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Introduction

Chronic obstructive pulmonary disease (COPD) is a debilitating condition that is characterized by poorly reversible airflow limitation, difficulty breathing during physical activities^1–4 decreased exercise capacity,⁵ and limitations in daily activities.^6–9 According to the Italian National Statistics Institute (ISTAT) and the Global Burden of Diseases initiative,^10–12 in Italy, approximately 3.5 million adults are affected by COPD and 2.5% of all Disease Adjusted Life Years lost were attributable to COPD in 2021.¹³ However, these figures may underestimate the actual prevalence of COPD because the disease is often diagnosed only in its advanced stages.^11,14 According to the Medicines Utilization Monitoring Center report recently published by the Italian Medicines Agency, some patients discontinue treatment early after initiating maintenance therapy,¹¹ emphasizing the need for improved COPD management in terms of appropriate diagnosis, pharmacological treatment, and treatment adherence.¹⁵ The management of COPD presents several challenges, including misdiagnosis, delayed diagnosis, failure to implement fundamental measures to slow disease progression (eg, tobacco cessation, vaccinations, and lifestyle changes), uncertainty in selecting the most appropriate drug for treatment, and poor adherence to therapy.^16,17 The Global Initiative for Chronic Obstructive Lung Disease (GOLD 2023–2025) recommend a comprehensive approach to COPD management, including accurate diagnosis and severity assessment, smoking cessation, and individualized pharmacological and non-pharmacological interventions, with a focus on managing exacerbations.^15,18 An e-Delphi study of 600 general practitioners (GPs) in Italy reported that although most GPs were familiar with the GOLD 2023 report and COPD reimbursement requirements, only 34% had access to spirometry. There was no consensus on the initial treatment options, and re-evaluation of triple therapy necessitated a specialist referral.¹⁹

Effective COPD management cannot be limited to expert care alone, especially when prevention and long-term monitoring are essential for optimal outcomes. To address the challenges in COPD management, the Italian Medicines Agency (AIFA) introduced Nota 99, which conforms to the GOLD report 2022 and confers GPs the responsibility of diagnosing and prescribing appropriate medication for mild to moderate COPD. Given the considerable prevalence of COPD, this act recognizes the critical role of GPs in managing COPD. Nota 99 allows GPs to prescribe any inhaled therapy, except for the single-device inhaler triple therapies, while maintaining specialist care for individuals with severe pulmonary obstruction or recurrent exacerbations.¹¹

Given this new scenario, ASTER, an Italian observational prospective multicenter trial, was designed to provide the first meaningful insights into COPD management by GPs following the Nota 99, describing the characteristics of patients, treatment patterns (primary outcome), and clinical outcomes (secondary outcome) over a 6-month observation period.

Materials and Methods

Trial Design and Oversight

ASTER was an observational, multicenter, prospective cohort study conducted in Italy that focused on patients with COPD who were managed by GPs following standard protocols of clinical practice. Consecutive patients at each participation center who provided the written informed consent and privacy form and met the eligibility criteria were enrolled in the study. The study was conducted in 30 centres distributed throughout Italy in order to obtain results reasonably albeit not formally representative of the management of COPD in general medicine in Italy according to the Nota 99. This study was conducted in compliance with the Guidelines for Good Pharmacoepidemiology Practice (GPP)²⁰ and the regulatory elements of observational research in Italy.²¹

Eligible patients were aged 40–80 years and had spirometry-confirmed COPD (post-bronchodilator Forced Expiratory Volume in one second (FEV₁) to Forced Vital Capacity (FVC) ratio <0.70) with an FEV₁ of ≥50% of the predicted value. Patients were enrolled if they had ≤1 exacerbation requiring antibiotics and/or oral corticosteroids; had no emergency room (ER) visits or hospitalizations for COPD in the past year; and, according to the prescription limits for GPs before Nota 99, they could have been treated in the last 3 months before enrollment exclusively with a short or long-acting bronchodilator or an ICS/LABA; and had a COPD Assessment Test (CAT) score ≥10 at the enrollment appointment. Patients were excluded if they were unable to undergo spirometry according to Nota 99, had received LABA/LAMA combinations within the previous 3 months, had low treatment adherence as judged by the clinician, inability to properly use an inhaler, were pregnant or breastfeeding, had a current asthma diagnosis, could not read or write in Italian, or were already enrolled in another clinical trial.

Each patient was assessed during the enrollment visit, which coincided with the reconfirmation of diagnosis and therapy prescription. Patients were then followed up with specific visits at 3 and 6 months, as outlined by standard clinical practice. During the enrollment visit, the GP collected the patient’s history of respiratory disease and symptoms including those within the previous year, occupational and tobacco smoke exposure, COPD anamnesis, and previously prescribed COPD therapies as well as comorbidities and related therapies. The GPs provided the COPD Assessment Test (CAT)²² to the patients and completed the modified Medical Research Council Dyspnea Scale (mMRC)²³ questionnaires. At the 3- and 6-month follow-up visits, the GP collected data on the incidence, severity, and treatment of exacerbations and adjusted the treatment as needed. Additionally, at the 6-month visit, the GP collected information on functional parameters if spirometry was performed according to clinical practice, provided the CAT questionnaire to the patient, and completed the mMRC questionnaire.

Primary and Secondary Effectiveness Analyses

The primary endpoint of the study was to describe treatment patterns during the 6-month observation period, including the proportion of patients taking different COPD medications and any changes in treatment patterns. The secondary endpoints were demographic and clinical features, FEV₁ at enrollment and after 6 months, patient-reported outcomes (CAT and mMRC scores) at enrollment and after 6 months, and the number of COPD exacerbations and exacerbations per patient during the observation period.

Statistical Consideration

The sample size was determined based on feasibility considerations, including the duration of the enrollment period and the number of participating centers. It was estimated that approximately 400 patients can be enrolled over an 8-month period from 40 Italian study centers. Given an expected drop-out rate of approximately 20% over the 6-month observation period, 320 patients were expected to be available for the primary analysis; accordingly, simulations were performed to estimate the achievable precision of the 95% confidence interval (95% CI) of the expected proportions for 320 evaluable patients. This descriptive study had no defined formal hypotheses and no statistical significance testing was performed; data was analyzed using epidemiological methods. Descriptive statistics were provided for all variables and endpoints. All analyses were performed using the SAS software (SAS Institute, Cary, North Carolina, USA).

Results

Participant Disposition and Characteristics

Initially, 385 patients were enrolled in the ASTER study, and 41 of these who did not meet the eligibility criteria were excluded (Figure 1), resulting in 344 (89.4%) eligible patients. Of these eligible patients, 332 (96.5%) completed the study, whereas 12 (3.5%) were lost to follow-up (n = 10) or excluded due to consent withdrawal (n = 2).

Figure 1 Study flowchart. Note: N, total number of patients.

The majority of the eligible patients were men (61.9%) and predominantly Caucasian (98.8%) (Table 1). Most patients had either primary (18.9%) or secondary education (71.7%) and were unemployed or retired (73.6%). At enrollment, 49.3% of the patients were active smokers, 41.7% had previously smoked, and 9.0% had never smoked. The majority of patients (83.7%) had comorbidities at enrollment, with arterial hypertension (58.3%), diabetes (24.0%), and cardiac ischemic disease (16.7%) being the most common (Table 1). Moreover, 50% of patients were regularly treated with ≥3 medications for concomitant diseases. The most prevalent COPD symptoms were cough (82.6%), shortness of breath (66.3%), and phlegm (48.0%). More than half of the patients (54.1%) had mild to moderate dyspnea (mMRC grade ≤2) at enrollment (Table 2). Further, 77.6% of the patients reported experiencing the same COPD symptoms in the year before enrollment as they did at the time of diagnosis.

Table 1 Characteristics of the Patients at Enrollment

Table 2 Disease Characteristics at Enrollment (Eligible Patients)

Of the eligible patients, 196 (57%) were classified as incident patients—symptomatic individuals not previously diagnosed with COPD by spirometry but likely prescribed treatments such as LAMA, LABA, or ICS/LABA by their GP without a confirmed diagnosis—while 148 (43%) were classified as prevalent patients with a prior COPD diagnosis (Table S1). Incident patients had a higher FEV₁ (mean, 2.0 vs 1.7), were slightly younger (mean age, 67.2 vs 69.2 years), were more likely to be employed (25.4% vs 17.4%) and had fewer comorbidities (80.6% vs 87.8%) than prevalent patients. In addition, incident patients experienced severe dyspnea less frequently (mMRC grade ≥2: 40.3% vs 53.4%) and had a lower incidence of poor quality of life (CAT score of 21–30: 14.3% vs.19.6%) than prevalent patients.

Primary Endpoint results

At enrollment, 20.9% of patients were treated with LAMA, 13.7% with an ICS/LABA combination, 2.9% with LABA, and 62.5% (30 prevalent and 185 incident patients) were not receiving any treatment (Figure 2). At the 3-month follow-up, 56.7% of patients were being treated with LABA/LAMA, 21.2% with LAMA, and 12.2% with ICS/LABA (Table S2). At the 6-month follow-up, 53.5% of the patients were being treated with a LABA/LAMA combination, 19.2% with LAMA, and 11.3% with an ICS/LABA combination. Current therapy at 6 months was not notably different between prevalent and incident patients, with the only exception of the ICS/LABA combination was being used more frequently in prevalent patients than in incident patients (16.2% vs 7.7%) (Table S3).

Figure 2 Treatment pattern of COPD medications for eligible patients at enrollment (baseline) and at the 6-month follow-up. Abbreviations: ICS, inhaled corticosteroid; LAMA, long-acting muscarinic antagonist; LABA, long-acting beta-agonist; N, total number of patients at each visit; n = number of patients in each category.

Secondary Endpoint results

Overall, lung function improved over the 6-month observational period, as evidenced by the increase in pre-bronchodilator FEV₁ (Table 3). When the pre-bronchodilator FEV₁ at 6 months was compared with that at enrollment in 206 patients, a mean increase of 140 mL was observed (Table 3), and one-quarter of the patients exhibited an increase of at least 300 mL.

Table 3 Summary of Secondary Endpoint Results for Eligible Patients at Enrollment and at the 6-month Follow-up

At enrollment, 45.9% of patients with COPD reported a significant level of dyspnea, with an mMRC score ≥2. At the 6-month follow-up, this value was reduced to 23.5% (Table 3). The mMRC scores at enrollment and at the 6-month follow-up are summarized in Table S4. Overall, the mMRC score decreased by at least one point in 40% of the patients and remained unchanged in 53.9%.

In terms of the impact of COPD on patients’ lives, the patients’ health status and quality of life improved noticeably over the 6-month study period, as evidenced by a mean decrease of 3.6 points in the CAT score (Table 3). Notably, the CAT score decreased by at least 6 points in one-quarter of the patients.

During the observation period, 3.9% (n = 13/332) of the patients experienced 14 exacerbations (10 mild and 4 moderate), resulting in an approximate annualized exacerbation rate of 7.8%. This represents a meaningful drop from the 23.2% incidence rate in the year before recruitment, indicating an absolute reduction of 15.4% and a 34% reduction in the annualized relative risk over the 6-month period (Table 3). Notably, none of these reported exacerbations required admission to the ER or hospitalization.

Discussion

To the best of our knowledge, the ASTER study was the first COPD study conducted in a GP setting in Italy, providing real-world evidence of clinical practice for patients with COPD managed according to Nota 99, which confers GP with a critical responsibility in the COPD management. The study findings highlight the importance of treatment pathways, and the public health implications of appropriate COPD management.

The findings of the ASTER study provide a detailed overview of the evolving treatment patterns for patients with COPD. Over the course of the study, there was a noticeable shift towards combination therapy, with a substantial number of patients switching from monotherapy with LABA or LAMA to LABA/LAMA combination therapy. Similarly, the majority of patients who were initially treated with ICS/LABA combination therapy, switched to LABA/LAMA therapy. This shift towards LABA/LAMA therapy as well as the observed improvements in the CAT and mMRC scores suggests the effectiveness of these treatments in managing symptoms and improving the quality of life of patients with COPD. These real-world results are consistent with prior randomized controlled studies that demonstrated the effectiveness of LAMA/LABA therapy for patients with COPD.^24,25 Overall, the results of the ASTER study suggest that the implementation of Nota 99 may positively influence clinical practices on COPD treatment. The absence of severe exacerbations requiring hospitalization suggests that enhanced patient management and treatment regimens are effective in preventing severe episodes. This is also consistent with previous research indicating that treatment with LABA/LAMA therapy is more effective than monotherapy in preventing all COPD exacerbations.²⁶ Although no formal hypothesis was established, our findings indicate that intervention according to Nota 99, as implemented in the ASTER study, has the potential to avoid an exacerbation episode for every six patients correctly diagnosed and treated over a year.

The ASTER study emphasizes the pivotal role played by Italian GPs in COPD management. With resources and clear guidelines, GPs can effectively diagnose, treat, and monitor patients with COPD, reducing the disease’s impact and improving long-term outcomes. Continuous training and resources are essential for GPs to provide optimal care. Comparative analyses with practices in other countries^27–29 reveal that Nota 99 promotes structured COPD management, allowing for a proactive rather than reactive strategy by providing clear guidance on the correct diagnostic process, prevention, and management of COPD. According to the ASTER study, 94.4% of newly diagnosed patients with COPD were untreated at the time of enrollment, highlighting that COPD is often overlooked. Improved diagnostic procedures in primary care are essential. Active research and surveillance by GPs can help develop precise diagnostic tools and protocols, ensuring accurate and prompt treatment. Routine case findings and early detection strategies are critical to improve COPD management outcomes.

Although the ASTER study provides valuable insights, it has the following limitations. As a real-life, non-interventional study with prospective data collection, there are inherent biases to consider. Information and selection biases may have influenced the outcome as respondents may have been influenced by GPs or their own beliefs about meeting GP expectations. Additionally, the inclusion criteria required patients to be able to read and write in Italian and fill out questionnaires on their own, which may have disqualified some patients and reduced the generalizability of the findings. Furthermore, GPs’ participation in the trial may have encouraged them to adhere more rigorously to treatment recommendations, potentially diverging from “real-world” treatment practices (known as the Hawthorne effect). However, efforts were made to mitigate these biases, including consecutive patient enrollment and regional diversity in site selection.

The study’s findings may have limited applicability to the broader Italian patient population with COPD because of the study’s recruitment strategies and specific eligibility criteria. Despite the efforts taken to choose locations from various geographic regions and ensure representative sampling, the enrolled patients may not fully represent Italy’s COPD patient community. As a result, the findings should be interpreted with caution, taking into account potential selection bias.

Conclusion

The proactive identification of patients with COPD in a general practice setting may allow for early detection, effective treatment, and better clinical outcomes. In ASTER study, the application of AIFA’s Nota 99, which empowers GPs to initiate the most effective therapy when needed, was associated with meaningful improvements in patient outcomes in this study. This suggests that GPs in Italy should actively identify patients with COPD, especially those who may not pay attention to their symptoms because of lack of awareness. Such a proactive approach could result in earlier interventions, more effective disease management, and, eventually, improved patient outcomes. However, future studies using public health system administrative registries or large clinical databases could confirm the ASTER results, accurately define their dimensions, and reduce potential biases, verifying if the promising outcomes are consistent in general medical practice in Italy.

Abbreviations

AIFA, Italian Medicines Agency; ASTER, Italian observational prospective multicenter study; CAT, COPD Assessment Test; CI, Confidence Interval; COPD, Chronic obstructive pulmonary disease; GPP, Good Pharmacoepidemiology Practice; ER, Emergency room; FEV₁, Forced expiratory volume in 1 second; FVC, Forced vital capacity; GOLD, Global Initiative for Chronic Obstructive Lung Disease; GP, General practitioner; ICS, Inhaled corticosteroids; ISTAT, Italian Institute of Statistics; LABA, Long-acting beta-agonists; LAMA, Long-acting muscarinic antagonists; mMRC, modified Medical Research Council; SABA, Short-acting beta-agonists; SAMA, Short-acting muscarinic antagonists; Nota 99, Italian Medicine Agency’s guideline for managing mild-to-moderate COPD by GPs.

Data Sharing Statement

Anonymized individual participant data and study documents can be requested for further research from https://www.gsk-studyregister.com/en/.

Ethics Approval and Informed Consent

This study complies with the Declaration of Helsinki. Informed consent was obtained before initiating this study. The ethics committee of each participating study center has received approval through the Coordinator Research Ethics Committee (Comitato Etico Lazio 1, Rome, Italy), and the individual study centers have received approval from the following ethical committees: Comitato Etico Interaziendale – ASL Alessandria; Comitato Etico Area Vasta Centro – USL Toscana Centro; Comitato Etico Area Vasta Sud Est – USL Toscana Sud Est; Comitato Etico Interprovinciale Area 1 ASL BT; CESC delle Province di Verona e Rovigo; Comitato Etico di Brescia; CET Regione Abruzzo; Comitato Etico – ARES Sardegna; CET Regionale dell’Umbria; Comitato Etico Regione Marche; Comitato Etico Lazio 1; Comitato Indipendente di Etica Medica – ASL Brindisi; Comitato Etico Regione Calabria Area Centro; Comitato Etico Campania Centro; Comitato Etico di Messina; Comitato Etico Indipendente – ASL Bari; CET Marche – AOU delle Marche.

Acknowledgments

The authors gratefully acknowledge the contributions of all the 30 Italian general practitioners (GPs) who participated as principal investigators and sub-investigators in this study. The authors also extend their sincere thanks to Alessandra Dal Collo for support in administrative matters. The authors would like to express their gratitude to the General Medicines Medical Science Liaisons (MSLs) team for their scientific support provided to the GPs. In addition, the authors thank IQVIA Solutions Italy SRL for their contributions to the conduct of clinical operations, data management, and statistical analysis. The authors also thank Dr. Rakesh Ojha, PhD, a medical writer and an employee of GSK, India, for his manuscript writing and project management support.

Author Contributions

All authors contributed to the study conception or design and/or data analysis and interpretation. All authors were involved in the writing, reviewing, and final approval of the manuscript and agreed to be accountable for all aspects of the work.

Funding

This analysis was funded by GSK (study number 217466). GSK also funded all expenses related to the development and publication of this manuscript.

Disclosure

M.V., C.S., D.C., and B.G. are employees of GSK and hold stock options. G.G. and U.A. have no conflicts of interest to declare. R.P. has received consulting fees from GSK Italy and holds stock options from GSK SpA. The authors report no other conflicts of interest in this work.

References

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Introduction

Nontuberculous mycobacteria (NTM) are environmental pathogens with over 190 identified species,¹ primarily affecting individuals with preexisting lung disease or immunodeficiency.² There is a rising trend in incidence of infection and mortality globally, including among younger populations. Among children, the infection may occur with or without pre-existing lung disease.³ Risk factors among adults have been well studied, but there are fewer pediatric studies in this area of study.⁴ Past research has found that immunodeficiency, the developing immune system, and chronic lung disease heighten the risk of NTM infections in the pediatric population.⁵ NTM infections are especially prevalent because of their ability to inhabit common soil and water sources such as water distribution systems, allowing repeated environmental exposures to invade the lungs through bioaerosols.⁵ It is also thought that different species and subspecies of mycobacteria containing genetic differences manifest themselves differently in infection and clinical response.⁶ For example, two of the most common NTM species Mycobacterium avium and Mycobacterium abscessus differ in their infections with the former being characterized by slow growth, greater general prevalence, and weaker biofilm formation,⁷ while the latter is associated with significant morbidity/mortality, is clinically resistant to most antibiotics, and shows greater general immune responses throughout the course of infection.⁸ Infections from different types of these bacteria can vary by geographic location⁵ with Mycobacterium abscessus being commonly observed in East Asia and Mycobacterium avium complex and Mycobacterium kansasii occurring most commonly in many parts of the US. In Europe, Asia, and partially in Australia, increases in latitude generally see higher rates of Mycobacterium avium complex.

NTM pulmonary disease is frequently misdiagnosed as tuberculosis due to similar symptoms, leading to delays in appropriate treatment. One of the most common signs of NTM infection in healthy children is cervical lymphadenitis, but its clinical presentation is indistinguishable from that of cervical lymphadenitis in regular tuberculosis.⁹ Pulmonary infection, while less commonly associated may be more difficult to treat. This is especially problematic in countries with high tuberculosis rates, where NTM testing methods are resource limited.^10–12 Industrialized nations often report higher NTM incidence than tuberculosis, although pediatric prevalence globally is not well studied.^13,14

This study was conducted to investigate pediatric pulmonary NTM infections among the non-cystic fibrosis population. The global incidence of NTM infections is increasing, and more studies are needed to better understand the disease among the pediatric population.¹⁵ Unlike Cystic Fibrosis patients, who are known to have higher susceptibility to pulmonary infections,¹⁶ the risk factors and clinical manifestations in non-cystic fibrosis pediatric population remains poorly understood. This study aims to investigate the epidemiology, clinical comorbid condition, and five year clinical outcomes of non CF pulmonary NTM infections in distinct pediatric age groups. We hypothesize that there are important differences in these key areas among pediatric age groups. This may lead to the identification of characteristics that could guide clinical decision-making and potentially improve patient outcomes.

The study further hypothesizes that: i) BMI percentiles mediate susceptibility to NTM infections in children, ii) common comorbidities are expected to be significant risk factors for pediatric pulmonary NTM infections, iii) pediatric pulmonary NTM may predispose patients to future lung disease.

Methods

The TriNetX Clinical Data Platform

We utilized the TriNetX research platform to select our patient cohorts from a total of 152,714,105 collected, de-identified patient records on the Global Collaborative Network containing 127 health-care organizations from the following 17 countries: United States, Canada, United Kingdom, Germany, France, Italy, Spain, Netherlands, Denmark, Australia, Singapore, Japan, Brazil, Mexico, Israel, South Korea, and Switzerland. TriNetX deidentifies and aggregates electronic health record (EHR) data from health-care systems, primarily drawing from large academic medical institutions across the USA. It organizes diagnoses under specific ICD codes and stores information such as demographics, medications, lab results, procedures, and vital signs. The platform provides a secure, web-based access to patient-level analyses and interpretation. It reports updated population-level data while ensuring Health Insurance Portability and Accountability Act (HIPAA) compliance.

Data Collection and Stratification

From the TriNetX Global Collaborative Network, the pediatric patient population was stratified into four groups: i) 0–2 years, ii) 3–5 years, iii) 6–12 years, and iv) 13–18 years based on age. The inclusion criteria were: i) subjects with pulmonary NTM infection, and ii) ages 0–18 years. The exclusion criteria were: i) Cystic Fibrosis, ii) Tuberculosis, iii) smoking history, iv) cutaneous non-mycobacterial infections, and v) adult patients. The total cohort (0–18 years) consisted of 2,344 NTM cases from a larger population base of over 23 million pediatric patients collected on July 13, 2024. The distribution among the four groups was expressed both numerically and as a percent of patients in each respective age group with NTM.

Data Analysis

Data analysis focused on patient demographics, clinical characteristics, comorbidities, and outcomes of the 2,344 NTM cases, with particular attention to odds ratios and lab values associated with these infections. Descriptive analysis for this data included prevalence, mean values, and standard deviation within the age cohorts. Demographic information included age, sex, ethnicity, and race. Comorbidities were identified in our analysis. Clinical data were collected including mean values for BMI and oxygen saturation.

Statistical analysis was performed to compare the prevalence and outcomes of NTM across the different age groups, and to evaluate the different comorbidities experienced by each age group. Specifically, the six most common five-year outcomes were compared across the cohorts, assessing risk difference, confidence interval, risk ratio, odds ratio, and statistical significance to determine risk levels between pairs of groups. The analysis was conducted through the built-in analytics tools in the TriNetX software where a p value of <0.05 was defined as statistically significant. Our patients were selected using the inclusion and exclusion criteria outlined in Table 1. Descriptive statistics (mean, SD, proportion) were reported for demographics and clinical features. Comparative analysis across age groups was conducted using Chi-square tests for categorical variables and Student’s t-test or ANOVA for continuous variables. To evaluate associations between NTM and clinical outcomes across age groups, we conducted: Logistic regression analysis for binary outcomes to generate odds ratios (OR), Binomial regression to estimate risk ratios (RR) when appropriate. All models were adjusted for key covariates including age, sex, race, and comorbidity burden. Point estimates were reported with 95% CI.

Table 1 Inclusion and Exclusion Criteria Entered into TriNetX

Ethics

The University of California, Riverside IRB determined that the current study was exempt from further ethics review as it did not fit under the federal definition of human subjects research [DHHS 45 CFR46.102(e), 46.102(l)] or clinical investigation [FDA 21 CFR 50.3(c), 56.102(c)].

Results

Demographics

The demographic data provided insight into the prevalence and distribution of pediatric pulmonary NTM infections among the age cohorts, and with respect to sex, race and ethnicity. The study cohort comprised 2,344 cases stratified across four age groups: 0–2 years, 3–5 years, 6–12 years, and 13–18 years. The majority of NTM cases were observed in the 6–12 year age group at 1074 (734/100,000), followed closely by the 13–18 year age group at 760 (848/100,000). The 3–5 year age group had 401 patients (1261/100,000) and the 0–2 group had 109 patients (689/100,000) (Table 2). There were gender differences in the prevalence of pediatric pulmonary NTM lung infections (Table 3). There was a higher mean prevalence of NTM infections amongfemales compared to males 53.31% vs 46.29% among all age groups. There was no identified gender for 4.17% of the cohort. The majority of the cohort was identified as white 57.8%, followed by Black/African American, 8.88%, Asian, 4.49%, Native American, 2.83%, Native Hawaiian, 1.19%, another race, 8.52%, and unspecified race 21.6%. BMI percentiles were above the 50th percentile for all age groups, and this was statistically significant (p < 0.05) (Table 4).

Table 2 Age Demographics of Our Study Cohort, Including the Number of Patients in Each Age Cohort

Table 3 Sex Demographics of Our Study Cohort, Stratified by Age

Table 4 BMI Percentiles of Each Age Cohort

Laboratory Findings

The data indicated a varying prevalence of NTM infections across the age groups, with the highest prevalence observed in children aged 6–12 years at 1,074 cases (0.007339%). Lab values, particularly those related to immune function, revealed a trend towards higher inflammatory markers among older children, possibly reflecting a more robust immune response or delayed diagnosis. There was an elevated mean white blood cell count in the 13–18 age group at 18.5×10³/μL compared to the expected range of 4.0×10³/μL to 11.0×10³/μL (Table 5). Similarly, CRP mean levels for this age group were significantly above the expected value of 1 mg/L at 26.8 mg/L. In the 3–5 year and 13–18 year cohorts, basophil levels were above the expected 0–1% at 2.68% and 1.83%, respectively. Ferritin levels were higher in the 6–12 year and 13–18 year groups at 546 ng/mL and 1758 ng/mL, respectively, compared to the normal range of 10–200 ng/mL. Among younger children, there were lower levels of inflammatory markers, which may suggest an underdeveloped immune response or early-stage infection.

Table 5 Comparison of Selected Inflammatory Markers by Age Cohort

Comorbid Conditions

Comorbidities such as acute pharyngitis, pneumonia, asthma, malignancy, and immunodeficiency were identified. (Table 6 and Figure 1). The most common comorbidity among the groups was acute pharyngitis in the 13–18 age group at 30% of the cohort. There was a higher burden of NTM infections among the older pediatric group, ages 13 to 18 years. This group also had the highest proportion of each comorbidity: 18% pneumonia, 22% asthma, 12% malignancy, and 11% immunodeficiency. The overall proportion of individuals with comorbidities was lower in the 6–12 year age group, still with acute pharyngitis leading at 19%, pneumonia at 12%, asthma at 13%, malignancy at 6%, and immunodeficiency at 7%. The 3–5 year old cohort had the following comorbid conditions: 11% acute pharyngitis, 7% pneumonia, 11% asthma, 5% malignancy, and 4% immunodeficiency. The 0–2 age groups saw a notable increase in acute pharyngitis, pneumonia, and malignancy at 18%, 9%, and 13%, respectively. The age 0–2 year old group had the highest percent of malignancy. Asthma and immunodeficiency data was not recorded for this age group likely due to reduced diagnosis of these issues at such ages. The most prevalent comorbidity was acute pharyngitis at 78% across all age groups, and no other comorbidity rose above 46%.

Table 6 Comparison of the Top Five Most Common Comorbidities by Age Cohort

Figure 1 The five most common comorbidities associated with NTM infection as a percent of each age cohort. *indicates P < 0.05.

Discussion

The findings of this study demonstrates an age-based differential presentation of Pulmonary NTM infection among the cohort studied. This study highlights the need for age-specific approaches to the diagnosis and management of pediatric pulmonary NTM infections. Our study adds novel data regarding the global prevalence of NTM pulmonary infections among a Pediatric population. The varying prevalence and outcomes across age groups suggest that different factors, such as immune development and environmental exposure, play significant roles in the susceptibility to and progression of these infections. It is important to understand the mechanisms of these differences. Further studies will be completed to better understand the age based differential of disease prevalence.¹⁷ The top five comorbidities we identified were acute pharyngitis, unspecified pneumonia, asthma, malignancy and immunodeficiency. The presence of these comorbidities also correlated with a higher burden of NTM infection in the older pediatric groups, suggesting that these children might have prolonged exposure to risk factors or delayed diagnosis.¹⁸ This increased susceptibility is thought to be related to damaged respiratory mucosa, promoting NTM attachment and infection.¹⁹ Our observed comorbidity of asthma drew our attention to the fact that children with underlying chronic lung diseases may have a higher likelihood of severe outcomes, including prolonged hospitalizations and the need for intensive care.²⁰ There was a higher odds ratio (OR) of developing more severe lung disease, such as pneumonia, pulmonary fibrosis, lung abscess, bronchiectasis, interstitial lung disease upon NTM infection among 0–2 year olds compared to older age groups (Table 7). This is also supported by several reports of some of these outcomes occurring in various age groups.^21,22 Immunodeficiency, whether congenital or acquired, tends to be associated with a more complicated clinical course and higher mortality rates, and we also observed it as a comorbidity.²³ Specifically, this immunosuppression allows for extrapulmonary NTM disease to develop and spread by escaping the body’s immune cells and entering the lymphatic system and bloodstream.²⁴ The finding of lung or mediastinal abscess as a five year outcome heightens our clinical concern among this cohort, especially when considering clinical uncertainty in treatment of NTM.²⁵ The analysis of racial and ethnic data was limited in this study due to potential incomplete availability of documentation of some demographics in the electronic health records, which may have introduced bias in the interpretation of these variables. However, our demographic data supports our initial hypothesis that even a mildly elevated BMI may increase susceptibility for pediatric NTM.²⁶ While current literature supports lower BMI as a significant risk factor for NTM infection,²⁷ one possible explanation is the susceptibility of the host in states of malnutrition. Individuals with higher BMI are more likely to be diagnosed with chronic inflammatory or autoimmune conditions²⁸ which are commonly treated using immunosuppressive corticosteroids,²⁹ potentially increasing the risk for NTM infection.^30,31 In addition to BMI, other physical conditions and comorbidities, particularly chronic lung diseases and immunodeficiency, underscore the need for vigilant screening and management in at-risk populations. The significant odds ratios associated with future 5-year outcomes in older children (Table 7) suggest that more aggressive diagnostic and therapeutic strategies may be warranted in these age groups.³² Our findings show that the majority of NTM cases were observed in the 6–12 year age group and the highest percent of cases were observed in the 3–5 year age group. This finding shows that school-aged children and adolescents are more likely to be diagnosed with NTM infections, potentially due to increased exposure to environmental reservoirs of NTM, such as water and soil, and higher rates of outdoor activities compared to toddlers.³³ Abnormal inflammatory marker values among the 13–18 age group were also a notable age cohort-based finding. Particularly, the elevated white blood cell count, ferritin levels, and CRP in older children suggest a more pronounced inflammatory response in the adolescent age group with NTM infections. These findings are distinct from the data that has been found from a previous study done in Wisconsin. In the Wisconsin study, the isolates were based on statewide data, rather than a global database, and this study included non Pulmonary infections. Our study is the first to our knowledge to analyze a global database, with a focus on Pediatric non CF Pulmonary infections. The data in the Wisconsin study included Cystic Fibrosis patients. Since CF is a known risk factor for Pulmonary NTM, our study design focused on non CF patients.³² From our demographic data, we also found that there were significant differences in the gender of infected patients. This trend aligns with existing adult literature that suggests females may have a higher susceptibility to pulmonary infections due to differences in immune response, hormonal influences, or behavioral factors, such as a greater likelihood of engaging in activities that increase exposure to NTM.³⁴ There were statistically significant differences in NTM infection rates among different racial groups, which has to our knowledge not been studied utilizing a global EHR database. The available data from diverse populations shows that certain racial and ethnic groups might be disproportionately affected by NTM infections, potentially due to disparities in healthcare access, environmental exposures, or genetic predispositions.³⁵ This finding highlights the need for more comprehensive data collection and medical record documentation in future studies to better understand the role of race and ethnicity in the epidemiology of pediatric pulmonary NTM infections.

Table 7 Pulmonary Health Outcomes by Age Cohort

Limitations

This study has several limitations, including its retrospective nature and reliance on data from the TriNetX platform, which may introduce selection bias. Additionally, the reliance on electronic health record data may result in incomplete or inaccurate documentation of clinical characteristics and outcomes. The platform’s retrospective nature may introduce selection bias, affecting the generalizability of the results. Additionally, differences in cohort sizes can impact comparison results. TriNetX also anonymizes data by replacing counts of 1–10 with “10” to protect patient privacy, which limits the statistical analysis of rare outcomes. Furthermore, patients who move to non-TriNetX facilities are lost and not included in the outcome comparisons. There may also be comorbidities that were documented in patients’ records in institutions not participating in TriNetX. The study also did not account for the potential impact of socio-economic factors, geographic location, or variations in healthcare access, which could influence the prevalence and outcomes of NTM infections.^36,37 Despite these limitations, our study has provided an extensive analysis of global data in this area of study, utilizing a novel EHR based database.^38,39 Additionally, more advocacy is needed to ensure that NTM is properly documented, species-wise in the EHR to ensure that future epidemiological studies can be done effectively.

Conclusions

Our study uniquely provides an analysis of global prevalence of non CF Pediatric Pulmonary NTM infections. The study contributes to current knowledge in the field and identifies selected future five year outcomes. We also compared the associated risk for the specific age cohorts studied. This study thus adds to current understanding of the incidence and characteristics of Pediatric NTM non CF pulmonary disease. Future studies to develop treatment strategies and age based considerations are therefore important in the management of Pediatric pulmonary NTM infection.

Disclosure

The authors report no conflicts of interest in this work.

References

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Introduction

The circadian system is the body’s internal clock that regulates sleep-wake cycles, hormone secretion, and metabolism. Several lifestyle behaviors have been attributed to the disruption of this system including shift work, jet lag, irregular sleep patterns, and exposure to artificial lights at night.¹ The interplay between circadian disruptions and metabolic dysregulation highlights the complicated relationship between sleep patterns (quantity and quality of sleep) and metabolic balance, specifically glucose metabolism. Evidence from primary studies shows a positive relationship between circadian disturbances and glucose intolerance, obesity, and insulin resistance leading to the development of type 2 diabetes (T2DM).^2–6 In Qatar’s population, preliminary evidence supported a positive association between short sleep duration and poor self-rated health, obesity, and chronic illness independent of age, gender, or social class.⁷

Metabolic syndrome (MetS) – a marker of metabolic dysfunction – includes obesity, glucose intolerance, dyslipidemia and hypertension, and insulin resistance is strongly associated with an elevated risk of T2DM, cardiovascular diseases (CVDs), mood disturbances, and mortality.⁸ The metabolic abnormalities in MetS such as insulin resistance and glucose intolerance further exacerbate hyperglycemia and T2DM complications, and once T2DM develops, it often worsens other components of MetS, such as lipid profile, creating a vicious cycle that amplifies CVDs risk.⁹ According to the American Heart Association (AHA), MetS is defined by the presence of three or more of the following: fasting blood glucose ≥ 100 mg/dL; waist circumference ≥ 88 cm for women and ≥ 102 cm for men; elevated blood pressure represented by systolic pressure of ≥130 and/or diastolic of ≥85 mm Hg and/or use of antihypertensives; HDL cholesterol < 40 mg/dL for men and < 50 mg/dL for women; and triglycerides ≥ 150 mg/dL. The Joint Interim Statement definition (JIS) definition for MetS is identical to the AHA components, but considers patients on glucose control medication, or LDL lowering or HDL raising medicines as alternative in diagnosis to their corresponding laboratories abnormalities.¹⁰ Furthermore, due to lifestyle changes, in 2022, the prevalence of MetS worldwide was around 31.4% based on the JIS and the incidence of MetS is increasing particularly in the Middle East region, where prevalence estimates are as high as 34% according to the JIS criteria.¹¹ The consequences of MetS are not limited to CVDs, with growing evidence of its association with mood disorders such as depression according to several meta-analyses.^12–14

In contrast to MetS, Circadian Metabolic Syndrome or Circadian Syndrome (CRD), is a relatively new concept, which has been described in some primary studies.^15–17 However, no clear definition exists. Given the close relationship between the circadian and metabolic systems, some authors proposed to incorporate depression, metabolic dysfunction-associated steatotic liver disease (MASLD), and sleep-wake cycle disruptions into the definition for this syndrome.¹⁷

Evidence from the National Health and Nutrition Examination Survey, a study based in the United States, showed high odds of CVD mortality in participants with MetS and CRD. However, the odds of mortality were higher in individuals with CRD than MetS.¹⁵ There is limited evidence from Middle Eastern populations regarding the association between CRD, MetS, and CVDs.

Our study aimed to estimate the prevalence of CRD and MetS and identify their potential risk factors. Additionally, due to Qatar’s distinctive demographic composition and lifestyle characteristics, including potentially high rates of shift work and widespread use of artificial lighting, it was pertinent to investigate the associations of CRD and MetS with T2DM and CVDs to delineate primary and secondary prevention pathways and guide relevant public health initiatives and policy adjustments.

Methods

Study Design

A cross-sectional study was conducted between March and April of 2018. The interview questionnaire was designed to assess health and diabetes mellitus awareness in Qatar among its citizens and expatriates.

A stratified probability-based sample was drawn from a cellphone frame prepared by the Social and Economic Survey Research Institute (SESRI) with the help of the main telecommunication providers in Qatar. The sample was representative of the three main groups within Qatar’s resident population (Qatari nationals, white-collar expatriates (WCE), and blue-collar expatriates (BCE)).¹⁸ A total of 10,579 phone interviews were attempted, of which 5872 constituted our target population of adults who were 18 years or older and living in Qatar at the time of the study. Up to seven call attempts were made to complete the interview for each participant. After removing “hard refusals” and numbers that were difficult to reach, a total of 2560 interviews were completed giving an overall response rate of 43.6%. After data cleaning (N = 2523) and removing cases for which MetS or CRD status could not be determined (n=191), a final sample of 2332 was retained for the analysis.

Interview Questionnaire Development

The interview questions were initially developed in English and then translated into Arabic and other languages (Urdu, Hindi, Malayalam, Nepalese, Bengali and Tamil) by professional translators. The translated version was carefully checked by bilingual researchers using forward-backward translation. The interview questionnaire was programmed into the CATI (Computer Assisted Telephone Interview) system using Blaise software. A pre-test was run on a small number of cellphone units, which informed the further refinement of the questionnaire before study fielding.

Interview Administration

Telephone interviews were conducted by a team of experienced interviewers and supervisors at Social and Economic Survey Research Institute (SESRI). The phone calls were allocated over times of day and days of the week to maximize the chances of contacting respondents. The interviews took on average between 20 and 30 minutes to complete.

Data Management

After the data collection, all individual interviews were merged and saved in a single Blaise data file. This dataset was cleaned, coded, and weighted to account for complex sample design including sampling selection probability, non-response, and calibration to align estimates with known population characteristics available from the census bureau.¹⁹

Main Study Measures and Outcomes

International guideline consider individuals having three or more of the following criteria as having MetS: high cholesterol levels, high triglyceride levels, high waist circumference (> 85cm), prediabetes, and raised blood pressure (DBP ≥ 85mmHg or SBP ≥ 130mmHg).¹⁰

All variables used to ascertain the abovementioned criteria for MetS and define our main outcomes T2DM and CVDs were defined based on yes responses to a series of questions prefaced by “have you ever been told by a health professional such as a nurse or a doctor that you have any of the following health conditions?”… the list included: “Cardiovascular or heart disease?”, “Diabetes mellitus?”, “Hypertension or high blood pressure?”, “high cholesterol?”, “high triglycerides?”, among other chronic health conditions. For example, if they said yes to any of these questions, then they were considered positive for that criterion of MeTS. The exact wording for survey questions used to define our main exposures and outcomes appear in Appendix A.

Of those who reported having diabetes mellitus, they were further asked if they knew which type of diabetes they had (type 1 or T2DM).

Depression was defined as having at least a total score of 3 out of 6 on the two-item or Patient Health Questionnaire 2 (PHQ-2) in the past two weeks.²⁰ The number of hours of sleep on a typical night were used to define average duration of sleep for each participant in the survey.

CRD was defined based on criteria adapted from previous studies [14, 15] of either (i) short sleep duration (≤ 6 hours per night) and MetS or (ii) short sleep duration, depressive symptoms ((PHQ-2) score ≥ 3) and two components of MetS.

Current smoking status was assessed by the following question: “At the present time, do you smoke cigarettes daily, occasionally or not at all?”, for which, responses were grouped into current versus non-current smoker (never and former smoker status).

Other covariates were age, gender, income status, marital status, and education level. Age was recorded as a date of birth, which was subtracted from the interview year (2018) to derive an estimate of actual age in years. The respondent type was used as a proxy for socioeconomic status in our study, reflecting three main working classes in Qatar: Qatari citizens, white-collar or blue-collar expatriates. Respondent type was ascertained by asking a series of questions about their nationality and per month salary ranges in Qatari Riyals (QAR): 1) Qataris (less than 30K, 30K to 50K, 50K to 70K, or 70K QAR or more); 2) white-collar expats (4K to 10K, 10K to 15K, 15K to 25K, 25K QAR or more); and 3) blue-collar expats (Less than 1K, 1K to 1.4K QR, 1.4K to 1.8K QR, and 1.8K to 4K QAR).¹⁸ For marital status, respondents were categorized into two categories based on their status: 1) married and 2) not married, which included separated, divorced, widowed and never married. Education level was divided into school education (Primary (1–6), Preparatory (7–9), Vocational (After Preparatory, but not Secondary), and Secondary (10–12)), university education (Diploma, Bachelor of Arts (BA), Bachelor of Commerce (BCOM), Bachelor of Science (BSC), Master’s degree, and Doctor of Philosophy (PHD)), never attending school, and other.

Data Analysis

Our statistical analysis was conducted using Stata software (version 18) (StataCorp LLC, College Station, TX, USA). Descriptive statistics, bivariable comparisons, and multivariable logistic regression were carried out. To account for complex sampling design in our estimates, we applied sampling weights using the survey (svy) commands in Stata. For our descriptive results, we reported frequency in total sample of each variable. For bivariable results, weighted proportions of each of our exposure groups (MetS, CRD, no MetS or CRD) were reported with corresponding 95% confidence intervals (CI) and compared across different levels of sociodemographic and health characteristics. The F-transformed version of the Pearson Chi-squared statistics were used to generate the corresponding p-values for these comparisons. Multivariable logistic regression models for estimating the associations between a set of different risk factors and each of our main exposure variables (MetS, CRD), with sampling weights, were fit to data. We then used this minimum set of confounders to estimate the main effects of each independent variable (MetS, CRD) on CVDs and T2DM in lieu of a causal model rather than fitting predictive models for these outcomes.²¹ The overall model fit for these logistic regression models was assessed using the log-likelihood ratio, the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Model misclassification was assessed using the area under the curve (AUC) method.

Ethical Considerations

The ethics approval for the study was granted by the Ministry of Health (MOPH-A-QDA-007) and our university’s ethics committee (QU-IRB 882-E 2018). We conducted a minimal-risk phone survey interview, which qualified for a verbal consent process over the phone. Our institutional review board (IRB) reviewed and approved our interview questionnaire and the phone script that addressed key elements of informed consent. Each participant was allocated a unique case number at the beginning of the interview. The data was anonymously stored on a highly secure university server.

Results

Characteristics of Participants and the Prevalence of MetS and CRD

As shown in Table 1, the total sample of participants that completed the interview was 2332. The mean age of the participants was 37.1 years (SD = 11.2), 637 (27.3%) were women, and 754 (32.3%) were Qataris.

Table 1 Sample Characteristics by Metabolic Syndrome (MetS) and Circadian Syndrome (CRD)

The overall prevalence of MetS and CRD was 6.8% (95% CI: 5.4–8.5) and 2.4% (95% CI: 1.7–3.4), respectively. MetS and CRD were more prevalent in the population above 40 years of age (15.1% and 5.8%, respectively. P≤0.001). Overall, MetS was more prevalent in men (7.0%) compared to women (5.8%); CRD showed a different pattern with higher prevalence in women (4.4%) than men (2.4%), however these differences were not statistically significant (p=0.2954). MetS was more common among married (8.3%) participants than CRD (3.2%) with p-value of 0.0013. In addition, being a smoker was more prevalent among those with MetS (7.0%) than CRD (1.2%) (p=0.1005) (Table 1). CVDs were more prevalent among those with MetS (26.5%) than those with CRD (14.5%) (Table 1).

Risk Factors for MetS and CRD

Unitary increases in age were associated with both MetS and CRD with OR of 1.06 in both being statistically significant (p<0.001) (Table 2). Socioeconomic status was negatively associated with both MetS and CRD. Compared to Qataris, WCE had lower odds of MetS (OR = 0.56, 95% CI 0.40–0.79) and CRD (OR = 0.57, 95% CI: 0.32–0.99), respectively. Similarly, BCE had lower odds of MetS (OR = 0.32, 95% CI 0.23–0.58, p<0.001). However, association between BCE and CRD (OR = 0.46, 95% CI: 0.20–1.05) were not statistically significant (p=0.066). As shown in Table 2, there was a statistically significant association between currently married compared unmarried participants and MetS (OR = 2.39, 95% CI: 1.56–3.66, p<0.001), but not with CRD (OR=1.44, 95% CI: 0.74–2.79, p= 0.270).

Table 2 Risk Factors for Metabolic Syndrome (MetS) and Circadian Syndrome (CRD) From Multivariable Regression Models

MetS and CRD as Risk Factors for T2DM and CVDs

As shown in Table 3, both MetS (OR = 5.51, 95%: CI 2.33–13.03) and CRD (OR = 2.01, 95% CI: 0.92–4.42) had increased odds of CVDs, however, CRD results did not demonstrate statistical significance (p=0.080). Additionally, MetS showed had higher odds of T2DM (OR = 19.08, 95% CI: 10.87–33.50) compared to CRD (OR = 10.32, 95% CI: 4.60–23.2).

Table 3 Associations Between Cardiovascular Diseases (CVDs) and Type 2 Diabetes (T2DM) and Both Metabolic Syndrome (MetS) and Circadian Syndrome (CRD)

The AUC values (MetS = 0.764 vs CRD = 0.745) for models shown in Table 3 and their corresponding ROC curves indicated that models for MetS had marginally better predictive capacity for CVDs than CRD models (Figure 1). For MetS, AIC and BIC values were 614.97 and 661.17, respectively. For CRD, the AIC was 632.50 and BIC was 678.70, respectively.

Figure 1 Receiver Operator Curves (ROCs) for models with Metabolic Syndrome (MetS) and Circadian Syndrome (CRD) Regressed on Cardiovascular Diseases (CVDs). The figure on the left (A) shows the area under the curve (AUC) for the receiver operating curve (ROC) of the multivariable model with MetS regressed on CVDs. The figure on the right (B) shows the AUC of the ROC for the multivariable model of CRD regressed on CVDs.

The AUC values (MetS = 0.8475 vs CRD = 0.7943) for models shown in Table 3 and their corresponding ROC curves are shown in Figure 2. The AUC values suggested that MetS may have better predictive capacity for T2DM than CRD models (Figure 2). For MetS, AIC and BIC values were 1237.012 and 1283.22, respectively. For CRD, the AIC was 1407.87 and BIC was 1454.07, respectively.

Figure 2 Receiver Operator Curves (ROC) for Models of Metabolic Syndrome and Circadian Syndrome Regressed on Type 2 Diabetes (T2DM). The figure on the left (A) shows the AUC for the ROC of the multivariable model with MetS regressed on CVDs. The figure on the right (B) shows the area under the AUC of the ROC of the multivariable model of CRD regressed on CVDs.

Discussion

In this study, we compared how MetS and CRD were associated with CVDs and T2DM in a representative sample of the general population of Qatar while also controlling for important sociodemographic factors. Notably, both syndromes exhibited significant associations with both outcomes, with MetS potentially showing stronger and more predictive associations than CRD for both of these outcomes.

In alignment with the literature, MetS also showed a strong association with CVDs in our study. This is not surprising given that different components of MetS are also part of the pathophysiological process leading to CVDs.²² Evidence suggests that impaired glucose regulation,²³ obesity,²⁴ insulin resistance,²⁵ dyslipidemia,^26,27 and hypertension²⁸ were contributing risk factors for CVDs. Moreover, this finding can also be explained through the mechanism of chronic inflammation markers. Previous research indicated that MetS was associated with markers of mild and chronic inflammation (eg, high levels of fibrinogen, C-reactive protein, and Erythrocyte Sedimentation Rate) typically associated with insulin resistance.²² Normally, insulin enhances albumin synthesis and reduces fibrinogen synthesis in the liver, contrasting with the acute-phase response. However, decreased insulin sensitivity, as seen in MetS, may potentially disrupt this balance, leading to increased production of acute-phase proteins like fibrinogen and C-reactive protein, previously shown to be potential predictors of CVDs.²⁹

In our study, CRD was associated with double the odds of CVDs in our sample of Qatar’s general population emphasizing the importance of circadian disruptions in influencing cardiovascular outcomes. This is consistent with findings from two previous cross-sectional studies reporting that CRD may also be an important predictor for CVDs.^15,16 Previous studies also supported that circadian rhythms play an important role in regulating multiple physiological processes including blood pressure, endothelial function and lipid metabolism, all of which are key determinants of the risk of CVDs.^30,31 Adverse cardiovascular consequences of circadian misalignment were also reported in individuals with irregular sleep patterns or shift work schedules.^31,32

In addition to individuals who have shift work schedules or irregular sleeping habits, vitamin D deficiency was also reported to be associated with an increased risk of developing CRD. A recently conducted study reported a two-fold increase in the odds of CRD as a result of vitamin D deficiency.³³ Given the high prevalence of vitamin D deficiency in Qatar,³⁴ vitamin D may also be an important contributor to the underlying mechanism of circadian rhythm disruptions and cardiometabolic outcomes in Qatar and the region.

However, there are some conflicting findings concerning the relationship between CRD and CVD risk suggesting that circadian disruptions may have variable effects depending on population characteristics and specific conditions.^30,31,35 In our study, MetS emerged as a marginally superior predictor of CVDs compared to CRD. This is in contrast to findings from prior investigations involving American and Chinese populations, which concluded that CRD may have higher predictive power for CVDs with ORs of 2.92 (95% CI: 2.21–3.86) and 2.83 (95% CI: 2.33–3.43), respectively. We suspect that the superiority in AUCs for MetS in our findings compared to these two previous studies may arise from differences in how CRD was assessed. Specifically, our data for the main components of CRD and MetS were self-reported and based on questionnaires, whereas objective measurements of waist circumference and serum levels of lipid parameters were carried out in these other studies.

Even though both MetS and CRD were associated with T2DM status in Qatar, our findings suggested that MetS maybe potential better predictor of T2DM than CRD in the general population. However, future prospective studies may better disentangle the predictive performance of the two syndromes in relation to these outcomes. Some researchers claim that CRD includes key etiological components, such as sleep disturbance and depression that explain the clustering of cardiometabolic risk factors, illnesses, and associated comorbidities, while MetS does not fully address these factors. For example, Hidese et al explored the association between depression and MetS, their findings emphasized that depression, a component of CRD, can significantly influence cardiometabolic outcomes, highlighting the intricate interplay between psychiatric factors and metabolic health.³⁶ As more evidence emerges linking these cardiometabolic risk factors and comorbidities to circadian rhythm disorders, it seems that most cluster components share the same etiology highlighting the important role of the body’s circadian cycle. Thus, it is reasonable to propose that circadian disruption may be driving this commonly seen cluster of risk factors and disorders, including T2DM.¹⁷ In contrast, MetS directly measures physiological changes that impact conditions such as T2DM, circadian disturbances may have a cumulative rather than an immediate effect on health, which over time could contribute to the development of multiple chronic conditions including T2DM.³⁷

Our study had many limitations. Firstly, given the cross-sectional study design, causal relationships between MetS and CRD with both T2DM and CVDs cannot be established. Secondly, self-reported data might have introduced random and systematic errors (recall bias and other misclassification bias). However, data collected based on self-report offer a cost-effective way of measuring and tracking the studied phenomena at the population level. Thirdly, the lack of data on one of CRD components,¹⁷ non-alcoholic fatty liver disease, could have led to the potential exclusion of patients with CRD from our study. Consequently, it is possible that we were not able to capture the full spectrum of individuals affected by CRD, thus limiting the generalizability of the study’s findings. Moreover, small sample size, especially after adjusting the confounders results in imprecise estimates, which can be mitigated by future studies with larger sample sizes. Finally, the underreporting of depressive symptoms due to stigma³⁸ may have contributed to the underestimation of the rates of CRD in our sample.

To the best of our knowledge, this is the first study in Qatar and the Middle East to assess the association between CRD and MetS in relation to CVDs and T2DM. Our study is one of the few studies worldwide that investigated CRD as an independent entity from MetS. Moreover, our study had a large (N= 2332) and representative sample of the general population in Qatar. We used a robust analytical approach with relevant sociodemographic and health-related variables to provide contextual insights into the prevalence and associations of MetS and CRD with important health outcomes for informing targeted public health interventions. Future studies can validate our findings in other populations using objective measures and prospective study design.

Conclusions

The prevalence of MetS in Qatar’s general population was higher than that of CRD. Both MetS and CRD were positively associated with CVDs and T2DM in a representative sample of Qatar’s population. Compared to Qataris, blue-collar expatriates had lower odds of MetS and CRD as such long-term screening and education programs should focus on Qatari nationals. The present study is exploratory yet paves the way for more studies in the Middle East and other regions of the world to better understand the pathophysiology of CRD, MetS and their associations with CVDs and T2DM for early screening and prevention against its complications.

What This Study Adds?

• Both MetS and CRD were associated with type 2 diabetes (T2DM) and cardiovascular diseases (CVDs) in Qatar.
• MetS showed potentially stronger associations than CRD for both T2DM and CVDs.

Implications for Policy and Practice

• Our findings suggest the need for early screening and public education of known risk factors of MetS and CRD.
• Further prospective research is needed for predictive utility of CRD compared with MetS for T2DM and CVDs in different populations including the Middle East.

Data Sharing Statement

Data are available upon request from the corresponding author.

Statements of Ethical Approval

This study was granted ethical approvals from the Ministry of Health (MOPH-A-QDA-007) and the ethics committee at Qatar University (QU-IRB 882-E 2018). The study complies with the Declaration of Helsinki.

Acknowledgments

The abstract of this paper was presented at the 6th Qatar International Internal Medicine (6-QIIM) Conference 2025 as a poster presentation with interim findings. The poster’s abstract was uploaded in ResearchGate, accessed through the following link: http://dx.doi.org/10.13140/RG.2.2.22752.60165.

Funding

Qatar Diabetes Association sponsored the study. Open Access funding provided by the Qatar National Library.

Disclosure

All authors declare no conflicts of interest in this work.

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A team of scientists was crunching Danish health registry data several years ago when it noticed something surprising: Diabetes patients who’d used Novo Nordisk A/S’s last-generation diabetes medicine Victoza or similar GLP-1 drugs appeared to be getting dementia at noticeably lower rates than those treating their diabetes another way. Specifically, adults who’d been taking the injectable for two years had about a 20% lower risk of a dementia diagnosis. “That is in and of itself not proof,” says Martin Holst Lange, the drugmaker’s chief scientific officer. But “it did catch our attention.” Novo was already looking into whether its newer GLP-1 drugs could help patients with obesity-adjacent disorders of the heart, liver and joints. Spurred in part by the analysis of the curious Danish data (which Novo helped publish), it decided to test the drugs’ effects on Alzheimer’s disease as well.

Those trial results are due out this fall, and if they show what the company hopes they do, GLP-1s could revolutionize the treatment of Alzheimer’s, the most common form of dementia. It’s in no way a sure thing, but if it works, the payoff could be huge. Analysts at UBS Group AG estimate a 1 in 10 chance that the company is able to generate an additional $15 billion in annual sales for Alzheimer’s treatment. “We are excited about this,” Novo’s Lange says. “We also see it as very, very high risk.”

The Africa Centres for Disease Control and Prevention (Africa CDC), under its mandate as the specialized technical health agency of the African Union Commission, continues to lead continental efforts to mitigate antimicrobial resistance (AMR).

As part of this effort, the Africa CDC initiated peer to peer learning exchange among Africa Union Member States to advance surveillance capacity and shaping continental objectives to address AMR.

The most recent exchange visit brought technical experts from South Sudan’s line ministries for a learning exchange at the National Institute for Communicable Diseases (NICD) in South Africa’s Healthcare-Associated Infections, Antimicrobial Resistance and Mycoses (CHARM) department.

The visit focused on strengthening South Sudan’s national AMR surveillance system by providing practical exposure to best practices in laboratory systems, data management tools, and regional coordination mechanisms.

“This visit marks a significant milestone for South Sudan as we plan to apply this knowledge to our own context in setting up our national AMR surveillance plan,” said Dr. Abe Abias, Head of the AMR Program in South Sudan.

He noted that South Sudan has a strong One Health multisectoral coordination mechanism, and the learning experience was important to begin AMR data collection in South Sudan.

Fowzia Mohamed Sheikh, AMR Officer, Eastern RCC explained thatSouth Sudan is just starting their AMR surveillance initiatives, and this visit was to learn from South Africa’s well-established surveillance system. . These were some of the clear outcomes from this visit:

Sheikh said a strong partnership between the two countries is expected, as South African experts will be supporting the country in reviewing their National AMR Surveillance Plan once drafted.

“There is clear guidance and understanding on implementing standardized AMR surveillance systems at national, sub-national, and facility levels,” she said adding enhanced knowledge and technical skills among key staff to support AMR surveillance program implementation in South Sudan.

The  visit will lead to Development of a context-specific National AMR Surveillance Plan to guide the phased implementation and scale-up of surveillance efforts across South Sudan, Sheikh said.

South Africa’s NICD continues to play a leadership role in the region and serves as a hub for collaborative learning. During the five-day visit, the South Sudanese delegation toured NICD laboratories, observed antimicrobial susceptibility testing procedures, reviewed quality assurance protocols, and explored strategies for integrating AMR surveillance data into national health systems.

The delegation also received technical discussions on the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) framework, along with insights into Africa CDC- and Fleming Fund-supported initiatives.

“NICD will continue to support South Sudan and other AU Member States through similar exchange visits through the collaboration with Africa CDC,” said Professor Olga Perovic, Principal Pathologist at NICD-CHARM.

Africa CDC has supported South Sudan in developing its National Action Plan on AMR  in 2023. The Africa CDC’s AMR Surveillance Exchange Visit Initiative was launched in response to the growing threat of AMR in Africa, where nearly 1.3 million deaths were attributed to AMR in 2019. Despite this burden, surveillance infrastructure remains limited in many AU Member States. This initiative forms part of a broader strategy to strengthen AMR surveillance systems across the continent .

Surveillance is critical for understanding AMR trends and pathogen patterns and Africa experiences patchy and fragmented surveillance systems as these are not well coordinated at National Level particularly in low-income countries.

Africa CDC remains committed to engaging AU Member States on AMR surveillance and to supporting initiatives that promote excellence in AMR prevention, containment, and control across the continent. These international exchange visits are a key component of efforts to build technical capacity and leverage existing resources in the fight against AMR in Africa.

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