Depression, a pervasive mood disorder affecting millions globally, has long been epidemiologically associated with smoking. However, the biological underpinnings of this association have remained elusive. Researchers have hypothesized that systemic inflammation and metabolic insulin resistance might be pivotal mediators in this complex interplay, but definitive population-based evidence was lacking until now. The current investigation delves deeply into this hypothesis by examining specific biomarkers reflective of immune-inflammatory status and metabolic health.

The research harnessed robust data from 15,391 adults surveyed in NHANES—the premier health assessment program in the United States—yielding insights that extend to approximately 92 million Americans. Depressive symptoms were quantified using the clinically validated Patient Health Questionnaire-9 (PHQ-9), while smoking status was meticulously categorized through detailed questionnaires. Crucially, two objective biological indices were evaluated: the Systemic Immune-Inflammation Index (SII) and the metabolic insulin resistance score (METS-IR), both serving as proxies for systemic inflammation and insulin sensitivity, respectively.

Statistical analyses revealed that current smokers are over three times more likely to exhibit depressive symptoms compared to individuals who have never smoked, underscoring the strong epidemiological tie between smoking and mood disorders. This association persisted consistently across demographic subgroups, highlighting its broad relevance. The study employed weighted logistic regression models, controlling for multiple potential confounders, to solidify these findings and illuminate the scale of impact smoking exerts on mental health.

The investigation went a step further by examining how smoking modulates systemic inflammation and metabolic function. Results demonstrated that current smokers tend to have significantly elevated SII and METS-IR levels, with smoking contributing an increase of approximately 86.1 units in SII and a measurable uptick in metabolic insulin resistance. These shifts in immune and metabolic markers elucidate potential biological mechanisms by which smoking might precipitate or exacerbate depressive symptoms.

Intriguingly, dose-response relationships between these biomarkers and depressive symptom severity were non-linear, as shown by restricted cubic spline models. This complexity suggests that the pathophysiological consequences of inflammation and insulin resistance on mood do not simply scale in a straightforward manner but may involve threshold effects or saturation points. Such nonlinear dynamics challenge simplistic models of causation and beckon further mechanistic research.

Crucially, mediation analyses pinpointed the relative contribution of inflammation and metabolic dysfunction to the smoking-depression nexus. SII and METS-IR were found to mediate approximately 0.69% and 0.86%, respectively, of the association between smoking behavior and depressive symptoms. Although seemingly modest, these mediating effects are biologically meaningful and highlight the multifactorial nature of depression’s pathogenesis among smokers, where inflammation and metabolic derangement constitute just pieces of a larger puzzle.

The public health implications of these findings cannot be overstated. The dual role of smoking as a direct risk factor for depression and an inducer of detrimental biological states accentuates the urgency of integrative cessation programs. Such initiatives could not only curb the incidence of mood disorders but may also ameliorate the inflammatory and metabolic disturbances that compound mental health challenges.

Moreover, the research explored the interplay between smoking, depressive symptoms, and overall mortality risk. Smokers with depressive symptomatology exhibited elevated all-cause mortality rates, underscoring the compounded health risks faced by this vulnerable population. This association signifies that tackling smoking within psychiatric care paradigms could confer survival benefits alongside psychological relief.

These findings enrich the growing body of literature elucidating the biological links between lifestyle factors like smoking and mental health. By adopting a multidimensional approach that integrates symptomatology assessment with biomarker analysis, the study paves the way toward precision psychiatry, wherein interventions are tailored not just to symptoms but to underlying physiological mechanisms.

Scientists and clinicians alike may leverage this knowledge to refine screening tools and therapeutic strategies. The identification of inflammation and insulin resistance as mediators opens potential avenues for adjunctive treatments targeting these pathways, possibly enhancing the efficacy of existing antidepressant regimens or preventive efforts in smokers.

In conclusion, this expansive analysis from NHANES data provides compelling evidence that smoking exacerbates depressive symptoms partly through systemic immune activation and insulin resistance. It calls for a synergistic approach in clinical practice and public health policy, combining smoking cessation, metabolic health support, and mental health services to effectively combat the intertwined epidemics of tobacco use and depression.

As the scientific community continues to unravel the complex biological networks connecting behavior and brain health, studies like this underscore the necessity of comprehensive lifestyle interventions. Beyond merely highlighting risk, they invoke hope for developing multifaceted treatment avenues that address root causes rather than symptoms alone.

Subject of Research: Mechanistic exploration of inflammation and metabolic insulin resistance as mediators between smoking and depressive symptoms in a large nationally representative sample.

Article Title: METS-IR and SII as mediators in the association between smoking and depressive symptoms: insights from NHANES (2005–2018).

Article References: Zhou, Y., Zhuang, J., Bian, Q. et al. METS-IR and SII as mediators in the association between smoking and depressive symptoms: insights from NHANES (2005–2018). BMC Psychiatry 25, 1073 (2025). https://doi.org/10.1186/s12888-025-07114-6

Image Credits: AI Generated

DOI: 11 November 2025

Keywords: smoking, depressive symptoms, systemic immune-inflammation index (SII), metabolic insulin resistance score (METS-IR), NHANES, inflammation, insulin resistance, mental health, epidemiology, biomarker mediation, dose-response, all-cause mortality

The passage of time in the NHANES data gauges fluctuations in early onset Type 2 diabetes prevalence, which appears not only to increase in frequency but also in the diversity of those impacted. The research indicates that various demographic factors, such as ethnicity, socio-economic status, and lifestyle choices, profoundly influence susceptibility to this condition. This study sheds light on the potential disparities in health outcomes and access to care faced by different demographic groups suffering from this chronic disease.

Examining the demographic characteristics elicits a deeper understanding of who is most prone to early onset Type 2 diabetes. Findings suggest that certain groups, notably Hispanic and Black populations, demonstrate disproportionately higher rates of the disease. These results underscore the critical need for culturally sensitive public health interventions designed to address these disparities. Both higher prevalence and severity of diabetes-related complications within these populations call for urgent measures to combat this trend.

The clinical features associated with early onset Type 2 diabetes also deserve scrutiny. Data reveals that individuals diagnosed at a young age often exhibit more aggressive disease manifestations. Factors such as body mass index (BMI), insulin resistance, and a family history of diabetes play pivotal roles in determining not just the onset of the disease but also its progression. Younger patients frequently grapple with a range of comorbidities, including hypertension and dyslipidemia, highlighting the multifaceted challenges faced by this population.

One of the pivotal aspects of this NHANES analysis is the identification of lifestyle factors contributing to the rise of early onset Type 2 diabetes. Sedentary behavior, unhealthy dietary patterns, and obesity are now being recognized as significant precursors to the disease. The research indicates an alarming trend of declining physical activity levels among youth, alongside the rising consumption of calorie-dense, nutrient-poor foods. These lifestyle choices underline the urgency for educational campaigns advocating for healthier habits among younger populations.

Mental health implications associated with early onset Type 2 diabetes cannot be disregarded. Individuals facing this diagnosis at a young age frequently experience psychological distress, which exacerbates the challenges of managing their condition. Depression and anxiety disorders are notably prevalent among these patients, raising questions about the intersection between mental health and chronic disease management. Addressing mental health alongside physical health emerges as a critical component of comprehensive care for these patients.

The implications of early onset Type 2 diabetes extend beyond individual health. The rising prevalence foretells an impending public health crisis, as more young people will require long-term management of a condition that can significantly diminish quality of life. As prevalence trends continue upward, healthcare systems may face unprecedented challenges in providing adequate resources and care for this demographic. This future burden emphasizes the necessity for preventative strategies aimed at curbing the tide of this concerning health trend.

Public health interventions must be dynamically tailored to meet the needs of at-risk populations. Strategies focusing on comprehensive educational programs, robust community engagement, and accessibility to health resources can play a crucial role in prevention efforts. Emphasizing preventative care, including regular screening for high-risk demographic groups, could help detect early signs of diabetes and prevent its onset.

Collaborative efforts across various sectors, including healthcare providers, schools, and community organizations, will be essential for combating the rising tide of early onset Type 2 diabetes. Such collaboration may lead to the development of innovative programs that promote physical activity, healthy eating, and mental health awareness. Harnessing the collective expertise of multiple stakeholders increases the potential for impactful interventions that resonate with the target population.

The significance of this NHANES study cannot simply be measured in prevalence statistics. The research serves as a clarion call, beckoning society to respond to an emerging healthcare crisis while highlighting the need for ongoing research into the biology of Type 2 diabetes and the long-term consequences of early onset. Finding effective preventative strategies will depend upon further amplifying the voices of those directly affected while educating the broader community about the risks associated with early onset Type 2 diabetes.

As the healthcare landscape adapts to this emerging epidemic, a renewed emphasis on patient-centered care may pave the way for better outcomes. Empowering patients, particularly those diagnosed at a young age, with knowledge and resources to manage their health can foster a sense of agency. Furthermore, investing in mental health resources can significantly improve life satisfaction and health outcomes for those living with diabetes.

In conclusion, the rise of early onset Type 2 diabetes presents an urgent public health challenge, requiring multi-faceted approaches to understand and mitigate its effects. This NHANES analysis serves as an important foundation for future research, advocacy, and intervention strategies aimed at addressing this growing concern. As the conversation around diabetes evolves, it is crucial for society to prioritize preventative health measures, empowering individuals and communities to combat the rising threat posed by this chronic condition.

Subject of Research: Early onset Type 2 diabetes prevalence and characteristics

Article Title: Prevalence, Demographic and Clinical Characteristics of Individuals with Early Onset Type 2 Diabetes in the USA: an NHANES Analysis 1999–2020

Article References:

Lee, C.J., Bergman, B.K., Gou, R. et al. Prevalence, Demographic and Clinical Characteristics of Individuals with Early Onset Type 2 Diabetes in the USA: an NHANES Analysis 1999–2020. Diabetes Ther (2025). https://doi.org/10.1007/s13300-025-01788-7

Image Credits: AI Generated

DOI:

Keywords: Early onset diabetes, NHANES, public health, demographics, lifestyle factors, prevention, comorbidities, mental health, healthcare systems

Central adiposity, characterized by excessive fat accumulation around the abdomen, arguably plays a more pivotal role than generalized obesity in influencing metabolic and oncologic outcomes. The WWI has emerged as a promising anthropometric measure designed to more accurately quantify central fat distribution by adjusting waist circumference relative to body weight. Unlike BMI, which merely correlates body mass to height squared, WWI offers a nuanced perspective on fat accumulation patterns that could potentially translate into better risk stratification tools for breast cancer. Given breast cancer’s status as the most frequently diagnosed cancer and a leading cause of cancer mortality among women worldwide, refining risk prediction models is of utmost importance.

This ambitious study analyzed a large, nationally representative sample of 10,760 women aged 20 years and older, collected between 2005 and 2018 by NHANES. The dataset provided a rich source of demographic, clinical, and anthropometric variables, which allowed for a thorough examination of the relationship between WWI and breast cancer prevalence. The researchers employed logistic regression as their primary analytical method to initially assess the association between WWI and breast cancer odds. Recognizing the complex interplay of variables potentially confounding this relationship, they incorporated rigorous adjustments for covariates and adopted diagnostics such as the variance inflation factor to tackle multicollinearity, ensuring the robustness of their analyses.

Parallel to classical statistics, the study pioneers the integration of machine learning approaches to refine variable selection and predictive modeling. Specifically, the researchers harnessed random forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods to probe which anthropometric and clinical markers best predict breast cancer presence. Machine learning offers sophisticated algorithms capable of capturing nonlinear relationships and complex interactions often missed by traditional models. Notably, the random forest algorithm identified WWI as a top-tier predictor, emphasizing its potential significance, whereas LASSO regression excluded it, highlighting the nuances inherent in variable selection methodologies.

Assessing model performance through Receiver Operating Characteristic (ROC) curves, calibration plots, and decision curve analysis, the authors affirmed the enhanced discriminatory power of models that incorporated variables initially selected by both machine learning methods, including WWI. The random forest model achieved an area under the curve (AUC) of 0.795, while the LASSO-based model closely trailed with an AUC of 0.79, signifying respectable predictive accuracy. These results hint that although WWI alone may not independently predict breast cancer status, its inclusion alongside key covariates can bolster model performance, potentially aiding clinicians and researchers in risk stratification.

Yet, the study’s results prompt nuanced interpretation. In unadjusted logistic regression, WWI’s association with breast cancer was statistically significant, with an odds ratio suggesting increased risk as WWI rises. However, after adjusting for a comprehensive set of demographic and clinical variables—such as age, race, socioeconomic status, comorbidities, and other anthropometric measures—the association attenuated and lost statistical significance. This attenuation underscores the intricate, multifactorial nature of breast cancer etiology where WWI influences may be mediated or confounded by other factors, tempering its utility as a standalone biomarker.

The cross-sectional design of the study warrants caution in inferring causality. Breast cancer cases represented a relatively small subset of the study population (326 out of 10,760 women), constraining statistical power and possibly limiting the detection of subtle associations. Because cross-sectional data capture a snapshot rather than a temporal sequence, it remains uncertain whether increased WWI preceded cancer development or vice versa. Prospective cohort studies with a larger number of incident breast cancer cases are indispensable to validate the observed trends and to unravel WWI’s true predictive capacity over time.

Further, biological plausibility supports conceptualizing WWI as a meaningful metric in oncological risk prediction. Central adiposity is linked with insulin resistance, chronic inflammation, and hormonal dysregulation—all critical pathways implicated in breast cancer pathogenesis. WWI’s ability to better reflect visceral fat accumulation compared to BMI may therefore harbor mechanistic relevance. If substantiated through longitudinal research, WWI might serve as a valuable clinical tool to augment existing risk models by emphasizing fat distribution rather than generalized adiposity, paving the way for personalized preventative strategies.

The study’s integration of advanced machine learning underscores the evolving landscape of epidemiologic research. Such methods excel in handling high-dimensional data, identifying interaction effects, and enhancing predictive validity. Importantly, the divergence observed between random forest and LASSO outcomes highlights the complementary nature of these algorithms; employing multiple approaches may yield a more comprehensive understanding of variable importance, particularly in complex biomedical settings. This methodological rigor advances precision medicine efforts by refining risk markers tailored to individual patients.

Overall, these findings illustrate the promise and limitations of novel anthropometric indices in breast cancer risk assessment. While the WWI demonstrates potential as an informative variable when combined with other predictors, it does not replace the multifaceted risk framework but adds nuance to conventional obesity metrics. Clinicians and researchers are encouraged to interpret WWI’s utility within this broader context, recognizing that anthropometry constitutes one piece of a larger puzzle involving genetic, lifestyle, and environmental factors.

In light of these insights, the authors advocate for larger prospective investigations incorporating WWI alongside a spectrum of biological, behavioral, and sociodemographic variables. Such studies could elucidate whether longitudinal changes in WWI influence breast cancer incidence and if WWI can refine risk stratification algorithms for clinical application. Additionally, research exploring the biological mechanisms underpinning WWI’s association with oncogenesis could illuminate novel preventative or therapeutic targets.

The study bridges a gap in existing literature by merging classical epidemiology with machine learning, illustrating how emerging data science techniques can enrich traditional frameworks. Such integrative approaches are poised to revolutionize cancer epidemiology by enabling refined risk prediction, earlier detection, and ultimately, improved patient outcomes. As precision oncology advances, leveraging sophisticated anthropometric indices like WWI may represent a valuable frontier.

In conclusion, while the weight-adjusted waist index does not emerge as an independent predictor of breast cancer prevalence after adjustment for confounders, it shows potential as part of a combined set of predictors enhancing overall model performance. This underscores the importance of comprehensive approaches to cancer risk prediction, incorporating advanced metrics and analytic methods. The study stands as a call to further explore anthropometric innovations and machine learning applications in cancer epidemiology, fostering progress toward more sophisticated, personalized risk assessments.

Subject of Research: The relationship between weight-adjusted waist index (WWI) and breast cancer prevalence using NHANES data.

Article Title: The application and predictive value of the weight-adjusted-waist index in BC prevalence assessment: a comprehensive statistical and machine learning analysis using NHANES data.

Article References:
Wang, W., Wu, B., Li, J. et al. The application and predictive value of the weight-adjusted-waist index in BC prevalence assessment: a comprehensive statistical and machine learning analysis using NHANES data. BMC Cancer 25, 1234 (2025). https://doi.org/10.1186/s12885-025-14651-6

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14651-6

The study taps into data gathered from the US National Health and Nutrition Examination Survey (NHANES), covering the period from 2011 through 2023. NHANES is renowned for its nationally representative sampling methodology, designed to reflect the broader American population across various demographics and health statuses. By analyzing five cycles of survey data, the investigators were able to rigorously compare depression prevalence among individuals with military service backgrounds to those without, while controlling for numerous confounding factors such as age, sex, race, socioeconomic status, and comorbid health conditions.

Central to the assessment of depressive symptoms was the Patient Health Questionnaire (PHQ-9), a clinically validated instrument widely employed in psychiatric epidemiology. The PHQ-9 evaluates the presence and severity of nine hallmark depressive symptom domains over the preceding two weeks, rating frequency on a scale of 0 ("not at all") to 3 ("nearly every day"). Clinical significance was determined using a threshold score of 10 or above, indicative of at least moderate depression requiring professional attention. This quantifiable approach ensures robust and interpretable mental health metrics, facilitating population-level comparisons.

Among the 25,949 survey participants, 2,407 individuals reported prior US military service. Statistical weighting extrapolated these figures to represent approximately 8.8 million military-experienced adults nationwide. The remaining cohort of 23,542 participants without military affiliation corresponded to roughly 89 million US adults. Depression, as defined by the PHQ-9 criteria, was identified in 2,548 respondents overall. The unadjusted prevalence of depression across the entire sample was nearly 9.5%, yet notably, it was lower—about 7.5%—among those with a history of military service.

To unravel the complexity behind these numbers, the research team employed multivariate regression analyses to adjust for a suite of potential confounders including demographic variables, income levels, educational attainment, marital status, and prevalent metabolic conditions like hypertension, hypercholesterolemia, and diabetes. After rigorous statistical adjustment, military service was associated with a statistically significant 22 to 23% relative reduction in depression risk compared to individuals with no military experience. This protective effect persisted even after further controls were introduced for physical health comorbidities.

Examining the veteran subpopulation separately, the study found no significant difference in depression rates compared to the general population when unadjusted. However, refined analyses revealed that female veterans and those who were unmarried or divorced faced elevated depression risks. Conversely, high income and healthier cardiovascular profiles appeared to mitigate such risks. Interestingly, active duty status itself did not emerge as a significant independent predictor of depression, challenging stereotypes that ongoing service inherently predisposes individuals to poor mental health outcomes.

It is imperative to emphasize that this investigation is observational and cross-sectional by design, meaning causality cannot be definitively inferred. The researchers acknowledge that essential variables such as combat exposure intensity, length of military service, trauma history, and specific deployment experiences were not captured in the NHANES dataset and likely influence mental health trajectories profoundly. Furthermore, psychiatric outcomes are multifactorial, involving complex interactions between genetic predispositions, psychological resilience, social supports, and environmental stressors.

Nevertheless, the study authors propose that the structured environment, camaraderie, discipline, and coping mechanisms honed during military service may foster psychological resilience, potentially offsetting some traditional stressors associated with military life. This hypothesis aligns with developmental psychology theories positing that controlled exposure to adversity can stimulate adaptive growth when supported by protective social networks. The military’s emphasis on unit cohesion and identity formation might provide essential buffers against depressive symptomatology.

The findings also highlight a critical methodological consideration: prior research demonstrating higher depression rates in veterans has often been limited to healthcare system-based samples, which may be biased toward individuals actively seeking medical and mental health care, thus overestimating population prevalence. By utilizing NHANES, which samples the civilian community at large, this study offers a less biased lens through which to evaluate mental health burdens associated with military service, potentially reconciling conflicting epidemiological data.

This nuanced understanding invites policymakers, clinicians, and military leadership to reconsider strategies targeting mental health interventions within armed forces and veteran populations. It underscores the importance of tailored approaches that recognize variability in risk profiles linked to gender, marital status, and socioeconomic factors rather than blanket assumptions about service-related mental health impairments. Equally, it prompts further research into identifying protective elements within the military culture and training that could be leveraged in broader mental health promotion efforts.

In sum, this extensive cross-sectional study advances a compelling argument that, contrary to widespread belief, general US military service does not inherently increase depression risk and may, under certain circumstances, be protective. While it stops short of establishing causality, its breadth and representativeness provide a robust platform for re-examining military mental health paradigms and underscore the complexities inherent in understanding how service shapes psychological well-being across the lifespan.

Subject of Research: People
Article Title: Military service and depression risk among American adults: a cross-sectional analysis based on NHANES data from 2011 to 2023
News Publication Date: 29-May-2025
Web References:
NHANES: https://www.cdc.gov/nchs/nhanes/index.html
PHQ-9: https://patient.info/doctor/patient-health-questionnaire-phq-9
References: 10.1136/military-2024-002932
Keywords: Affective disorders, Environmental illness, Environmental health, Government jobs, Warfare

Telomeres, the protective caps located at the ends of chromosomes, play an essential role in maintaining chromosomal integrity. Over time, as cells divide, these telomeres progressively shorten, which can lead to impaired cell division and the onset of age-related diseases. Previous studies have correlated healthy lifestyle choices with preserved telomere length, but many have focused on a limited array of factors while neglecting key components such as chronic inflammation. The present study endeavors to fill this gap by employing advanced statistical modeling techniques known as Copula Graphical Models.

The research team analyzed health data from over 7,000 U.S. adults aged 20 to 84, collected during appropriate time frames between 1999 and 2002. This multi-faceted approach allowed the researchers to assess more than 100 variables impacting telomere length, including dietary habits, physical activity, smoking, and various blood biomarkers. The participants were divided into three age categories—young (20–39 years), middle-aged (40–59 years), and older adults (60–84 years)—to evaluate how age influences the relationships between these factors and telomere length.

One pivotal finding of the study indicates that the levels of C-reactive protein (CRP) are more prominently associated with telomere length than other lifestyle factors such as diet and physical activity. CRP serves as a widely recognized marker of inflammation, and its elevated levels were consistently linked with shorter telomeres, particularly in the younger and middle-aged groups. This correlation underscores a critical insight: chronic inflammation may be a more influential determinant of cellular aging than previously assumed lifestyle choices.

Furthermore, while lifestyle factors remain integral to overall health, this research asserts that their impact is likely mediated through their effects on inflammation rather than direct associations with telomere length. The authors propose that focusing on reducing chronic inflammation could be a more effective strategy for preserving telomere integrity and promoting healthier aging than conventional approaches that center primarily on dietary changes.

As the study further elucidates the relationship between lifestyle factors, telomeres, and aging, it calls into question previous research that isolated individual lifestyle elements for examination. By adopting a holistic data-driven methodology, this investigation provides a clearer picture of how various health behaviors and biological markers interact to influence the aging process at the cellular level.

While the results do not definitively establish cause-and-effect relationships, they strongly support the premise that inflammation plays a pivotal role in cellular aging. The authors advocate for additional long-term studies aimed at understanding the temporal dynamics between inflammation and telomere length. This pursuit is essential to elucidate the mechanisms by which chronic inflammation contributes to telomere attrition and age-associated diseases.

The implications of these findings extend beyond academic interest; they bear significant relevance for public health. As understanding chronic inflammation’s impact on aging becomes increasingly recognized, it highlights the importance of reducing inflammatory states through lifestyle interventions such as regular physical activity, stress management, and potential dietary modifications focused on anti-inflammatory foods.

In conclusion, the study not only challenges the traditional views on the relationship between diet and telomere length but also emphasizes the necessity of addressing chronic inflammation as a cornerstone of healthy aging. By shifting the focus of research and health recommendations to encompass inflammatory factors, there is potential for developing innovative approaches to enhance healthspan and mitigate age-related diseases.

The intricate interplay of biochemistry and lifestyle thus becomes not merely an academic curiosity but rather a critical consideration for individuals seeking longevity and healthy aging. With further research on this topic, the hope is that we can cultivate a deeper comprehension of cellular aging processes, ultimately guiding effective public health strategies for the aging population.

With its quantitative assessments and comprehensive methodologies, this study stands as a significant contribution to our understanding of aging and invites ongoing dialogue among researchers, clinicians, and the general public. Indeed, as we enter an era where the science of aging is becoming increasingly significant, studies like this will illuminate pathways to healthier futures.

Subject of Research: People
Article Title: Diet, lifestyle and telomere length: using Copula Graphical Models on NHANES data
News Publication Date: 29-Jan-2025
Web References: Aging-US
References: DOI: 10.18632/aging.206194
Image Credits: Copyright: © 2025 Tedaldi et al.

Keywords: aging, telomere length, NHANES, C-reactive protein, inflammation, lifestyle factors