Protein-Fat Baseline Date: _____________

MORNING ENTRY

Foods eaten: (list all foods and approximate quantities) .................................................................

Meal time: (e.g. 08:30) .................................................................

Hunger on waking: 1=none at all 10=extremely hungry 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Energy on waking: 1=exhausted 10=fully alert 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Mental clarity on waking: 1=foggy 10=sharp and clear 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Energy at 60 min post-meal: 1=crashed 10=stable and energised 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Mental clarity at 60 min post-meal: 1=brain fog 10=fully clear 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Satiety duration: (time until next hunger — e.g. '4 hours' or 'still not hungry at dinner') .................................................................

Morning — Gastrointestinal Assessment

Bowel movement occurred: NO / MILD / YES

Estimated transit time: (from eating to elimination — e.g. under 12h / 12–24h / over 24h) .................................................................

Stool consistency (Bristol Scale — 1=hard lumps 4=normal 7=watery) 1 — 2 — 3 — 4 — 5 — 6 — 7

Stool volume: (vs. your personal norm — MUCH LESS / LESS / NORMAL / MORE / MUCH MORE) .................................................................

Time to complete (minutes): (from sitting down to finish) .................................................................

Straining required: NO / MILD / YES

Bloating: NO / MILD / YES

Excessive flatulence: NO / MILD / YES

Abdominal cramping or pain: NO / MILD / YES

Urgency or unpredictability: NO / MILD / YES

EVENING ENTRY

Evening meal: (foods and approximate quantities) .................................................................

Overall energy today: average across full day 1=very low 10=high and stable 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Overall mood today: 1=irritable/low 10=stable/positive 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Physical motivation: 1=lethargic 10=strong and motivated 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Sleep quality last night: 1=very poor 10=deep and restorative 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Cravings intensity today: 1=none 10=intense, hard to resist 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Craving type: (e.g. sweet / salty / bread / meat / none) .................................................................

Overall digestive comfort today: 1=significant discomfort all day 10=no symptoms, effortless 1 — 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 — 10

Total bowel movements today: (number) .................................................................

Any GI symptoms today: NO / MILD / YES

Notes

DAY 4 — Phase A — Protein-Fat Baseline Date: _____________