Duality#
Heredity plays a surprisingly minor role in determining lifespan, contributing a mere 2% to the overall variation in mortality risk. Despite the scientific fascination with genomics and the promises of precision medicine, the reality is that an individual’s genetic makeup exerts only a marginal influence on their longevity. While certain inherited traits can predispose individuals to specific diseases, their actual impact is dwarfed by the sheer magnitude of environmental and random factors that shape human aging. The obsession with genetic determinism often overshadows the reality that genes are not the primary architects of fate; they are merely one factor in an intricate system of influences.
Fig. 16 Some Key Issues. Let’s treasure it, nourish it, Nourish It, move it, celebrate it.#
The exposome, a term encompassing all non-genetic environmental exposures throughout life, has a significantly greater impact on mortality risk, accounting for approximately 17% of the variation. This includes lifestyle choices such as diet, physical activity, exposure to pollutants, socioeconomic status, and even the psychosocial environment. Unlike the fixed nature of genes, these factors are modifiable, which suggests that individuals and societies have substantial agency in shaping health outcomes. Yet, even within this domain, there are limits—one cannot fully escape the social and environmental conditions into which they are born. Nonetheless, policies promoting cleaner air, better nutrition, and equitable healthcare access have far greater potential to extend life expectancy than genetic interventions.
The most unsettling revelation from the data, however, is that randomness dominates the equation. A staggering 81% of mortality risk is attributed to what can only be described as bad luck—unpredictable, unexplained forces that operate beyond individual control. This includes stochastic cellular mutations, accidents, unforeseen medical complications, and idiosyncratic disease manifestations that defy prediction. While people are eager to ascribe causality to personal habits or genetic predispositions, the brutal truth is that life is largely dictated by chaos. The discomforting implication is that no amount of biohacking, medical surveillance, or genetic engineering will confer absolute control over fate. Mortality remains, to a significant extent, a cosmic roll of the dice.
Fig. 17 Frailty Phenotype. Fried et al in their landmark study reported that comorbidity is an etiological risk factor for frailty, which in turn has disability as an outcome.#
If one seeks to optimize longevity, the data suggest that investments should be made wisely. Chasing genetic modifications for lifespan extension is an exercise in diminishing returns, as the genome’s contribution is negligible. Lifestyle interventions, while important, offer only incremental benefits. The true wild card remains fortune, an element immune to human intervention. Understanding this reality should lead to a radical reassessment of priorities: rather than obsessing over marginal gains in longevity, perhaps the focus should shift toward maximizing the quality of the finite years available. A life well-lived, rich in meaning and experience, is the only real hedge against the whims of fate.
Show code cell source
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
# Define the neural network layers
def define_layers():
return {
'Suis': ['Foundational', 'Grammar', 'Nourish It', 'Know It', "Move It", 'Injure It'], # Static
'Voir': ['Gate-Nutrition'],
'Choisis': ['Prioritize-Lifestyle', 'Basal Metabolic Rate'],
'Deviens': ['Unstructured-Intense', 'Weekly-Calendar', 'Refine-Training'],
"M'èléve": ['NexToken Prediction', 'Hydration', 'Fat-Muscle Ratio', 'Visceral-Fat', 'Existential Cadence']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['Gate-Nutrition'],
'paleturquoise': ['Injure It', 'Basal Metabolic Rate', 'Refine-Training', 'Existential Cadence'],
'lightgreen': ["Move It", 'Weekly-Calendar', 'Hydration', 'Visceral-Fat', 'Fat-Muscle Ratio'],
'lightsalmon': ['Nourish It', 'Know It', 'Prioritize-Lifestyle', 'Unstructured-Intense', 'NexToken Prediction'],
}
return {node: color for color, nodes in color_map.items() for node in nodes}
# Define edge weights (hardcoded for editing)
def define_edges():
return {
('Foundational', 'Gate-Nutrition'): '1/99',
('Grammar', 'Gate-Nutrition'): '5/95',
('Nourish It', 'Gate-Nutrition'): '20/80',
('Know It', 'Gate-Nutrition'): '51/49',
("Move It", 'Gate-Nutrition'): '80/20',
('Injure It', 'Gate-Nutrition'): '95/5',
('Gate-Nutrition', 'Prioritize-Lifestyle'): '20/80',
('Gate-Nutrition', 'Basal Metabolic Rate'): '80/20',
('Prioritize-Lifestyle', 'Unstructured-Intense'): '49/51',
('Prioritize-Lifestyle', 'Weekly-Calendar'): '80/20',
('Prioritize-Lifestyle', 'Refine-Training'): '95/5',
('Basal Metabolic Rate', 'Unstructured-Intense'): '5/95',
('Basal Metabolic Rate', 'Weekly-Calendar'): '20/80',
('Basal Metabolic Rate', 'Refine-Training'): '51/49',
('Unstructured-Intense', 'NexToken Prediction'): '80/20',
('Unstructured-Intense', 'Hydration'): '85/15',
('Unstructured-Intense', 'Fat-Muscle Ratio'): '90/10',
('Unstructured-Intense', 'Visceral-Fat'): '95/5',
('Unstructured-Intense', 'Existential Cadence'): '99/1',
('Weekly-Calendar', 'NexToken Prediction'): '1/9',
('Weekly-Calendar', 'Hydration'): '1/8',
('Weekly-Calendar', 'Fat-Muscle Ratio'): '1/7',
('Weekly-Calendar', 'Visceral-Fat'): '1/6',
('Weekly-Calendar', 'Existential Cadence'): '1/5',
('Refine-Training', 'NexToken Prediction'): '1/99',
('Refine-Training', 'Hydration'): '5/95',
('Refine-Training', 'Fat-Muscle Ratio'): '10/90',
('Refine-Training', 'Visceral-Fat'): '15/85',
('Refine-Training', 'Existential Cadence'): '20/80'
}
# Calculate positions for nodes
def calculate_positions(layer, x_offset):
y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
return [(x_offset, y) for y in y_positions]
# Create and visualize the neural network graph
def visualize_nn():
layers = define_layers()
colors = assign_colors()
edges = define_edges()
G = nx.DiGraph()
pos = {}
node_colors = []
# Create mapping from original node names to numbered labels
mapping = {}
counter = 1
for layer in layers.values():
for node in layer:
mapping[node] = f"{counter}. {node}"
counter += 1
# Add nodes with new numbered labels and assign positions
for i, (layer_name, nodes) in enumerate(layers.items()):
positions = calculate_positions(nodes, x_offset=i * 2)
for node, position in zip(nodes, positions):
new_node = mapping[node]
G.add_node(new_node, layer=layer_name)
pos[new_node] = position
node_colors.append(colors.get(node, 'lightgray'))
# Add edges with updated node labels
for (source, target), weight in edges.items():
if source in mapping and target in mapping:
new_source = mapping[source]
new_target = mapping[target]
G.add_edge(new_source, new_target, weight=weight)
# Draw the graph
plt.figure(figsize=(12, 8))
edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
nx.draw(
G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
plt.title("OPRAH™: Heredity, Lifestyle, Badluck", fontsize=25)
plt.show()
# Run the visualization
visualize_nn()
Fig. 18 It’s a known fact in the physical world that opportunity is discovered and solidified through loss, trial, and error. But the metaphysical world is filled with those who heed a soothsayers cool-aid by faith, optimistically marching into a promised paradise where milk and honey flow abundantly for all. This is human history in a nutshell. Meanwhile, heredity (2%), lifestyle (17%), and badluck (81%) are the ways to frame your optimism and pessimism in life and science.#