Category: 8. Health

The rapid adoption of GLP-1 receptor agonists has fundamentally altered obesity management, with these medications now prescribed to millions of patients seeking significant weight loss. Yet a substantial number of physicians continue to prioritize lifestyle interventions as their primary approach, even as professional guidelines increasingly emphasize pharmacologic options and patients arrive with specific medication requests.

What drives this clinical philosophy in an era where injectable medications promise double-digit weight loss? The answer lies not in resistance to innovation but in a nuanced understanding of what produces lasting results in real-world practice.

Guidelines Support Combination Therapy, Not Medication Replacement

Current medical guidance reflects the growing evidence base for antiobesity medications while maintaining emphasis on behavioral foundations. As noted in the National Institutes of Health’s Endotext chapter on obesity pharmacotherapy, current guidelines recommend that individuals who have attempted lifestyle improvements and continue to have a BMI of ≥ 30 or ≥ 27 with an obesity-related comorbidity may be eligible for weight-loss medication treatment.

The guidance emphasizes that antiobesity medications “are indicated in combination with lifestyle modification for the management of overweight and obesity,” similar to approaches used for other chronic diseases.

These guidelines represent a significant evolution from previous recommendations that positioned medications as last-resort options. However, they consistently emphasize pharmacotherapy as an adjunct to, rather than a replacement for, structured behavioral interventions. This distinction proves crucial for physicians who maintain lifestyle-first approaches. They’re not ignoring current guidance but interpreting it through the lens of clinical experience and patient outcomes.

Real-World Data Reinforces Lifestyle Foundations

Real-world outcomes highlight the limitations of medication without sustained adherence. This may help explain why some clinicians continue to lead with lifestyle interventions.

A Cleveland Clinic study of 7881 patients with obesity published in the journal Obesity revealed significant gaps between clinical trial efficacy and everyday practice outcomes. More than 50% of patients discontinued GLP-1 medications within 1 year — 20% within 3 months and 32% between 3 and 12 months. Additionally, more than 80% remained on subtherapeutic maintenance dosages.

The weight-loss results varied dramatically based on adherence and dosing. Patients who discontinued early achieved only 3.6% weight loss, while those who discontinued late lost 6.8%. Patients who continued treatment lost 11.9% on average, but those who both continued treatment and achieved high maintenance dosing lost 13.7% with semaglutide and 18.0% with tirzepatide — results approaching clinical trial outcomes.

Dexter Shurney, MD, MPH, MBA, chief medical officer at ModifyHealth, sees these data as validation of his approach: “The majority of common chronic conditions — hypertension, [congestive heart failure] CHF, hyperlipidemia, diabetes, depression, and obesity — are fundamentally lifestyle issues. Therefore, a lifestyle-first approach to care makes perfect sense because it addresses root cause.”

Clinical Philosophy Rooted in Sustainability

For physicians committed to lifestyle-first care, the approach stems from observed patient outcomes rather than theoretical preferences. Kenji Kaye, MD, a board-certified internist and concierge physician with South Denver Concierge in Denver, explains: “Without foundational lifestyle changes, medications and surgery are destined to fail. We have seen many patients not lose weight or even gain weight despite max dosages of these pharmaceuticals.”

This perspective is informed by understanding obesity as a multifactorial condition requiring comprehensive intervention. As Kaye notes: “Lifestyle habits, genetics, hormonal state, activity level, and other comorbid conditions all contribute to obesity. I like to focus on addressing the variables that will have the biggest impact while evaluating for underlying contributing medical conditions.”

The sustainability argument extends beyond weight loss to broader health outcomes. Shurney emphasizes the systemic benefits of lifestyle interventions: “Lifestyle medicine has a much broader clinical application than a single medication or surgical intervention, which are typically designed to treat one condition at a time and come with multiple side effects. Lifestyle interventions work well to effectively avoid the polypharmacy issues that many patients often face.”

He cites dramatic results achievable with intensive lifestyle programs: “When starting a patient on a rigorous lifestyle medicine program for type 2 diabetes, it is often necessary to reduce their insulin dose by half within days to avoid hypoglycemia. I have routinely seen average drops in cholesterol of 20%-50% within 7-8 weeks.”

Strategic Medication Use Within Lifestyle Framework

Even among physicians who lead with lifestyle-based care, some incorporate GLP-1 receptor agonists as part of a broader treatment plan that includes behavior change. Elizabeth Slauter, MD, a board-certified family medicine and obesity medicine physician who practices at a direct primary care clinic in Boerne, Texas, explains her approach: “Studies consistently show that the best outcomes with obesity medications occur when they are combined with lifestyle changes. So, it makes sense to start with lifestyle interventions as a foundational approach.”

The decision to add medications often hinges on practical considerations. Cost remains a significant barrier, with many patients unable to afford long-term treatment. Slauter frequently encounters this challenge: “Many people cannot afford the cost of medications, especially long term — and research shows that these medications are often needed long-term to maintain results,” she said.

Insurance coverage inconsistencies and prior authorization requirements create additional barriers. The Cleveland Clinic study identified cost and insurance coverage as primary reasons for treatment discontinuation, alongside side effects and medication shortages.

For these physicians, medications serve as tools within a comprehensive framework rather than standalone solutions. Kaye describes his typical process: “My usual practice is to discuss these medications as an option but only after a careful review of their food choices, activity level, health history, and current medications.”

Navigating Patient Expectations and Media Influence

The widespread media coverage of GLP-1 receptor agonists has created new clinical challenges. Patients increasingly present with specific medication requests, often based on social media testimonials or celebrity endorsements rather than clinical assessments.

Kaye addresses this directly: “Medications like GLP-1s are mentioned almost everywhere including the media, pharmaceutical ads, and celebrity gossip. When a patient presents asking for a prescription, it is a perfect opportunity to really delve into the details of what these medications can offer and also the risks involved.”

Setting realistic expectations becomes crucial, Slauter said. “One issue I run into frequently is that patients expect to be on weight-loss medication for a short term, and this is not always reasonable,” she said. This expectation management is particularly important given the Cleveland Clinic data showing that discontinuation leads to reduced effectiveness.

The educational approach allows physicians to address misconceptions while maintaining therapeutic relationships, Kaye said. “Most of the time patients welcome an open discourse about options and strategies to achieve their goals,” he said.

Systemic Pressures and Professional Conviction

Healthcare systems increasingly favor interventions that produce rapid, measurable outcomes, creating pressure to prescribe medications over time-intensive lifestyle counseling. Reimbursement structures often inadequately compensate for the extended counseling sessions required for effective lifestyle interventions.

Shurney identifies this as a fundamental barrier: “The lack of reimbursement parity for lifestyle interventions is a disincentive to practice this way,” he said. “It’s much easier to prescribe a medication and receive the ‘quality prize’ for checking the drug adherence box than to prescribe lifestyle and not receive a similar financial reward.”

Some physicians have modified their practice models to maintain their clinical philosophy. “I joined a direct primary care specifically to have the time to counsel my patients on this,” Slauter said. “A traditional insurance-based practice did not offer the time needed for this.”

Long-Term Perspective Drives Clinical Decisions

What ultimately sustains these physicians’ commitment to lifestyle-first care is their long-term perspective on patient outcomes, Kaye said. “After seeing many patients start down the pathway of pharmaceuticals and ultimately not reaching their goals reaffirmed my commitment to a more holistic approach,” he said. “In my experience, without a strong foundation of lifestyle changes, the long-term success rate is low even with antiobesity medications.”

This perspective is reinforced by concerns about healthcare sustainability. Shurney warns: “What we risk are ever-higher healthcare costs, since these medications are very expensive and need to be taken for years, if not forever, to sustain the weight loss. Additionally, we still do not know the long-term effects of these medications.”

Apriori to leverage foundational insights from the Crick’s Legacy Study to further validate Octavia™ platform’s ability to predict viral evolution and vaccine performance

CAMBRIDGE, Mass., Aug. 12, 2025 /PRNewswire/ — Apriori Bio, a biotechnology company focused on developing variant-resilient vaccines, today announced a research collaboration with the Francis Crick Institute to better understand critical aspects of immune response, with the goal of informing the development of more predictive and effective vaccines against present and emerging viral threats for patient benefit.

“We are delighted to collaborate with the Francis Crick Institute,” said Craig Williams, MBA, Chief Executive Officer of Apriori Bio and CEO-Partner at Flagship Pioneering. “This collaboration will enhance our capacity to accurately predict viral evolution and develop innovative, prospective vaccine candidates. Together, we aim to enhance the global seasonal strain selection framework and improve vaccine effectiveness for individuals of all ages, immune histories, and geographic locations.”

The collaboration will leverage output from the Crick’s Legacy Study to further validate Apriori’s biology-informed artificial intelligence platform, Octavia™, to both predict viral evolution and design vaccines that elicit the optimal immune responses against present and emerging viral threats. Legacy is a long-term research initiative between the Crick and the National Institute for Health and Care Research UCLH Biomedical Research Centre, designed to provide insights into immune responses to COVID vaccines and infections.

“The Legacy Study is an unparalleled resource for understanding how viruses evolve,” said Dr. Emma Wall, a Lead Investigator for the Legacy Study at the Francis Crick Institute. “Together, we have an opportunity to translate meaningful insights that can be used to enhance vaccine design and safeguard communities worldwide by staying ahead of emerging health threats.”

This public and private sector collaboration is the latest to be facilitated through Flagship Pioneering’s UK initiative. Launched in 2023, the initiative serves as a bridge between the UK’s rich research and life science networks and Flagship and its companies.

“This collaboration with the Crick, one of the world-leading scientific institutes, underscores Flagship Pioneering’s dedication to leveraging world-class science to accelerate innovation across our portfolio companies,” said Junaid Bajwa, M.D., Senior Partner and Head of the United Kingdom for Flagship Pioneering. “The integration of the Crick’s Legacy Study and Apriori’s Octavia platform demonstrates the immense potential to prepare for and address future health challenges.”

About Apriori Bio
Apriori Bio is developing variant-resilient vaccines to better protect human health. Our pioneering approach centers on a unique technology platform, Octavia™. The platform allows us to survey the entire landscape of existing and potential viral variants to design new vaccines that elicit ideal immune responses against present and emerging health challenges. Apriori was founded in 2020 in Flagship Labs, a unit of Flagship Pioneering. For more information, visit www.aprioribio.com or follow us on LinkedIn and X.

About The Francis Crick Institute
The Francis Crick Institute is a biomedical discovery institute with the mission of understanding the fundamental biology underlying health and disease. Its work helps improve our understanding of why disease develops which promotes discoveries into new ways to prevent, diagnose and treat disease.

An independent organisation, its founding partners are the Medical Research Council (MRC), Cancer Research UK, Wellcome, UCL (University College London), Imperial College London and King’s College London.

The Crick was formed in 2015, and in 2016 it moved into a brand new state-of-the-art building in central London which brings together 1500 scientists and support staff working collaboratively across disciplines, making it the biggest biomedical research facility under a single roof in Europe. For more information, please visit http://crick.ac.uk/

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Introduction

As the global population ages, dementia, including Alzheimer’s disease (AD), have become significant and growing global public health threats. AD is the most prevalent type of dementia, representing around 60–70% of all diagnosed cases.¹ According to the findings of Global Burden of Disease (GBD) research,² the global population of individuals with dementia was estimated at 57.4 (95% UI=50.4–65.1) million in 2019. By 2030, this figure is projected to increase to 83.2 (95% UI=73.0–94.6) million, making dementia the seventh leading cause of death worldwide, following lung cancer.³ Senile dementia, the most prevalent form, is typically observed in people aged 65 years and older. However, it can also occur in person younger the age of 65, known as presenile dementia, which highlights a concerning trend of dementia affecting younger populations. Importantly, individuals with presenile dementia are frequently in the height of their professional careers, managing substantial family, work, and social domains. Research further suggests that, compared to late-onset dementia, presenile dementia is linked to a faster deterioration in cognitive function and places a substantially heavier psychological burden on caregivers.^4,5 Appreciating the global burden of presenile dementia is crucial for the allocation of appropriate healthcare resources across diverse regions.

A systematic evaluation and meta-analysis encompassing 95 studies demonstrated that the global age-standardised prevalence of presenile dementia (30–64 years) was 119.0 per 100,000 individuals.⁶ A further systematic evaluation and meta-analysis,⁷ comprising 61 articles, revealed an escalation in the global age-standardised prevalence from 0.17/100,000 for ages 30–34 to 5.14/100,000 for ages 60–64, with an overall global age-standardised prevalence of 11/100,000 for ages 30–64 years. However, the majority of these aggregated data were derived from Europe and North America, with limited representation from Africa. This lack of data from low-income countries may fail to accurately reflect the true impact of presenile dementia in these regions. The absence of data from low-income nations may not adequately represent the actual impact of presenile dementia in these areas.

To date, inadequate studies have utilized data from the GBD study to comprehensively forecast the trends in age-standardized incidence, death, and DALYs rates of presenile dementia in the ten countries expected to experience the highest increases. Gaining insights into these projections is essential for informing public health strategies and optimizing resource distribution. To bridge this gap, our study sought to estimate the future burden of presenile dementia by forecasting the anticipated cases, deaths, and DALYs, together with their age-standardized rates, for the period from 2020 to 2030. We employed a Bayesian age-period-cohort model, leveraging GBD data between 1990 and 2019 to ensure the accuracy and reliability of our predictions.

Given the increased recognition of presenile dementia as a public health problem, we built on earlier research by combining trends from high-income nations with limited data from low- and middle-income regions. This method emphasizes the need for more representative and regionally varied data to accurately reflect the worldwide burden of presenile dementia, particularly in underserved and low-resource areas. The findings of epidemiological study will help to offer insight a more nuanced knowledge of presenile dementia trends and to promote targeted solutions in high-risk populations.

Materials and Methods

Study Design and Data Source

This cross-sectional, population-based research utilized data from GBD study, which offers extensive and detailed estimates of disease and injury burdens across 204 countries and territories worldwide, covering the period from 1990 to 2019. The dataset integrates information from several different sources, including hospital and clinical records, vital registration systems, disease registries, household surveys, census data, and published studies. Data were retrieved through the GBD Results Tool provided by the Institute for Health Metrics and Evaluation (IHME) at http://ghdx.healthdata.org/ gbd-results-tool. The study was approved by the Ethics Committee of Shenzhen Nanshan Center for Chronic Disease Control (ll20240017).

Disease Metrics and Definition

GBD utilized case definitions derived from the Diagnostic and Statistical Manual of Mental Disorders (DSM-III, DSM-IV, or DSM V), which are predominantly employed in surveys and cohort studies, as well as from the International Classification of Diseases (ICD-8, ICD-9, and ICD-10), which are utilized in vital registration and claims data sources. Dementia was characterized as a “progressive, degenerative, and chronic neurological disorder distinguished by cognitive dysfunctions that interfere with activities of daily living.” In the GBD framework, Alzheimer’s disease and other dementias were identified according to specific diagnostic codes: ICD-10 codes F00–F03, G30, and G31, as well as ICD-9 codes 290, 2901.2, 291.8, 294, and 331. This study focuses on presenile dementia, a condition characterized by the onset of dementia under the age of 65 and is also known as dementia with youth onset.⁸

Key indicators included in the analyses include prevalence, incidence, mortality, DALYs and influencing factor proportion. All indicators are disaggregated by age group, gender and year, with the age groups further subdivided into 40–44, 45–49, and five-year intervals up to 64 years. To enable comparisons with different age-structured populations in different regions, age-standardised rates (ASRs) were computed using the standard population defined by the GBD. To account for statistical uncertainty, all estimates were accompanied by 95% uncertainty intervals (UIs), generated through 1000 model simulations.⁹ In addition, the Socio-Demographic Index (SDI) is used by the GBD study to examine potential impact of socio-economic development on the burden of disease. The SDI is a combined measure that incorporates three essential indicators: income per capita adjusted for purchasing power parity, the average years of education among individuals aged 15 years and over, and the fertility rate of women younger than 25 years. Higher scores indicate higher levels of socio-economic development, with the SDI taking the minimum value of 0 and the maximum value of 1. Based on the SDI, nations and regions are grouped into five primary categories: low, low-middle, middle, high-middle, and high SDI.¹⁰

Trend Analysis and Project Trends

Assuming a consistent rate of change on a logarithmic scale over the defined period, the regression model equation below was used to figure out the EAPC and its 95% confidence interval (CI) to describe the temporal trend of the presenile dementia ASR for a given time horizon:¹¹ The equation Y = α + βX + ε is used, where Y represents the logarithmically transformed ASR, X denotes a time variable expressed in years, and ε is a random error factor that represents unexplained fluctuation. β indicates a positive or negative trend in the ASR. β: slope is the average rate of change in ln(ASR) per unit increase in time (X). The regression model’s predicted β is EAPC = (e^β − 1) × 100.¹² If both the EAPC and the lower boundary of the CI are positive, ASR tends to increase. In contrast, when EAPC and the upper limit of CI are negative Conversely, if the EAPC and the upper boundary of the CI are negative, ASR decreases. When none of these requirements are satisfied, the ASR remains reasonably steady. The connections between the EAPC and ASR, as well as the relationship between the SDI and EAPC, were explored using Pearson’s correlation coefficient (ρ) and Gaussian process regression. This method aids in providing a deeper insight into the interrelations among these variables.¹³

Here, burden estimates related to age, period, and birth cohort were forecasted using Bayesian age-period-cohort (BAPC) analysis, employing integrated nested Laplace approximations (INLA). The burden statistics pertaining to age, time (period), and birth cohort were forecasted using BAPC analysis, employing integrated nested Laplace approximations (INLA).¹⁴ Compared to alternative methods like generalized additive, BAPC more effectively captures the intricate relationships among these factors, while INLA, as a rapid approximation method, significantly enhances computational efficiency.¹⁵ Also, Scatter plots were constructed to illustrate the associations between age-standardized rates of incidence, mortality, and DALYs for presenile dementia across various SDI quintiles, offering a comprehensive visualization of how disease burden correlates with socio-economic development levels. The ASR of presenile dementia for specific age groups is derived by multiplying the crude rates, categorized in 5 years intervals, by the GBD 2019 standard population distribution. The summed values for the 40–64 age group help mitigate differences in population age structures, ensuring comparability. For the 95% CI, the BAPC model estimates uncertainty through posterior distributions. ASR is calculated from 500 posterior samples, with the 95% CI established by the 2.5th and 97.5th percentiles, reflecting uncertainty in parameter estimation.

Previously our research team and current published literature have provided a comprehensive overview of GBD research methods, core concepts and basic approaches.¹⁶ Regarding missing data handling, GBD employs sophisticated statistical modeling techniques including spatiotemporal Gaussian process regression and Bayesian approaches to address data gaps and regional variations. All statistical analyses and visualizations were carried out using R 4.1.2 (Lucent Technologies, Jasmine Mountain, USA). P-values < 0.05 were deemed statistically significant.

Results

Prevalence and Trends of Presenile Dementia

The worldwide total number of cases, deaths, and DALYs of presenile dementia in 2019 were 911,600, 55,360, and 2,409,860, respectively. The corresponding of presenile dementia’s age-standardized incidence, death, and DALY rates per 100,000 individuals were 43.30, 2.63, and 114.44, respectively (Table 1). In 1990, there were 410,180 incidence cases of presenile dementia, 27,260 deaths, and 1,163,770 DALYs. The corresponding age-standardized incidence of presenile dementia was 38.57 per 100,000; the age-standardized death rate was 2.56 per 100,000; and the age-standardized DALYs rate was 109.50 per 100,000 (Table S1). Between 1990 and 2019, the global total number of presenile dementia cases showed a sustained increase, rising by 122.24%; the number of deaths also increased by 103.08%; and the total number of DALYs rose by 107.07% (Figure S1). The age-standardized incidence rate showed a growing trend with an EAPC of 0.41 (95% CI: 0.39 to 0.44) (Table 1 and Figure 1A). Similarly, the age-standardized death rate increased, with an EAPC of 0.16 (95% CI: 0.12 to 0.21) (Table 1 and Figure 1B). The age-standardized DALYs rate also demonstrated a rising trend, with an EAPC of 0.21 (95% CI: 0.18 to 0.24) (Table 1 and Figure 1C).

Table 1 Global Burden of Presenile Dementia in 2019 for Individuals Aged 40–64 Years, with Age-Standardized Rates (ASRs) by Sex, Age Groups, SDI Levels, and GBD Regions, and Trends from 1990 to 2030

Figure 1 The trends and projections of age-standardized incidence rate, death rate, and DALYs rate of presenile dementia between 1990 and 2030 at the global level. (A) The age-standardized incidence rate of global; (B) The age-standardized death rate of global; (C) The age-standardized DALYs rate of global.

In terms of gender, women experienced greater age-standardized incidence rates of presenile dementia than men in 2019, as well as higher death rates and DALY rates (Table 1). These indicators of presenile dementia increased between 1990 and 2019 for both males and females (Table 1 and Figure S2).

In 2019, presenile dementia’s incidence, death, and DALY rates grew with age in each of the five age groups (40–64 years, with a 5-year interval per group). The age range of 60–64 had the greatest rates, with the incidence rate of 126.99 per 100,000, the death rate of 9.34 per 100,000, and the DALYs rate of 364.39 per 100,000 (Table 1). These indicators of presenile dementia for each of the five age groups from 1990 to 2019 are shown in Figure S3.

In 2019, age-standardized incidence, death, and DALY rates of presenile dementia were highest in middle-SDI countries and lowest in high-SDI countries (Table 1). A negative association was found by Pearson correlation analysis between the age-standardized incidence (r = −0.13, p < 0.001), death (r = −0.29, p < 0.001), and DALY rates (r = −0.24, p < 0.001) of presenile dementia and SDI in 2019 (Figure S4). Across all SDI nation groups, the age-standardized incidence of presenile dementia increased overall between 1990 and 2019 (Figure S5). In the high-SDI country group, the EAPC for the age-standardized death rate of presenile dementia was −0.15 (95% CI: −0.16 to −0.14), and the EAPC for the DALY rate was −0.06 (95% CI: −0.07 to −0.05), both showing a downward trend. However, both indicators of other SDI country groups showed an upward trend. The high-middle SDI countries experienced the largest increase in the age-standardized death rate, with an EAPC of 0.33 (95% CI: 0.27 to 0.40). Meanwhile, the low-SDI countries saw the largest increase in the age-standardized DALYs rate, with an EAPC of 0.37 (95% CI: 0.33 to 0.40) (Table 1 and Figure S5).

Among the 21 regions categorized by geographical location, Tropical Latin America had the greatest age-standardized incidence rate of presenile dementia at 63.46 per 100,000 in 2019, while Western Europe had the lowest prevalence at 32.63 per 100,000. The age-standardized death and DALY rates were highest in East Asia, at 3.55 per 100,000 individuals and 148.69 per 100,000, respectively. In comparison, Australasia had the lowest age-standardized death and DALY rates at 1.72 per 100,000 and 82.21 per 100,000, respectively (Table 1). During the period from 1990 to 2019, only Australasia, High-income North America, Oceania, and Western Sub-Saharan Africa exhibited a decline in the age-standardized incidence of presenile dementia, while the remaining regions exhibited an upward trend (Table 1 and Figure 2A). Among these, East Asia experienced the substantial growth in the age-standardized incidence of presenile dementia (EAPC, 0.74 [95% CI: 0.69 to 0.79]). Eastern Sub-Saharan Africa experienced the most substantial rise in the age-standardized death rate of presenile dementia (EAPC, 0.50 [95% CI: 0.46 to 0.54]), while Tropical Latin America saw the largest increase in the age-standardized DALYs rate (EAPC, 0.43 [95% CI: 0.29 to 0.56] (Table 1 and Figure 2B, C)).

Figure 2 The global distribution and the estimated annual percentage change(EAPC) in age-standardized incidence, death, and DALY rates of presenile dementia at the two time periods (1990–2019 and 2020–2030). (A) The EAPC of age-standardized incidence rate, 1990–2019; (B) The EAPC of age-standardized death rate, 1990–2019; (C) The EAPC of age-standardized DALYs rate, 1990–2019; (D) The EAPC of age-standardized incidence rate, 2020–2030; (E) The EAPC of age-standardized death rate, 2020–2030; (F) The EAPC of age-standardized DALYs rate, 2020–2030.

Predicted Trends of Presenile Dementia

In 2030, the global age-standardized incidence rate of presenile dementia is predicted to be 43.97 per 100,000. The age-standardized death rate is projected to be 2.61 per 100,000. Additionally, the age-standardized DALYs rate is expected to reach 113.38 per 100,000 (Figure S1). The worldwide age-standardized incidence of presenile dementia is predicted to have an EAPC of 0.07 (95% CI: −0.02 to 0.17), showing a gradual increase; the age-standardized death rate of presenile dementia is predicted to have an EAPC of −0.01 (95% CI: −0.07 to 0.05), and the age-standardized DALY rate is predicted to have an EAPC of −0.05 (95% CI: −0.10 to 0.00), both showing a slight decrease between 2020 and 2030 (Table 1 and Figure 1).

Between 2020 and 2030, the age-standardized incidence rate of presenile dementia in females is predicted to have an EAPC of 0.04 (95% CI: −0.05 to 0.13), showing a slight increase; the age-standardized death rate is predicted to have an EAPC of −0.04 (95% CI: −0.08 to 0.00), and the age-standardized DALYs rate is predicted to have an EAPC of −0.13 (95% CI: −0.17 to −0.08), both showing a downward trend. In contrast, for males, all indicators are predicted to show a small increasing trend, consistent with the data between 1990 to 2019 (Table 1 and Figure S2).

Among the five SDI types of countries, the age-standardized incidence rate of presenile dementia is predicted to have an EAPC of 0.22 (95% CI: 0.11 to 0.33) in high-middle SDI countries, showing the greatest increase; the age-standardized death rate is predicted to have an EAPC of 0.65 (95% CI: 0.56 to 0.73) in low SDI countries, and the age-standardized DALYs rate is predicted to have an EAPC of 0.47 (95% CI: 0.42 to 0.52) in low-middle SDI countries, both showing the greatest increase during the period of 2020 to 2030 (Table 1). Additionally, the age-standardized death and DALY rates of presenile dementia are predicted to increase in low SDI and low-middle SDI countries, while the other three regions are expected to show a decreasing trend (Table 1 and Figure S5).

Among the 21 regions of the world, the age-standardized incidence rate of presenile dementia in Tropical Latin America is expected to see the largest decline (EAPC, −2.52 [95% CI: −2.61 to −2.42]), while East Asia is projected to experience the most significant increase (EAPC, 0.50 [95% CI: 0.38 to 0.63]) between 2020 and 2030 (Table 1 and Figure 2D). During the same period, the age-standardized death and DALY rates of presenile dementia in Central Sub-Saharan Africa is expected to experience the largest increases, with EAPC of 0.98 (95% CI: 0.93 to 1.02) and 0.64 (95% CI: 0.58 to 0.70), respectively (Table 1 and Figure 2E, F).

At the country level, Singapore is projected to experience the largest increase in the age-standardized incidence rate of presenile dementia between 2020 and 2030 (EAPC, 0.66 [95% CI: 0.54 to 0.78]; Table 2 and Figure S6A), followed by Chile (EAPC, 0.61 [95% CI: 0.52 to 0.70]; Table 2 and Figure S6B) and China (EAPC, 0.52 [95% CI: 0.39 to 0.64]; Table 2 and Figure S6C). During the same period, the country predicted to experience the largest decrease in the age-standardized incidence rate of presenile dementia is Brazil (EAPC, −2.59 [95% CI: −2.69 to −2.49]; Table S2). The country with the largest increase in age-standardized death rate for presenile dementia is projected to be Senegal (EAPC, 1.11 [95% CI: 0.34 to 1.87]; Table 2 and Figure S7A), followed by Angola (EAPC, 1.07 [95% CI: 0.99 to 1.15]; Table 2 and Figure S7B) and the Democratic Republic of the Congo (EAPC, 1.03 [95% CI: 0.98 to 1.09]; Table 2 and Figure S7C). Conversely, the country projected to experience the largest decrease is Norway (EAPC, −4.54 [95% CI: −4.64 to −4.44]; Table S2). Similarly, Burkina Faso is predicted to have the largest increase in the age-standardized DALY rate for presenile dementia (EAPC, 1.54 [95% CI: 1.45 to 1.64]; Table 2 and Figure S8A), followed by Niger (EAPC, 0.95 [95% CI: 0.86 to 1.05]; Table 2 and Figure S8B) and Djibouti (EAPC, 0.83 [95% CI: 0.77 to 0.89]; Table 2 and Figure S8C). The country with the largest decrease is also Norway (EAPC, −1.23 [95% CI: −1.33 to −1.13]; Table S2). Figures S6–S8 present the data for the top 10 countries with the largest increases in the three rates of presenile dementia, respectively.

Table 2 Projections and Trends in Age-Standardized Incidence, Death, and DALYs Rates of Presenile Dementia in the Ten Countries Expected to Experience the Highest Increases from 2020 to 2030

Figure 2 and Tables S3–S5 illustrate the changes in the EAPC of all presenile dementia indicators across 204 countries during the two periods, 1990–2019 and 2020–2030, respectively. Globally, the EAPC of the age-standardized incidence of presenile dementia during 2020–2030 were negatively correlated with the trends observed during 1990–2019 (r = −0.27, p < 0.001; Table S6). Among the five SDI types of countries, only the middle SDI countries exhibited a negative correlation (r = −0.41, p = 0.01; Table S6). The global age-standardized death rate of presenile dementia EAPC showed a positive correlation with the trend during 2020–2030 (r = 0.34, p < 0.001; Table S6). Among the five SDI types of countries, this correlation was observed only in low SDI countries (r = 0.58, p < 0.001; Table S6). Similarly, the global age-standardized DALY rates of presenile dementia EAPC showed a positive correlation with the trend during 2020–2030 (r = 0.38, p < 0.001; Table S6). However, among the five SDI types of countries, this correlation was observed only in high-middle SDI countries (r = 0.37, p = 0.01; Table S6).

Risk Factors

The percentage of deaths and DALYs among those with presenile dementia worldwide, stratified by SDI and by 21 regions, that can be attributed to particular risk factors (smoking, high fasting blood glucose, and high body mass index) are shown in Figure 3A and B. The results show that, when compared with the other two risk factors, smoking continues to be the leading cause of deaths and DALYs, both in 1990 and 2019. Compared with 1990, the percentage of deaths and DALYs among patients with presenile dementia resulting from high fasting blood glucose and high body mass index increased in 2019. Among the 21 regions, only East Asia and Eastern Europe saw an increase in smoking-related deaths and DALYs in 2019, while all other regions experienced a decrease.

Figure 3 Proportions of death and DALYs attributable to the specific risk factors (Smoking, High fasting plasma glucose, High body-mass index) for presenile dementia worldwide, 1990 and 2019. (A) The proportions of death attributable to the specific risk factors; (B) The proportions of DALYs attributable to the specific risk factors.

Discussion

This study forecasted the age-standardized incidence, death, and DALY rates of presenile dementia for the years 2020–2030 by methodically analyzing the global prevalence of presenile dementia during 1990–2019. The global number of cases, deaths, and DALYs of presenile dementia continued to increase between 1990 and 2019, with all indicators rising by at least one-fold. On the one hand, this increase is primarily attributed to the worldwide population’s recent decades of rapid expansion and aging. On the other hand, it may also be linked to advancements in disease diagnostic technologies and methods, which have led to the identification of more cases. During this period, the age-standardized incidence, death, and DALY rates of presenile dementia exhibited an overall upward trend. The forecasted results indicate that between 2020 to 2030, the age-standardized incidence rate is expected to rise gradually, whereas the age-standardized death and DALY rates will decline slowly. This suggests that policies such as implementing early intervention, encouraging healthy lifestyles, and bolstering social support systems, as outlined in the 2017 Global Dementia Action Plan of the World Health Organization,¹⁷ have the potential to reduce both death and the overall burden of presenile dementia. Future efforts should prioritize further support for these policies.

Both men and women experienced an upward trend in the age-standardized incidence, death, and DALY rates of presenile dementia from 1990 to 2019. Women had higher age-standardized incidence, death, and DALY rates than men in 2019. A meta-analysis found that although many studies did not show a significant statistical difference in the incidence of AD between men and women, the prevalence and incidence in women were higher in most studies.¹⁸ This phenomenon may be related to the different risks faced by women compared to men at physiological, social, and psychological levels.¹⁹ For example, estrogen in women can act as a protective factor against neurodegeneration, but as women age, especially after menopause, the rapid decline in estrogen levels may increase the risk of AD.^20–22 While the age-standardized incidence of presenile dementia in women is anticipated to modestly rise between 2020 and 2030, the age-standardized death and DALY rates are anticipated to fall over that time. In contrast, in men, the age-standardized incidence, death rate, and DALY rates for presenile dementia are anticipated to exhibit a consistent rising trend, consistent with the patterns observed from 1990 to 2019. This suggests that there may be a growing need for targeted early screening and disease management strategies for presenile dementia in the male population to address the increasing disease burden in this group.

Ageing, as the primary risk factor for AD, follows the same trend in presenile dementia.² Among the five age groups in 2019, the older the age group, the higher the incidence, death, and DALY rates. The age group aged 60–64 had the highest incidence, death, and DALY rates. However, a study by Hendriks et al highlighted that the early symptoms of presenile dementia are often subtle, and most data on AD are collected primarily from the elderly population, with young populations frequently excluded from studies.⁶ As a result, the incidence of presenile dementia in younger age groups may be significantly underestimated. The earlier the onset of presenile dementia, the greater the resulting disease burden. Therefore, in addition to focusing on the elderly population, early screening of younger individuals should also be prioritized.

Our study found that in 2019, the age-standardized incidence, death, and DALY rates of presenile dementia were negatively correlated with the SDI. The age-standardized death rates in low SDI nations increased the most between 1990 and 2019, whereas only the age-standardized death and DALY rates of presenile dementia in high SDI countries exhibited a downward trend. First, countries with higher SDI levels tend to have higher economic development and more abundant medical resources, which facilitates early diagnosis and intervention, potentially slowing the progression of presenile dementia. In contrast, low SDI countries generally face lower economic development and limited medical resources. These countries often prioritize addressing infectious diseases and malnutrition, which may result in fewer resources allocated for presenile dementia.⁹ Consequently, presenile dementia is typically detected at later stages when the patient’s condition has worsened. This delay in diagnosis and treatment could contribute to an increased mortality rate and higher DALY rates. Secondly, high SDI countries typically have more comprehensive social support systems, which can provide better care services and social welfare to individuals with presenile dementia, thereby reducing disability and death associated with the disease. The forecast for 2020–2030 reveals significant variations in the expected changes across different countries. High-income countries, such as Norway, are projected to experience improvements in the disease burden, while low and middle-income countries, including Senegal, Angola, and Burkina Faso, face a growing burden due to aging populations and inadequate public health infrastructure. Thus, it is crucial to develop targeted intervention strategies tailored to the unique requirements of each country to address the health challenges posed by presenile dementia.

The 2024 Lancet Dementia Commission report identified 14 risk factors for dementia, including hypertension, smoking, obesity, physical inactivity, diabetes, and others.²³ In this study, we analyzed three specific risk factors—smoking, Body Mass Index (BMI), and high fasting blood glucose. We found that smoking accounted for the largest proportion of deaths and DALYs related to presenile dementia. Moreover, both high fasting blood glucose and high BMI have been shown to increasingly contribute to the burden of presenile dementia, as they accounted for a growing proportion of deaths and DALYs worldwide. According to certain research, leading a healthy lifestyle that includes regular exercise, eating a balanced diet, and giving up bad habits like smoking will greatly lower the incidence of AD and presenile dementia.^24–26 Given the limited progress in developing effective drugs for AD,²⁷ controlling risk factors offers a critical avenue for prevention and management. By targeting risk factors that can be changed, like diabetes, high blood pressure, smoking, and a high BMI, we can mitigate the risk of presenile dementia, reduce its burden, and improve overall health outcomes. This approach is especially significant as it presents a preventive strategy when pharmacological solutions remain limited.

This study has several restrictions. First off, the GBD database served as the sole source of data for our investigation. There are variations in the diagnosis and reporting of presenile dementia throughout various nations and areas. In particular, in low-income countries, the incidence and disease burden of presenile dementia may be significantly underestimated. Secondly, the COVID-19 pandemic that occurred after 2019 in the world may have a significant impact on the epidemic trend of presenile dementia. Lastly, while there are many known risk factors associated with AD, the GBD data only includes metabolic and environmental factors, and does not account for other relevant risk factors. Therefore, future studies could improve the accuracy and reliability of global presenile dementia epidemic predictions by enhancing data quality, refining prediction models, and incorporating additional risk factors.

Conclusions

While our findings reveal a persistent and growing burden of presenile dementia—particularly in low SDI countries—despite declining age-standardized death and DALY rates, future research should aim to identify the underlying drivers of rising incidence and regional disparities. To lessen the increasing burden of disease, early detection, treatment, and public health initiatives must be strengthened immediately. These results highlight the necessity of focused preventative measures, especially resource-poor settings, where enhancing healthcare infrastructure, improving access to care, and promoting healthy lifestyles can help slow the rise in disease burden. Expanding screening programs to younger populations globally may alleviate the associated dementia burden, while systematic research and policy adjustments remain crucial to optimizing efforts to control presenile dementia.

Data Sharing Statement

The data were obtained from GBD study (http://ghdx.healthdata.org/gbd-results-tool), and replication details are provided in the supplementary file.

Ethics Approval and Consent to Participate

The study was approved by the Ethics Committee of Shenzhen Nanshan Center for Chronic Disease Control (ll20240017).

Funding

This research was funded by National Nature Science Foundation of China (No: 82473625), and Shenzhen Science and Technology Program (No: JCYJ20230807153400001).

Disclosure

The authors report no conflicts of interest in this work.

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As a 40-year-old who lives with cystic fibrosis (CF), Zachary Schulz, PhD, EdS, MPH, is said to be among the reputed “last generation” of patients — those born before the advent of drugs that target the disease’s underlying genetic defect. The moniker has led him to wonder if it’s become just as difficult for clinicians to characterize this patient cohort as it has been to help people survive the challenging diagnosis. “It’s nonsense. We’re supposed to be dead,” said Schulz, a senior lecturer of history at Auburn University, Auburn, Alabama, who teaches health humanities and holds a master’s in public health, which lends to his unique perspective as a patient and student of the chronic condition. “We’re discovering that the pediatric lung disease we were born with is now becoming this ‘weird’ disease where we don’t know what’s going on anymore. We’re adults now. Where do we go from here?”

According to pulmonologists and other clinicians who help patients with CF to manage their conditions, the future for the estimated 2500 patients born with CF each year will likely still be dependent on reducing the severity of commonly acquired CF-related infections and ultimately treating them when they are infected.

Infection Control and Management

The threat of acute and chronic infections is essentially unavoidable for those who live with CF, but there are strategies for reducing severity for all age groups. As a baseline, the Cystic Fibrosis Foundation (CFF) recommends patients receive respiratory cultures quarterly, although more can be necessary during exacerbations or other signs of changing health status.

A variety of oral, inhaled, and intravenous antibiotics can be considered, but not all antibiotics are created equal, said Peter Polos, MD, PhD, a board-certified pulmonologist and medical advisor at Lindus Health, a full-service, end-to-end clinical trial provider for life science companies based in Boston.

“Any treatment decision should be driven by severity, patient history, and pathogen,” he explained. For example, someone with an initial presentation of mild symptoms could warrant an oral antibiotic with a bronchodilator to keep the airways open so that mucus doesn’t become too thick and escalate a productive environment for bacteria. 

Many clinicians will also use an antibiotic gel so that pathogens are not inhaled to set up infection, said Polos. “And then we’re doing what these patients are used to — chest physiotherapy and hydration,” he said. “Some patients will also do postural therapy if they’re trying to drain secretions out of their lungs. There’s a variety of techniques that are patient specific and clinical setting specific.”

At Phoenix Children’s Hospital, Pheonix, Michelle M. Ratkiewicz, DO, pulmonology specialist, said that when using cultures as a guide, the tendency is to turn to antibiotics more quickly, especially for pediatric patients. “If we know that the patient typically grows methicillin-resistant Staphylococcus aureus [MRSA] or Pseudomonas aeruginosa, our treatment is going to be dictated by those chronic cultures,” she said. “We might choose a different antibiotic than you might choose for a standard community-acquired infection. And our duration of treatment tends to be slightly longer, particularly in the setting of bronchiectasis or lower-airway damage. We also have a lower threshold to treat CF with antibiotics if symptoms following a viral infection are lasting longer, are more severe, or fall outside the usual course of a viral infection. We may suspect that bacterial infection is more active.”

In the setting of infection, Ratkiewicz said she will increase the intensity of treatment. “Typically, we’ll do a more frequent therapy vest, or if the patient uses a positive expiratory pressure device, we always recommend doing extra in the setting of an illness,” she said. “We also will sometimes augment aerosol treatments. Some patients use dornase alfa or hypertonic saline all the time. Some patients just use that when they’re sick or use it more frequently when they’re sick. But we always step up the frequency and intensity of their airway clearance beyond what they do at baseline in the setting of a viral infection or a bacterial infection. And that should be true for adults and pediatric patients.”

Attempting to dodge infections can invoke generalized tactics that are promoted to any segment of the public, with advice including hand hygiene, routine vaccinations, avoidance of sick contacts, and not sharing personal items. However, there are approaches to better protect the particularly susceptible CF population within these parameters. “Some important ways to prevent infections are to use contact precautions, including gowns and gloves, within medical settings,” said Anthony J. Fischer MD, PhD, a pulmonologist at University of Iowa Health Care. “This can protect people from acquiring antibiotic-resistant microbes that they are most prone to. Another important step is to ensure nebulizers and other respiratory equipment is properly sanitized daily.”

Polos noted there are points of emphasis to make on infections control. “Often, companies have suggested regimens for how to clean equipment that are specific for the device,” he explained. “But you can accomplish infection control by the basics of soapy warm water and then rinsing the equipment well and letting it dry properly.”

As far as infection control for patients who are washing their hands with soap and water, “we generally say 20-30 seconds of good scrubbing of the back and the front of your hands, as well as between your fingers, and rinsing with warm water. If they’re using hand sanitizers, use those that give about 60 percent alcohol.” 

Ratkiewicz also recommends that patients with CF refrain from certain indoor and outdoor environmental triggers and activities that can lead to more frequent exposure to bacteria, such as hot tubs, stagnant bodies of water, humidifiers, and gardening due to an abundance of soil and water. “Fortunately, chlorinated swimming pools and the ocean are safe,” she said.

Maintaining Multidisciplinary Best Practices

With the benefit of potentially initiating CF transmembrane conductance regulator modulator therapy earlier in a patient’s treatment continuum, many providers are not seeing a severity of lung disease that had become expected previously. Still, there are necessary precautions that are best served through a multidisciplinary care team.

“All people with CF should receive routine care at a center accredited by the CFF,” Ratkiewicz suggested. “Patients are more likely to have comorbidities such as diabetes, osteoporosis, and significant sinus disease that may require additional therapies or subspecialty involvement.” Ratkiewicz’s team includes physicians, nurse practitioners, dietitians, respiratory therapists, social workers, pharmacists, physical therapists, and psychologists. “We’re diligent about adhering to the clinical care guidelines by the CFF that dictate frequency of screening tests, monitoring lung function, and surveillance cultures. People with CF really shouldn’t be getting care at a general pulmonary clinic or a place that doesn’t have experience with CF,” said Ratkiewicz.

Inside her clinic, Ratkiewicz follows strict protocol that requires patients to not share elevators or restrooms and to be seen without spending time in a waiting room. Everyone also has the option of wearing purple CF identity badges that help others to distance. All rooms are disinfected between visits, with extra consideration given to multi-use equipment. Ratkiewicz also urges that patients with CF not share classrooms, day camps, or sports teams, except for siblings. She said it’s important to be increasingly mindful of these types of precautions to reduce the potential of complacency as modalities improve.

“Some people refer to CF as an ‘invisible disease’ because patients might outwardly appear healthy, but they really need more attention,” she said. “There’s less significant lung disease today, but that is not universal. We see adults who are healthy and stable, and pediatrics who have as much lung disease as some adults might have. CF is not ‘one-size-fits-all.’”

Schulz can attest to that. Born with the most common Delta F508 mutation, he began taking a modulator in 2015 and has always displayed low expressivity. He does carry pulmonary scar damage from an early-age infection and has colonized MRSA, but his cardiovascular status is excellent, allowing for regular bike rides.“My capacity for spirometry testing is well within the 100th-110th percentiles,” he said. “I feel healthier than I think most do. I don’t have joint issues. My resting heart rate is 56. My blood pressure is about 114 over 64. I don’t produce a lot of sputum anymore with the modulator. My main display of cystic fibrosis is a lack of pancreatic enzymes.” 
This all ranks in stark contrast to that of his older brother Eric, who was born with the same mutation but passed away several years ago. “Hewas a standard case,” said Schulz. “Constant exacerbations. Nebulizer tents that weren’t good for mold growth. Heavy steroid use by the end of his life to control inflammation. Lack of weight gain. We were involved in studies while he was alive on how siblings with the same mutation could have significant differences in expressivity.”

Potential of Evolving Care

There’s enlightening research ongoing to potentially improve future CF management, said Ratkiewicz. Notable examples include compounds to help disrupt biofilm and studies to help better detect bacterial infections through exhaled breath. “Many patients don’t produce sputum because they’re so much healthier on modulator therapy,” she said. “Using detecting compounds and exhaled breath that could detect viral or bacterial infections could be very helpful in identifying infections early and allowing us to treat them more effectively. Clustered regularly interspaced short palindromic repeats and mRNA technologies are also being explored as potential treatment options, and studies are underway on bacteriophage therapy, specialized viral particles that replicate inside bacteria and kill bacteria in a specific way.”

Current literature shows no routine use of biologics for CF, according to Ratkiewicz, with the most common use being for those with poorly controlled asthma or allergic bronchopulmonary aspergillosis, an allergic response to Aspergillus in the airways. “But it’s an interesting concept,” she said. She’s also encouraged by an increased rate of parents choosing to deliver babies born with CF since the more widespread use of modulator therapy beginning in 2019. “And that is because people are living longer,” she said. “We’re seeing many adults living very productive and healthy lives. More people are pursuing higher education, working full time, and we really expect that to continue to get better.”

Schulz, Ratkiewicz, Fischer, and Polos reported no relevant financial relationships.

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Introduction

Cardiovascular disease (CVD) represents the leading cause of mortality and morbidity globally, accounting for approximately 30% of all disease-related deaths each year.¹ Acute coronary syndrome (ACS) is one of the most lethal subtypes of coronary heart disease, requiring timely risk assessment and effective therapeutic interventions to improve patient outcomes.² Advances in medical techniques, particularly the widespread use of percutaneous coronary intervention (PCI), have significantly reduced mortality of ACS.^3,4 However, the major adverse cardiovascular events (MACEs) after PCI continue to threaten the health and quality of life of ACS patients.^5,6 Therefore, identifying biomarkers that can improve early diagnosis and predict the prognosis of ACS remains an urgent priority.⁷

Exosomes are small extracellular vesicles, with dimensions ranging from 30 to 150 nm, secreted by nearly all cell types. They function as cargo transporters, transferring nucleic acid, proteins, lipids, and other stuffs between cells.⁸ Exosomes are integral to numerous biological processes and the pathogenesis of various diseases.⁹ The process of exosome biogenesis enables the packaging of molecules from both membranous and cytosolic origins, making them reflective of the state of the releasing cell and providing valuable insights into the cellular environment. The encapsulation of proteins and RNAs within exosomes prevents their degradation, making exosomes an ideal source of biomarkers. Advances in exosome isolation techniques have garnered significant attention for their potential in clinical applications. Increasing evidence supports the potential of exosomes as valuable biomarkers for early diagnosis and prognosis assessment in cardiovascular diseases.¹⁰

MALAT1-associated small cytoplasmic RNA (mascRNA) originates from the nuclear long non-coding RNA MALAT1, is a tRNA-like small non-coding RNA, and is localized in the cytoplasm.¹¹ While MALAT1 has been extensively studied and shown to influence various cellular processes, including the development of atherosclerosis,^12,13 the function of mascRNA remains largely unknown. Recent research has detected high levels of mascRNA in circulating human peripheral blood mononuclear cells (PBMCs).¹⁴ MascRNA suppresses the production of inflammatory cytokines in LPS-stimulated macrophages by inhibiting the activation of NF-κB and MAPK signaling pathways.¹⁵ In murine models of atherosclerosis, mascRNA deficiency leads to hyperactivity of circulating inflammatory cells and an increased macrophage presence in atherosclerotic plaques, contributing to plaque rupture and thrombosis formation.¹⁶ However, the expression of mascRNA in circulating exosomes remains poorly understood.

Our previous study demonstrated that MALAT1 may serve as a promising biomarker for cardiovascular disease, showing diagnostic potential for ACS patients.¹⁷ However, limited research has evaluated the diagnostic value of mascRNA in cardiovascular disease. This study aims to explore the link between plasma-derived exosomal mascRNA and the occurrence of ACS and its association with adverse cardiovascular events.

Materials and Methods

Study Subjects

This study included 281 patients who underwent coronary angiography at Meizhou People’s Hospital from Oct. 2021 to May 2024. The ACS patients were diagnosed according to the 2020 ESC Guidelines for managing acute coronary syndromes,¹⁸ which were characterized by symptoms such as recurrent chest pain at rest or with minimal exertion, as well as severe angina that began or worsened within 4 weeks before the procedure. The exclusion criteria included severe valvular heart disease, severe arrhythmias, acute or chronic inflammation, malignant tumors, autoimmune diseases, and hematologic disorders. The non-ACS group included individuals who were diagnosed without coronary artery disease (CAD) by cardiologists as coronary angiography indicative of stenosis < 50%. This research was approved by the Ethical Committee of Meizhou People’s Hospital (Approval No. MPH-HEC 2023-C-34) and was conducted in full accordance with the principles of the Declaration of Helsinki. Informed written consent was collected from each participant. Figure 1 illustrates the study flow.

Figure 1 Study flow diagram.

Plasma Collection

A total of 5 mL of venous blood was obtained from patients prior to PCI and placed in EDTA anticoagulant tubes. The samples were maintained at 4°C for 2 hours. Subsequently, the samples were centrifuged at 300g for 10 minutes, after which the supernatant was collected and aliquoted into centrifuge tubes for exosome isolation.

Exosome Isolation

Exosomes were isolated from plasma by ultracentrifugation techniques (CP100NC, Hitachi), as shown in Figure 2A. In brief, the plasma was subjected to centrifugation at 2000g for 10 minutes, after which the supernatant was collected. This was followed by centrifugation at 10,000g for 30 minutes, and the supernatant was again collected. Subsequently, the sample was centrifuged at 120,000g for 30 minutes, and the supernatant was carefully discarded. The resulting pellet was resuspended in PBS and centrifuged once more at 120,000g for 30 minutes. The supernatant was discarded, leaving the exosomes at the bottom for subsequent experiments. All centrifugation procedures were performed at 4°C.

Figure 2 Isolation and characterization of exosomes from plasma. (A) Centrifugation protocol for enrichment of plasma exosomes; (B) Transmission electron microscopy (TEM) analysis of the exosome morphology. Representative exosomes are indicated by arrows; (C) The particle size of exosomes was measured by nanoparticle tracking analysis. (D) Western blot analysis of the exosomal markers.

Characterization of Exosomes

The exosomes derived from plasma were characterized using nanoparticle tracking analysis (NTA), Western blotting, and transmission electron microscopy (TEM). For the Western blot analysis, the exosomes were probed with the following primary antibodies: anti-CD9 (1:1000, Cell Signaling Technology), anti-CD63 (1:1000, Cell Signaling Technology), and anti-TSG101 (1:1000, Cell Signaling Technology). The NTA was conducted using a NanoSight NS300 instrument (Malvern Panalytical) to evaluate the size, distribution, and concentration of the exosomes. TEM was performed with a JEM-1400 microscope (JEOL, Japan) to examine the ultrastructural features and size of the exosomes.

RNA Isolation and Reverse Transcription-Quantitative Polymerase Chain Reaction (qRT-PCR)

Exosomal RNA was extracted utilizing the SteadyPure Small RNA Extraction Kit (Accurate Biology, China). RNA quality was assessed by measuring the A260/A280 ratio with an ultramicro-spectrophotometer (NP80, IMPLEN, Germany). Complementary DNA (cDNA) was generated by PrimeScript™ RT reagent Kit (Takara, Japan). Exosomal mascRNA expression was determined utilizing the TB Green^® Premix Ex Taq™ II and normalized to U6 using the 2^−ΔΔCt method.¹⁹ The primer sequences for qRT-PCR were as follows:

mascRNA forward, 5’-GATGCTGGTGGTTGGCACTC-3’; mascRNA reverse, 5’-TGGAGACGCCGCAGGGAT-3’; U6 forward, 5’-CTCGCTTCGGCAGCACA-3’; U6 reverse, 5’-AACGCTTCACGAATTTGCGT-3’.

Clinical Data Collection and Follow-Up

The clinical characteristics of patients were retrieved from the hospital’s electronic medical records. Collected variables included age, gender, hypertension, diabetes mellitus, dyslipidemia, left ventricular ejection fraction (LVEF), blood pressure, glucose levels, lipid profiles and blood cell counts.

One-year follow-up data for ACS patients were obtained from electronic medical records or through telephone interviews. The primary outcome measure was the incidence of major adverse cardiovascular events (MACE) including all-cause mortality, nonfatal myocardial infarction, target vessel revascularization, rehospitalization for angina or heart failure, and stent thrombosis.

Statistical Analysis

Statistical analyses were conducted using SPSS 20.0 (IBM Corp., Armonk, NY, USA). Data were presented as mean ± SD or number (percentage). The Shapiro–Wilk test checked the normality of continuous variables. Student’s t-test was used for continuous variables, and chi-square or Fisher’s exact test for categorical variables. The sample size, based on China’s ACS incidence of 1%, is approximately 95, with a significance level of α = 0.05 and a 2% margin of error. The correlation between exosomal mascRNA and clinical parameters were analyzed by Spearman correlation analysis. Logistic multivariate regression analysis was employed to assess the relationship between exosomal mascRNA and ACS risk. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic value of exosomal mascRNA for ACS. The one-year MACE-free survival was assessed using Kaplan–Meier analysis and the Log rank test, while multivariable Cox regression identified predictors of 1-year MACEs in ACS patients. A P-value < 0.05 was considered statistically significant.

Result

Characteristics of Study Subjects

The study included 140 ACS patients and 141 non-ACS, with baseline characteristics summarized in Table 1. There was no difference between the two groups regarding gender, age, hypertension, diabetes mellitus, and dyslipidemia. ACS patients exhibited higher levels of white blood cell (WBC), monocytes, neutrophils, Gensini scores, cTnI (P < 0.05), and lower LVEF compared to non-ACS group (P < 0.05).

Table 1 Baseline Characteristics of Study Subjects

Identification of Plasma Exosomes

Plasma exosomes were isolated utilizing multiple ultracentrifugation steps (Figure 2A). Plasma exosomes exhibited a typical double-layered vesicular structure (Figure 2B), with a mean diameter of approximately 130 nm (Figure 2C). Western blot analysis verified the expression of exosomal protein markers CD9, TSG101 and CD63 (Figure 2D).

Expression of Exosomal mascRNA in Patients with ACS

Our data suggested that exosomal mascRNA expression was elevated in ACS patients compared to the non-ACS (Figure 3A). However, exosomal mascRNA expression showed no significance between the subgroups of ACS (Figure 3B).

Figure 3 The expression of exosomal mascRNA in patients with ACS. (A) Exosomal mascRNA levels in ACS patients and non-ACS patients. **P < 0.01, comparison was tested by Student’s t test; (B) Exosomal mascRNA levels in different types of ACS patients. (C) Comparison of exosomal mascRNA expression in ACS patients with MACE and non-MACE during the one-year follow-up. *P < 0.05, comparison was tested by Student’s t test.

We compared the expression of exosomal mascRNA in patients with or without MACEs during the 1-year follow-up period after PCI treatment. A total of 29 ACS patients developed MACEs during the follow-up. Our data showed that mascRNA expression was significantly higher in the MACE group than the non-MACE group (Figure 3C).

Association Between Exosomal mascRNA and Clinical Variables

We further analyzed the association between exosomal mascRNA and clinical parameters. As shown in Figure 4, the Spearman correlation analysis revealed a significant positive correlation between exosomal mascRNA levels and Gensini scores (r = 0.242, P < 0.001), LDL (r = 0.173, P = 0.019), WBC (r = 0.183, P = 0.012), age (r = 0.164, P = 0.013). No significant associations were observed between exosomal mascRNA levels and LVEF (r = −0.120, P = 0.103), neutrophil count (r = 0.100, P = 0.109), as these differences did not reach statistical significance.

Figure 4 Correlation between exosomal mascRNA and clinical parameters. The correlation between exosomal mascRNA and age (A), LVEF (B), Gensini score (C), LDL level (D), WBC (E), and neutrophil (F) was assessed by Spearman correlation analysis.

The Diagnostic Value of Exosomal mascRNA for ACS

The diagnostic value of exosomal mascRNA for ACS was evaluated by ROC curve analysis. Our data revealed that exosomal mascRNA serves as a diagnostic predictor for ACS, with an AUC of 0.763 (95% CI: 0.702–0.824) and cutoff value of 1.173 (Figure 5). The predictive performance of mascRNA improved when combined with cTnI, with the AUCs increased to 0.866 (95% CI: 0.815–0.916) (Figure 5).

Figure 5 The diagnostic value of exosomal mascRNA for ACS.

To illustrate the association of the exosomal mascRNA with ACS risk, its levels were categorized into quartiles (35 patients for each quartiles). Compared with patients in the first quartile for mascRNA expression, patients in the second, third and fourth quartiles exhibited increased ACS risk (OR: 3.423, 95% CI: 1.427–8.213, OR: 5.542, 95% CI: 1.859–16.524 and OR: 9.288, 95% CI: 3.275–26.340, respectively; all P < 0.01; Table 2).

Table 2 Association Between Exosomal mascRNA Expression and Risk of ACS

The Prognostic Value of Exosomal mascRNA for ACS

We further explored whether the expression of exosomal mascRNA predict the occurrence of MACEs. Patients were divided into high mascRNA group (≥ 3.85, n = 60) and low mascRNA group (< 3.85, n = 60). Kaplan-Meier analysis and Log rank test were utilized to assess the 1-year MACEs‐free survival rate between high mascRNA and low mascRNA groups. The data revealed that patients with high mascRNA expression have a lower incidence of MACE-free survival compared to those with low mascRNA expression (long rank P < 0.001) (Figure 6).

Figure 6 The prognostic value of exosomal mascRNA in patients ACS. The 1-year MACEs‐free survival rate between high mascRNA and low mascRNA groups was assessed by Kaplan-Meier curves.

A multivariate Cox regression analysis was performed to determine association between exosomal mascRNA and MACEs in ACS patients. After adjusted for age, diabetes mellitus and LVEF, mascRNA was significantly associated with the occurrence of 1-year MACEs, with a HR of 2.959 (95% CI: 1.187–4.669, P < 0.001) (Tables 3 and 4).

Table 3 Clinical Characteristics of Non-MACE and MACE Group in ACS Individuals

Table 4 Multivariate Cox Regression Model Analysis of MACEs in ACS Patients

Discussion

ACS is still the leading cause of mortality despite the advances in treatment and diagnostic modalities.² Precise diagnosis of ACS is crucial for effective therapeutic intervention and enhancing patient survival rates. The study found that exosomal mascRNA levels were significantly higher in ACS patients and closely linked to ACS risk, suggesting its potential as a diagnostic and prognostic biomarker.

Cardiac troponin (cTnI) is the key plasma biomarker for detecting myocardial injury, with high sensitivity and specificity for diagnosing acute myocardial infarction (AMI). However, its specificity is low in the first 3 hours after symptoms begin, and elevated levels can also indicate other conditions such as myocarditis and stress-induced cardiomyopathy.^20,21 Exosomes have attracted increasing interest in the cardiovascular field due to their potential clinical implications. More and more exosome-based biomarkers are identified for diagnosis of cardiovascular diseases.^22–25 This study found that exosomal mascRNA levels were significantly higher in ACS patients, regardless of the type of ACS (UA, STEMI, or NTEMI), and were linked to an increased risk of ACS. MascRNA levels correlated with the Gensini score, LDL, and WBC, which are related to vascular stenosis, inflammation, and lipid metabolism. Notably, although the findings are significant, the correlations are weak, thus more studies would be needed to validate the clinical relevance of mascRNA.

MascRNA is a highly conserved small non-coding RNA originating from the primary transcript of MALAT1.¹¹ As one of the most abundant lncRNAs, MALAT1 has been established as a crucial regulator in cardiovascular pathological processes.^26–28 Our previous study as well as studies of others suggested that MALAT1 was enriched in exosomes and serve as potential biomarker for coronary heart disease.^29,30 To the best of our knowledge, this study is the first to identify the expression of mascRNA in plasma exosomes. Our data suggested that exosomal mascRNA could distinguish ACS from non-ACS individuals, achieving an AUC of 0.776. Notably, the combination of mascRNA and cTnI markedly enhanced diagnostic performance, achieving an AUC of 0.884, surpassing the efficacy of either marker alone and underscoring its clinical utility. Further investigation is warranted to assess the optimal integration of mascRNA with other established or emerging biomarkers to improve the specificity and accuracy of ACS diagnosis.

The prediction of major adverse cardiovascular events (MACE) is crucial for optimizing treatment strategies in patients with acute coronary syndrome (ACS). Numerous inflammatory biomarkers, such as C-reactive protein (CRP), the neutrophil-lymphocyte ratio (NLR), the fibrinogen/albumin ratio (FAR), and the systemic immune-inflammation index (SII), are gaining prominence in research due to their cost-effectiveness, simplicity, and ease of application.^31–33 Although these inflammatory biomarkers demonstrated a strong correlation with the occurrence of major adverse cardiovascular events (MACEs), their specificity remains problematic. Consequently, predictive biomarkers for MACEs are still limited.³⁴ This study found that patients with high exosomal mascRNA levels experienced a higher rate of MACEs within a year after PCI treatment, with mascRNA being an independent risk factor (HR = 3.357). This suggests a link between mascRNA and ACS outcomes. While the typical MACE incidence post-PCI is around 10%, our one-year follow-up showed a 20.7% rate (29/140), possibly due to the MACE criteria and the predominance of AMI among ACS patients.

Although the exact mechanisms underlying how mascRNA participated in the pathology of ACS remained unclear, some research suggested that it is in part due to its function on inflammation. Sun et al³⁵ reported that mascRNA inhibits the activation of NF-κB and MAPK signaling, as well as the production of inflammatory cytokines in macrophages stimulated by LPS. Gast et al¹⁶ found that selective ablation of mascRNA resulted in massive induction of TNF and IL-6 in macrophages, which significantly exacerbated vascular injury compared to wildtype macrophages. Previous studies have shown that endothelial dysfunction is linked to future MACEs. Endothelial dysfunction is a key factor in myocardial infarction and central to all ACS, contributing to atherosclerosis through vasoconstriction, macrophage migration, cellular growth, and inflammation.^36–38 Our prior research indicated that MALAT1 inhibits endothelial inflammation and the interactions between monocytes and endothelial cells through ATG5-mediated autophagy.¹³ Since mascRNA is closely associated with MALAT1, mascRNA may also participate in the regulation of endothelial inflammation. Nonetheless, additional investigations are required to elucidate the underlying mechanisms.

This study is subject to several limitations. Firstly, as a single-center investigation with a relatively small sample size and a retrospective design, it is vulnerable to information and selection biases. Consequently, multicenter cohort studies are required to validate our findings. Secondly, this study did not include a comparison of mascRNA levels before and after patient treatment. Future research should assess the changes in mascRNA expression pre- and post-treatment to explore its predictive value for MACEs.

Conclusions

In summary, exosomal mascRNA levels were elevated in the plasma of ACS patients and demonstrated significant diagnostic value for ACS. Furthermore, exosomal mascRNA demonstrated a significant association with the incidence of MACEs in patients ACS, indicating its potential utility as an independent predictor of adverse clinical outcomes.

Abbreviations

ACS, Acute coronary syndrome; cDMA, Complementary DNA; CVD, Cardiovascular disease; LVEF, Left ventricular ejection fraction; MACEs, Major adverse cardiovascular events; mascRNA, MALAT1-associated small cytoplasmic RNA; NTA, Nanoparticle tracking analysis; PBMCs, Peripheral blood mononuclear cells; PCI, Percutaneous coronary intervention; qRT-PCR, Reverse transcription-quantitative polymerase chain reaction; ROC, Receiver operating characteristic; TEM, Transmission electron microscopy; WBC, White blood cell.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

This research was granted by the Ethical Committee of Meizhou People’s Hospital (MPH-HEC 2023-C-34).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by China Foundation for Youth Entrepreneurship and Employment (P24032887714); Guangdong Basic and Applied Basic Research Foundation (2022A1515011860 and 2022A1515012590); Medical Research Foundation of Guangdong Province (A2023154); State Key Laboratory of Neurology and Oncology Drug Development (SKLSIM-F-202412).

Disclosure

The authors declare that they have no competing interests in this work.

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Introduction

Hypercapnic respiratory failure (HRF) is a common and life-threatening condition in critical care settings. It is characterized by elevated arterial carbon dioxide (CO₂) levels due to inadequate ventilation, and is often accompanied by hypoxemia.^1,2 The etiology of HRF is typically multifactorial, with common causes including chronic obstructive pulmonary disease (COPD), obesity hypoventilation syndrome, and sleep-disordered breathing.^3–5 Despite advances in medical therapy and ventilatory support, HRF continues to be associated with substantial morbidity and mortality.^6,7 Therefore, establishing reliable prognostic markers is crucial for early risk stratification and personalized management of patients with HRF.

Given the inflammatory nature of HRF, biomarkers that reflect systemic inflammation have attracted attention for their potential prognostic significance. The neutrophil percentage-to-albumin ratio (NPAR) is novel inflammatory biomarker that integrates neutrophil levels with serum albumin concentration.⁸ Neutrophils are widely recognized as cost-effective and sensitive indicators of acute inflammation, while serum albumin exerts anti-inflammatory, antioxidant, and antithrombotic effects.^9,10 Hypoalbuminemia is often a marker of malnutrition and heightened inflammatory response, both of which are frequently observed in patients with respiratory infections.^11,12 Recent researches have indicated that NPAR is an effective prognostic indicator in various clinical settings, including cardiovascular diseases, sepsis, acute renal injury, and malignancies.^13–16 Compared to other inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), NPAR has demonstrated superior predictive value for mortality in patients receiving maintenance hemodialysis and those with atrial fibrillation.^17,18 However, the prognostic utility of NPAR in HRF remains largely unexplored.

Most current research treats HRF as a secondary manifestation of underlying diseases like COPD, with limited investigation into HRF as a distinct clinical entity with its own epidemiological profile and long-term outcomes¹⁹ Considering that NPAR reflects both inflammatory and nutritional status, this study aims to evaluate its independent prognostic value for all-cause mortality in patients with HRF.

Materials and Methods

Research Subjects

We collected data from patients diagnosed with hypercapnic respiratory failure (HRF) who were admitted to the Department of Respiratory and Critical Care Medicine at Yancheng First People’s Hospital between October 2020 and September 2021. Inclusion criteria were as follows: (1) diagnosis of HRF with arterial oxygen pressure (PaO2) <8.0 kPa (60 mmHg), and arterial carbon dioxide pressure (PaCO2) >6.0 kPa (45 mmHg); and (2) age ≥18 years. Exclusion criteria included: (1) age <18 years; (2) death during hospitalization or withdrawal from treatment; (3) conditions that could affect NPAR values such as trauma, malignant tumors, hematologic malignancies, or pregnancy; and (4) incomplete clinical records. After excluding 4 patients with missing clinical data and 33 lost to follow-up, a total of 561 patients were included in the final analysis. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki, and was approved by the Ethics Committee of Yancheng First People’s Hospital (Jiangsu, China) (Approval Number: 2020-K062). Informed consent was obtained from all participants.

Data Collection

Within 24 hours of admission, we collected data on demographic variables, comorbidities, nursing assessment scores, and laboratory values. The collected variables included age, sex, body mass index (BMI), smoking status, and eight comorbidities (hypertension, diabetes, cerebrovascular disease, cardiovascular disease (CVD), chronic emphysema, asthma, interstitial lung disease, and pneumonia). Nursing assessment tools included the Braden scale, self-care ability score, and venous thromboembolism (VTE) score. Laboratory data obtained within 24 hours of admission included PaO₂, PaCO₂, white blood cell count (WBC), lymphocyte count, hemoglobin, neutrophil percentage, and serum albumin. Neutrophil percentage was measured using the Sysmex XN-A1 automated hematology analyzer (Sysmex, Japan), which employs a combination of flow cytometry and impedance technology. Serum albumin was measured using the Beckman Coulter AU5831 system (Beckman Coulter, USA) through the bromocresol green dye-binding method. All procedures were performed in accordance with the manufacturers’ instructions. NPAR was calculated using the following formula: (neutrophil percentage [%] × 100) / albumin (g/dL).

Outcomes

This was a prospective cohort study in which patients were followed up via telephone for 24 months post-discharge. The primary outcome was 24-month all-cause mortality, and the secondary outcomes were 3-, 6-, and 12-month all-cause mortality.

Statistical Analysis

Patients were stratified into tertiles based on baseline NPAR values as follows: T1 (NPAR ≤ 20.23), T2 (20.23 < NPAR ≤ 23.85), and T3 (NPAR > 23.85). Continuous variables were presented as mean ± standard deviation for normally distributed data, and categorical variables were presented as counts and percentages. Group comparisons for continuous variables were performed using one-way analysis of variance (ANOVA), and categorical variables were compared using chi-square tests. The relationship between NPAR and all-cause mortality was assessed using restricted cubic spline (RCS) models and multivariate Cox proportional hazards models. Kaplan–Meier survival analysis was used to estimate cumulative survival, and the Log rank test was applied to assess statistical differences among groups. Subgroup analyses were conducted to further explore the association between NPAR and mortality. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate and compare the predictive performance of NPAR, neutrophil percentage, and albumin. All statistical analyses were conducted using R software (version 4.4.0). A two-sided P-value of < 0.05 was considered statistically significant.

Results

Patient Characteristics

Figure 1 illustrates the flowchart of patient selection. A total of 561 HRF patients were ultimately included, consisting of 357 males (63.64%) and 204 females (36.36%), with a mean age of 73.16 ± 9.80 years. Baseline characteristics stratified by NPAR tertiles are presented in Table 1: T1 (NPAR ≤ 20.23), T2 (20.23 < NPAR ≤ 23.85), and T3 (NPAR > 23.85), with 187 patients in each group. Compared to the T1 group, patients in the T3 group exhibited lower scores on the Braden scale and self-care assessments, as well as decreased levels of PaO₂, lymphocyte count, hemoglobin, albumin, estimated glomerular filtration rate (eGFR), triglycerides, and total cholesterol. In contrast, they demonstrated higher age, venous thromboembolism (VTE) scores, WBC counts, and neutrophil percentages. Moreover, the prevalence of cerebrovascular disease and pneumonia was significantly higher in the T3 group. No significant differences were observed among the groups in terms of sex, BMI, smoking status, hypertension, diabetes, CVD, chronic emphysema, asthma, interstitial lung disease, PaCO₂, platelet count, blood uric acid, or glucose levels. Importantly, the 3-, 6-, 12-, and 24-month all-cause mortality rates were all higher in the T2 and T3 groups compared to the T1 group.

Table 1 Baseline Characteristics of the Study Population

Figure 1 The flow diagram of sample selection in the study.

Relationship Between NPAR and All-Cause Mortality in HRF Patients

As shown in Figure 2, we used RCS modeling to assess the nonlinear relationship between NPAR and all-cause mortality of HRF patients. After adjusting for age, sex, BMI, smoking status, hypertension, diabetes, cerebrovascular diseases, cardiovascular diseases, chronic emphysema, asthma, interstitial lung disease, and pneumonia, the RCS model revealed a positive linear association between NPAR and all-cause mortality (P for overall association < 0.001; P for nonlinear association = 0.533).

Figure 2 Nonlinear association between neutrophil-percentage-to-albumin ratio (NPAR) and HRF using restricted cubic spline (RCS) analysis. Hazard ratios were adjusted for age, sex, BMI, smoking status, hypertension, diabetes, cerebrovascular diseases, cardiovascular diseases, chronic emphysema, asthma, interstitial lung disease, and pneumonia.

To further investigate this relationship, three Cox proportional hazards models were constructed. Table 2 presents the hazard ratios (HRs) and 95% confidence intervals (CIs) for each model. After adjusting for age and sex (Model 2), and then for a broader range of covariates including BMI, smoking status, hypertension, diabetes, cerebrovascular diseases, cardiovascular diseases, chronic emphysema, asthma, interstitial lung disease, pneumonia, WBC count, hemoglobin, platelet count, albumin, triglycerides, uric acid, total cholesterol, eGFR, and glucose (Model 3), NPAR remained significantly associated with 24-month all-cause mortality (Model 2: HR 1.08, 95% CI 1.05–1.11; Model 3: HR 1.08, 95% CI 1.03–1.12). When NPAR was analyzed as a categorical variable (in tertiles), the fully adjusted Model 3 showed that patients in the T2 and T3 groups had significantly higher 24-month mortality risk compared to those in T1 (HR 1.65, 95% CI 1.16–2.34 and HR 1.81, 95% CI 1.17–2.79, respectively). Similar associations were observed for 3-, 6-, and 12-month mortality outcomes.

Table 2 Associations Between NPAR and Outcomes of HRF by Cox Regression Analysis

Using Kaplan–Meier survival curves (Figure 3), patients were stratified by NPAR tertiles to evaluate cumulative survival. The 24-month all-cause mortality rates were 33.16% in T1, 49.20% in T2, and 59.89% in T3, with significant differences across groups (log-rank P < 0.001). Additionally, statistically significant differences in 3-, 6-, and 12-month mortality rates were also observed among the tertiles (log-rank P < 0.01). In summary, higher NPAR levels were consistently associated with increased all-cause mortality.

Figure 3 Kaplan-Meier curves for survival probability, with follow-up in months. (A) 3-month mortality; (B) 6-month mortality; (C) 12-month mortality; (D) 24-month mortality (NPAR: T1 (≤20.23), T2 (20.23–23.85), T3 (>23.85)).

Subgroup Analysis

We conducted subgroup analyses to further explore the association between NPAR and 3- and 24-month all-cause mortality among patients with HRF (Table 3). In most subgroups, elevated NPAR levels were consistently and significantly associated with an increased risk of both short- and long-term all-cause mortality. Importantly, no significant interactions were observed between NPAR and the stratifying variables, indicating that the association between NPAR and mortality remained robust across different patient populations. These findings provide additional support for the independent predictive value of NPAR in HRF.

Table 3 Subgroup Analysis of the Association Between NPAR and 3-Month and 24-Month All-Cause Mortality

ROC Curve Analysis

To further evaluate the predictive performance of NPAR, we conducted receiver operating characteristic (ROC) curve analysis, comparing NPAR with neutrophil percentage and albumin in predicting all-cause mortality in HRF patients (Table 4). For 3-month all-cause mortality, the AUC was 0.71 (95% CI, 0.66–0.77) for NPAR, which was significantly higher than that of neutrophil percentage (0.65, 95% CI, 0.59–0.71; P < 0.05) and not significantly different from albumin (0.67, 95% CI, 0.61–0.73; P > 0.05). For 12-month all-cause mortality, the AUC for NPAR was 0.66 (95% CI, 0.61–0.71), again exceeding that of neutrophil percentage and albumin, both of which had an AUC of 0.62 (95% CI, 0.57–0.67; P < 0.05). These results demonstrate that NPAR provides superior discriminatory ability compared to neutrophil percentage alone and offers better predictive accuracy than albumin at 12 months, highlighting its clinical utility as a composite prognostic marker in HRF patients.

Table 4 Comparisons of the AUCs of NPAR with Neutrophil Percentage and Albumin in Predicting All-Cause Mortality

Discussion

This study was the first to examine hypercapnic respiratory failure (HRF) as an independent clinical entity and to evaluate the prognostic significance of the neutrophil percentage-to-albumin ratio (NPAR) in this population. Our findings demonstrated that elevated NPAR levels were significantly associated with increased all-cause mortality at 3, 6, 12, and 24 months, even after adjusting for demographic and clinical confounders. These findings are consistent with prior studies that have established the prognostic value of NPAR in patients with chronic obstructive pulmonary disease (COPD), chronic kidney disease, and cardiovascular disease.^20–22

The ROC analysis further confirmed that NPAR outperformed neutrophil percentage in predicting all-cause mortality at multiple time points, with statistically significant differences. This superior predictive ability is likely due to NPAR’s dual capacity to reflect both acute systemic inflammation (via neutrophils) and nutritional status (via albumin). In contrast, neutrophil percentage alone captures only transient inflammatory activity. Neutrophils, which constitute a major component of white blood cells, play a critical role in mediating inflammatory responses.^23–25 As summarized by Wang et al, neutrophilic inflammation is a hallmark of COPD, a leading cause of HRF, and is frequently observed in both sputum and peripheral blood of these patients.^5,26 Therefore, neutrophils likely contribute significantly to the onset and progression of HRF.

Elevated NPAR values may result from increased neutrophil percentage, decreased albumin levels, or both. Although NPAR demonstrated numerically higher predictive accuracy than albumin at 3, 6, and 24 months, these differences did not reach statistical significance (P > 0.05). However, a significant difference was observed at 12 months, with NPAR showing greater discriminatory power than albumin (AUC 0.66 vs 0.62; P = 0.039), indicating its added prognostic value during intermediate-term follow-up. Hypoalbuminemia often reflects both malnutrition and systemic inflammation, and is associated with poor outcomes across a range of diseases, including cardiovascular disease, stroke, acute respiratory distress syndrome, and nonalcoholic steatohepatitis.^27–31 For example, a study involving 590 patients with acute exacerbation of COPD found that lower serum albumin levels were independently associated with prolonged hospital stays (OR 0.92, 95% CI 0.87–0.97).³² Thus, NPAR effectively integrates two key prognostic components—inflammation and nutritional status—into a single composite indicator. A growing body of evidence supports the notion that NPAR outperforms either neutrophil percentage or albumin alone in predicting clinical outcomes.^33,34 While the prognostic value of NPAR has been established in other conditions, its application in HRF has not been previously explored, and this study helps to fill that gap in the literature.

Importantly, the clinical implications of our findings extend beyond statistical associations. In our fully adjusted model, a 1-unit increase in NPAR was associated with an 8% increase in 24-month mortality risk (adjusted HR 1.08, 95% CI 1.03–1.12). Moreover, patients in the highest NPAR tertile (T3) exhibited an 81% higher risk of 24-month mortality compared to those in the lowest tertile (T1) (HR 1.81, 95% CI 1.17–2.79). These findings suggested that NPAR may serve as a valuable tool for risk stratification in clinical practice. For patients with HRF, we propose that an NPAR threshold >23.85 could identify individuals at high risk, who may benefit from enhanced monitoring (eg, more frequent vital sign assessments, daily arterial blood gas analysis) and targeted interventions, such as anti-inflammatory therapy and nutritional support (eg, albumin supplementation). Such measures may help reduce mortality and improve patient outcomes.

A major strength of this study lies in its prospective design and the inclusion of a relatively large cohort, which enhances the generalizability of our findings to similar clinical settings. In addition, we assessed multiple mortality endpoints (3-, 6-, 12-, and 24-month all-cause mortality), allowing for a comprehensive evaluation of NPAR’s short- and long-term predictive performance. Stratifying patients into NPAR tertiles further clarified the dose-response relationship between NPAR and mortality risk in HRF. The tertile grouping more intuitively demonstrates that as the NPAR level increases, the survival probability gradually decreases.

However, several limitations should be acknowledged. First, although this was a prospective study, its observational design limits the ability to infer causality. Despite multivariable adjustments, residual confounding cannot be completely ruled out. Second, the study was conducted at a single center, which may introduce center-specific bias, though it also ensured standardized clinical management and data collection. Third, our dataset was limited in scope, and certain potentially relevant variables (eg, COPD severity, body composition, inflammatory cytokine levels) were not included. Future studies should consider integrating more comprehensive clinical, biochemical, and imaging data. Moreover, to minimize selection bias, future research should employ advanced techniques for handling missing data, such as multiple imputation by chained equations (MICE). We also encourage multicenter, large-scale prospective studies or integrate NPAR with other established predictors to develop comprehensive prognostic models in diverse HRF populations. Finally, mechanistic studies exploring how NPAR influences mortality—through interactions between neutrophil activity, albumin levels, and inflammatory signaling pathways—could uncover novel therapeutic targets. Evaluating the impact of anti-inflammatory strategies or nutritional supplementation in high-NPAR patients may also help define new therapeutic approaches.

Conclusions

In conclusion, our study demonstrates that NPAR is independently and positively associated with all-cause mortality at 3-, 6-, 12-, and 24-month all-cause mortality in HRF patients. As a biomarker that reflects both inflammatory and nutritional status, NPAR shows promise for clinical risk stratification. However, its current predictive performance remains limited and requires further refinement to enhance clinical applicability. Future research should focus on integrating NPAR with other established predictors to develop comprehensive prognostic models, thereby improving overall predictive accuracy and clinical utility.

Abbreviations

NPAR, neutrophil percentage-to-albumin ratio; HRF, hypercapnic respiratory failure; BMI, body mass index; CVD, cardiovascular disease; VTE, venous thromboembolism; PaO₂, arterial oxygen pressure; PaCO₂, arterial carbon dioxide pressure; WBC, white blood cell count; eGFR, estimated glomerular filtration rate; HR, hazard ratio; CI, confidence interval; AUC, area under the receiver operating characteristic curve.

Data Sharing Statement

The datasets used and analyzed in this study are available from the corresponding author upon reasonable request.

Ethical Approval and Consent to Participate

This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Yancheng First People’s Hospital (Jiangsu, China) (Approval Number: 2020-K062). Informed consent was obtained from all participants prior to data collection.

Acknowledgments

We express our gratitude to all the participants and colleagues who actively contributed to this study.

Author Contributions

All authors significantly contributed to the work, including its conception, design, data acquisition, analysis, and interpretation. They participated in drafting, revising, and critically reviewing the article. Each author approved the final version for publication, agreed on the target journal, and accepted accountability for all aspects of the work.

Funding

This study did not receive any specific grant from funding agencies in the Public, Commercial, or Not-for-Profit Sectors.

Disclosure

The authors declare that they have no competing interests.

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