The Trillion-Dollar Longevity Gap: Why Modern Medicine is Failing Aging Societies

Modern medicine has extended lifespan—but not healthspan. Across aging societies, patients are living longer with overlapping chronic conditions, while healthcare systems remain structured to treat diseases in isolation. This mismatch has created a widening gap between how disease actually develops and how it is managed in practice.

As the global population ages, humanity’s primary health threat is shifting from acute infections to a long-term battle against chronic non-communicable diseases. Taiwan officially entered the ranks of a “super-aged society” in 2025—a designation defined by the World Health Organization as a population in which more than 20% of citizens are aged 65 or older. Within this context, cancer, metabolic disorders, and degenerative diseases are no longer episodic events, but long-duration system failures that progress silently over years—often decades—before clinical intervention begins, placing sustained pressure on both healthcare systems and public financing.

A new framework is beginning to challenge this fragmented model. In 2023, the American Heart Association introduced Cardiovascular-Kidney-Metabolic (CKM) syndrome, redefining obesity, diabetes, kidney disease, and cardiovascular conditions not as separate disease entities, but as interconnected components of a continuous physiological progression. 

“We are not dealing with isolated diseases,” said Professor Ming-Shi Shiao, Chief Research Officer and Consultant at KimForest Enterprise Co., Ltd. “We are dealing with a system that has been breaking down for years—often before we choose to measure it.”

This reframing marks the starting point of a broader shift—from fragmented care toward system-level medicine.

From Organ-Based Medicine to System-Level Disease

For decades, clinical practice has been organized around organ-specific specialties—cardiology for hypertension, endocrinology for diabetes, and nephrology for kidney disease. While this structure has enabled deep specialization, it has also reinforced a fragmented view of disease that does not reflect underlying biology. As Shiao noted, “Human disease does not progress according to hospital departments—it evolves as a system.” This insight sits at the core of the CKM framework, which reframes disease as a dynamic, interconnected process rather than a collection of isolated conditions.

Earlier models such as metabolic syndrome attempted to bridge this gap by linking risk factors across five indicators—waist circumference, blood pressure, fasting glucose, triglycerides, and HDL cholesterol—requiring at least three abnormalities for diagnosis. However, these criteria often lacked mechanistic cohesion and failed to account for population-specific variability, particularly across ethnic groups. By contrast, CKM syndrome establishes a more integrated clinical narrative, defining disease progression as a continuous trajectory that begins with metabolic dysfunction—such as insulin resistance, obesity, and dyslipidemia—and advances through chronic kidney impairment before culminating in cardiovascular events, including heart failure.

“The endpoint may be heart failure,” Shiao explained, “but the origin often lies much earlier—in insulin resistance, lipid imbalance, and gradual kidney decline.” This shift in perspective fundamentally alters the goals of medical intervention. Rather than focusing on normalizing individual biomarkers in isolation, clinicians are increasingly tasked with preserving overall system integrity, maintaining multi-organ function, and intervening early enough to prevent irreversible damage.

The inclusion of kidney function as a central component of this trajectory carries particular clinical significance in Asian populations. Compared to Western cohorts, where type 2 diabetes often progresses directly to cardiovascular disease, patients in Asia—including Taiwan—frequently develop diabetic kidney disease at an earlier stage. “In Asian populations, kidney involvement often appears earlier,” Shiao noted. “If we monitor that stage carefully, we may prevent cardiovascular events before they occur.” This highlights the importance of redefining early detection strategies within region-specific clinical contexts.

The Limits of Traditional Biomarkers

The CKM framework not only reshapes disease classification but also challenges the validity of long-standing diagnostic markers, particularly blood glucose as an early indicator of metabolic disease. For decades, diabetes has been defined using thresholds such as fasting glucose ≥126 mg/dL or HbA1c ≥6.5%. However, according to Shiao, these markers emerge too late in the disease process to function effectively as early warning signals. “Blood glucose is not an early warning signal,” he said. “By the time it rises, the system has already been under stress for years.”

This delay is largely due to compensatory physiological mechanisms. In the early stages of insulin resistance, cells become less responsive to insulin, leading to elevated circulating glucose. In response, pancreatic β-cells increase insulin secretion to maintain normal glucose levels, effectively masking metabolic dysfunction. As a result, patients may appear metabolically normal for years, even as underlying pathology progresses. By the time glucose levels exceed diagnostic thresholds, substantial systemic damage may already have occurred.

In contrast, earlier signals of metabolic imbalance can be detected through lipid and amino acid metabolism. Elevated triglycerides and reduced HDL cholesterol often precede glucose dysregulation, reflecting early adipose tissue dysfunction and insulin resistance. Under updated CKM criteria, triglyceride levels above 135 mg/dL are already considered a significant risk threshold—lower than traditional metabolic syndrome cutoffs. “Triglycerides run through the entire disease trajectory,” Shiao said. “From early insulin resistance to atherosclerosis and cardiovascular outcomes—they are a continuous signal.”

In addition, metabolomics research has identified branched-chain amino acids (BCAAs)—including leucine, isoleucine, and valine—as early biomarkers of metabolic disruption. As insulin sensitivity declines, muscle protein breakdown increases while synthesis decreases, leading to elevated circulating levels of BCAAs and aromatic amino acids. “These metabolites rise before glucose does,” Shiao explained. “That makes them powerful early indicators—if we choose to measure them.” When combined, these metabolic signatures can predict progression toward type 2 diabetes well before conventional clinical markers become abnormal.

Metabolomics as the Final Layer of Precision Medicine

Addressing these diagnostic limitations requires a broader shift toward multiomics integration, with metabolomics playing a central role. While genomics provides insight into disease susceptibility, it does not capture the dynamic influence of environmental exposure, aging, and lifestyle. “Genomics tells you what might happen,” Shiao said. “Metabolomics tells you what is happening now.” As the most downstream layer of biological regulation, metabolomics reflects real-time physiological states and offers a more immediate window into disease progression.

Within this field, two primary approaches exist: untargeted metabolomics, which scans broadly to generate hypotheses, and targeted metabolomics, which focuses on quantifying predefined metabolites for clinical use. Although untargeted approaches are invaluable in research settings, their complexity and lack of standardization limit their clinical applicability. “In research, untargeted metabolomics is essential,” Shiao noted. “But in clinical practice, we need clarity, reproducibility, and actionability.”

KimForest has therefore prioritized targeted metabolomics, leveraging technologies such as liquid chromatography–mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) to precisely identify and quantify key metabolites in blood and urine. Rather than relying on individual markers, this approach constructs multi-dimensional biomarker portfolios, capturing metabolic pathways, regulatory networks, and disease-associated biochemical changes. “It’s not one biomarker,” Shiao emphasized. “It’s a pattern—a network that reflects the biology of the disease.”

These integrated profiles enable clinicians to track disease trajectories rather than static endpoints, incorporating markers such as BCAAs (linked to insulin resistance and muscle metabolism), tryptophan metabolites (associated with chronic inflammation), and uremic toxins like indoxyl sulfate (indicative of kidney fibrosis risk). “These markers allow us to see the trajectory,” he said. “Not just the endpoint.”

Building Population-Specific Data for Precision Prevention

Translating precision medicine into clinical practice depends not only on measurement technologies, but also on the quality and relevance of underlying data. “Precision medicine is only as precise as the data behind it,” Shiao noted. Many global datasets are derived from Western populations and may not accurately reflect disease patterns in Asian populations, leading to potential misclassification of risk and delayed intervention.

“If you apply Western data directly to Asian populations, you risk misjudging both disease progression and intervention timing,” he said. To address this gap, KimForest is developing a Taiwan-specific metabolomics database, stratified across age groups, genders, disease stages, and metabolic profiles. This includes cohorts ranging from middle-aged adults (45–64 years) to elderly (65–85 years) and super-aged populations over 85, enabling detailed mapping of metabolic trajectories across the lifespan.

By integrating individual metabolite data—such as BCAAs and triglycerides—into this localized reference framework, clinicians can more accurately position patients along the CKM continuum, from early risk stages (0–2) to advanced clinical disease (3–4). “This allows us to locate where a patient truly is—between health and disease,” Shiao said. “And act accordingly.” For patients identified at higher risk, early intervention strategies may include not only lifestyle modification but also pharmacological options such as SGLT2 inhibitors or GLP-1 receptor agonists to protect cardiovascular and renal function.

From Precision Medicine to Precision Nutrition

Beyond diagnostics and pharmacological intervention, the integration of metabolomics and population data is also enabling the emergence of precision nutrition. This approach shifts the focus toward individualized lifestyle interventions during the early, asymptomatic stages of disease progression. “Intervention does not always have to begin with drugs,” Shiao said. “In early stages, lifestyle and nutrition can be highly effective—if guided correctly.”

By identifying metabolic imbalances before overt disease develops, clinicians can design targeted dietary and exercise interventions tailored to an individual’s metabolic profile. This not only reduces reliance on late-stage medical treatment but also aligns with a broader preventive healthcare strategy aimed at addressing disease during the “gray zone” between health and pathology, where intervention is most effective.

From Reactive Treatment to Proactive Health Systems

Despite increasing life expectancy, healthy lifespan has not kept pace. In Taiwan, individuals often spend seven to eight years in poor health in later life, largely due to chronic disease burden. However, as Shiao emphasized, these conditions do not emerge suddenly. “These conditions develop over many years,” he said. “Which means they also offer a long window for prevention.”

With advances in targeted metabolomics and localized health data, healthcare systems are now positioned to transition toward P4 medicine—predictive, preventive, personalized, and participatory care. This model shifts the focus from reactive treatment to proactive health management, aiming to identify and intervene in disease processes before clinical symptoms arise. “The goal is no longer to treat disease at the endpoint,” Shiao said. “It is to intercept the trajectory.”

Toward Whole-Body Health and Healthy Longevity

KimForest’s long-term strategy reflects this paradigm shift. Rather than focusing solely on disease detection and treatment, the company is positioning itself within a broader framework of whole-body health management, particularly for aging populations. “Health is not about a single organ,” Shiao said. “It is about maintaining the function of the whole system.”

Maintaining integrated organ function is increasingly recognized as essential for extending not only lifespan but also healthspan—the number of years lived in good health. By combining targeted metabolomics, population-specific data, and early intervention strategies, this approach aims to reduce the burden of chronic disease and improve quality of life in later years.

“Our ultimate goal is healthy longevity,” Shiao concluded. “Not just living longer—but living well.”

Author

Bernice Lottering