Sage Journals: Discover world-class research

Abstract

Background:

The association between social risk and biomarkers of inflammation remains underexplored in low-resource communities, where social risk and levels of inflammation differ from those in industrialized urban centers. This study aims to assess the association between levels of social risk and biomarkers of inflammation in adults residing in remote rural settings.

Methods:

This population-based, cross-sectional study involved 1392 community-dwellers aged ≥40 years. Social risk was assessed using the social determinants of health (SDH) included in Gijon’s Social Familial Evaluation Scale (SFES). Biomarkers of inflammation were measured through the Systemic Immune-Inflammation Index (SII) and the C-Reactive Protein (CRP)/albumin ratio. Both unadjusted and multivariate models were fitted to determine the independent association between SDH and biomarkers of inflammation, treated as dependent variables.

Results:

The mean (±SD) age of 1392 study participants was 53.8 ± 11.5 years (59% women). The mean Gijon’s SFES score was 10.1 ± 2.6 points, the mean SII was 451.6 ± 240.5 × 10⁹ L, and the mean CRP/albumin ratio was 0.13 ± 0.29. Unadjusted generalized linear regression models demonstrated direct significant associations between SDH scores and both dependent variables, including SII (β: 6.12; 95% CI: 1.35-10.89) and the CRP/albumin ratio (β: 0.03; 95% CI: 0.01-0.05). These associations remained significant after adjusting for demographics, level of education, and cardiovascular risk factors for both, the SII (β: 7.24; 95% CI: 2.08-12.41) and the CRP/albumin ratio (β: 0.03; 95% CI: 0.01-0.05).

Conclusions:

Social risk is directly associated with biomarkers of inflammation in the study population, suggesting that inflammation may be associated with adverse health outcomes in individuals with high social risk. Study results demonstrate that this association is not only evident in high-income regions but in underserved rural communities as well.

Keywords

population-based study social risk social determinants of health biomarkers of inflammation systemic immune-inflammation index CRP/albumin ratio

Introduction

The social determinants of health (SDH) encompass non-medical factors that influence the conditions in which individuals are born, grow, live, work, and age. These factors include living conditions, economic stability, education quality, social relationships, support networks, and access to healthcare.¹ SDH are used to assess the social risk faced by individuals and have been shown to play a significant role on health outcomes, particularly in vulnerable populations, as they can influence the occurrence and progression of chronic conditions and diseases.^2
-4

A significant body of evidence suggests that inflammation plays a central role as a key factor mediating the relationship between social risk and negative health consequences.^5
-7 However, much of the existing evidence on the association between social risk and levels of inflammation does not fully address potential differences related to the diverse settings in which people live. Evaluating the mediators of the consequences of social risk should be tailored to the distinct circumstances of individuals residing in rural or urban settings.⁸ Rural populations often face unique social challenges such as limited access to healthcare, restricted educational opportunities, and smaller support networks. In contrast, urban populations might encounter different stressors, such as higher levels of pollution, greater socioeconomic disparities, and more pronounced gender and racial disparities.

There is a gap in the literature regarding how social risk assessments should be adapted to these different environments. Most studies have focused on urban settings, leaving the experiences and needs of rural populations underexplored. Additionally, it has been suggested that some biomarkers of inflammation may be different in rural settings compared to industrialized urban centers due to distinct environmental conditions.⁹ Addressing this gap is crucial for developing more accurate and adapted social risk assessments that can better inform about cost-effective public health interventions and policies aimed at reducing health disparities. It is also important to determine if the inflammatory response ensued through increased social risk differs in distinct settings.

The present study aims to assess the association between SDH and biomarkers of systemic inflammation middle-aged and older adults living in rural settings.

Methods

Study population: The study was conducted in 3 rural villages located in coastal Ecuador. Residents of these villages share ethnicity (Amerindian ancestry), low levels of education, low economic status, and living conditions. These consistencies minimize the possibility of disparities in assessing the investigated variables during the conduction of population studies.

Study design: Following a population-based cross-sectional design, community-dwellers aged ≥40 years residing in the study villages were identified through annual door-to-door surveys and invited to participate. Participants underwent interviews and procedures for the assessment of demographics, cardiovascular risk factors, social risk evaluation, and determinations of blood cells counts (platelets, neutrophils, and lymphocytes) as well as C-reactive protein (CRP) and serum albumin levels. Data was not imputed in any case, but individuals who did not complete all the required tests were excluded. We excluded individuals with chronic obstructive lung disease, advanced liver disease, chronic renal failure, cancer, autoimmune diseases, and hematological disorders, as well as those who had a platelet or leukocyte count outside a pre-established range (between 4000 and 11 000 µL). Participants signed a comprehensive informed consent before enrollment. The study was approved by the Ethics Committee of our Institution.

Social risk assessment: Social risk was determined using SDH included in Gijon’s Social-Familial Evaluation Scale (SFES).¹⁰ This instrument evaluates 5 different components that include: family situation, economic status, housing conditions, social relationships, and support networks. Each component consists of 5 questions, weighed on a scale of 1 to 5, for a total possible score of 25, with higher scores indicating greater social risk (Supplementary file 1). This scale was selected due to its suitability to the living conditions of the study population and has been endorsed by the Ecuadorian Minister of Health for the assessment of social risk.¹¹

Biomarkers of inflammation: This study focused on assessing the systemic immune-inflammation index (SII) and the CRP/albumin ratio, which are recognized biomarkers of systemic inflammation.^12,13 Blood samples were drawn and centrifuged on the field and then transported on ice to the laboratory for further processing. Cell counts were performed using a Sysmex XN-3000 Hematology Analyzer (Kobe, Japan), and biochemical analysis were conducted with a COBAS® c 501 Automatic Analyzer (Roche Diagnostics, Mannheim, Germany). The SII was calculated using the formula: platelets × neutrophils/lymphocytes × 10⁹ L, and the CRP/albumin ratio was determined by dividing the CRP level in mg/L by the albumin in g/L.

Covariables investigated: Age, sex, level of education (primary school education or higher), and traditional cardiovascular risk factors were used as relevant confounders due to their associations with both social risk and biomarkers of inflammation.^14
-16 Demographics and level of education were assessed by self-report, while cardiovascular risk factors (health metrics) were based on the Life’s Simple 7 construct of the American Heart Association (AHA), which includes 7 metrics categorized in 3 ranges (ideal, intermediate, and poor). Metrics in the poor range were determined according to specific cutoffs, as follows: (1) smoking status: current or quit <1 year; (2) body mass index: ≥30 kg/m²; (3) no moderate or intense physical activity; (4) poor diet: regular consumption of none or only one component of the suggested AHA healthy diet; (5) blood pressure: ≥140/≥90 mmHg; (6) fasting glucose: ≥126 mg/dL; and (7) total cholesterol blood levels: ≥240 mg/dL.¹⁷ We did not use Life’s Essential 8, the most recent construct of the AHA for assessing cardiovascular health status,¹⁸ since the data for most study participants were collected at the study entry, well before the introduction of this new construct.

Statistical analyses: Data analysis was carried out using STATA version 18 (College Station, TX, USA). In unadjusted analyses, continuous variables were compared using linear models and categorical variables by using the chi-square or Fisher exact test, as appropriate. Unadjusted and multivariate generalized linear regression models were fitted with Gijon’s SFES as the main independent variable and the SII and the CRP/albumin ratio as separate dependent variables. Multivariate models were adjusted first for age, sex, and education, and subsequently for all the above-mentioned covariates. The SII variable and the natural log of the CRP/albumin ratio were normally distributed. Non-parametric regression was utilized for confirmation using non-transformed CRP/albumin ratio.

Results

Of the 1838 individuals aged ≥40 years enrolled in the study villages as of June 2024, 1392 (76%) completed the clinical interviews and laboratory exams required to investigate all the variables of interest. Of the remaining 446 individuals, 13 died or emigrated between enrollment and the invitation to participate in this study, 37 were unable to complete the interviews due to aphasia or severe cognitive impairment, 67 had any of the exclusion criteria detailed in the methods section, and 329 declined consents, mostly because their unwillingness to have blood drawn. Included individuals were younger, less often women, and better educated than those who were excluded (all differences at the P < .001 level).

The mean (±SD) age of 1392 study participants was 53.8 ± 11.5 years (median age: 51 years), 818 (59%) were women, and 747 (54%) had primary school education only. CVH metrics in the poor range included: smoking status: 69 (5%); body mass index: 436 (31%); physical activity: 73 (5%); diet: 265 (19%); blood pressure: 354 (25%); fasting glucose: 301 (22%); and total cholesterol blood levels: 170 (12%).

The mean Gijon’s SFES score was 10.1 ± 2.6 points (median: 10 points), The mean SII was 451.6 ± 240.5 × 10⁹ L (median: 339.4 × 10⁹ L; range: 42.1-1,922.6; interquartile range: 295.6-552.2), and the mean CRP/albumin ratio was 0.13 ± 0.29 (median: 0.06; range: 0.004-3.54; interquartile range: 0.02-0.11).

Table 1 depicts characteristics of study participants, categorized by SDH scores stratified by the median (unadjusted analyses). Participants with lower scores on the Gijon’s SFES were younger, less often women, had lower education levels, better physical activity, and were less likely to have blood pressure readings ≥140/≥90 mmHg, and total cholesterol levels ≥240 mg/dL.

Table 1.

Characteristics of 1392 Study Participants According to Scores in the Gijon’s Social Familial Evaluation Scale (Gijon’s SFES), Measuring Social Determinants of Health, Dichotomized by the Media (Unadjusted Analyses).

Variable	Gijon’s SFES ≤ 9 points (n = 668)	Gijon’s SFES ≥ 10 points (n = 724)	P value
Age, years (mean ± SD)	51.7 ± 10.3	55.8 ± 12.2	<.001*
Women, n (%)	329 (49)	489 (68)	<.001*
Primary school education, n (%)	303 (45)	444 (61)	<.001*
Current smoker or quit <1 year, n (%)	38 (6)	31 (4)	.227
Body mass index ≥30 kg/m², n (%)	226 (34)	210 (29)	.052
Poor physical activity, n (%)	19 (3)	54 (7)	<.001*
Unhealthy diet, n (%)	132 (20)	133 (18)	.509
Blood pressure ≥140/≥90 mmHg, n (%)	144 (22)	210 (29)	.001*
Fasting glucose ≥126 mg/dL, n (%)	126 (19)	175 (24)	.016*
Total cholesterol ≥240 mg/dL, n (%)	88 (13)	82 (11)	.293

Statistically significant results.

Unadjusted generalized linear regression models showed significant direct associations between Gijon’s SFES scores and the SII (β: 6.12; 95% CI: 1.35-10.89), as well as between the Gijon’s SFES and the CRP/albumin ratio (β: 0.03; 95% CI: 0.01-0.05). When these models were adjusted for age, sex, and education, Gijon’s SFES scores were still associated with both biomarkers of inflammation (β: 7.81; 95% CI: 2.69-12.94, for the SII, and β: 0.03; 95% CI: 0.01-0.05, for the CRP/albumin ratio). Likewise, Gijon’s SFES scores and both biomarkers of inflammation remained significantly associated in multivariate models that included all the covariates of interest (Table 2). Age and increased body mass index were significant covariates in both models, while sex and high glucose levels were significant only in the model assessing the association between Gijon’s SEFS score and CRP/albumin ratio.

Table 2.

Multivariate Generalized Linear Regression Models Showing Associations Between Scores in Gijon’s Social Familial Evaluation Scale and the Systemic Immune-inflammation Index (Upper Panel) and the C-reactive Protein/Albumin Ratio (Lower Panel), After Adjustment for All Covariates of Interest.

Systemic immune inflammation index	β coefficient	95% CI	P value
Gijon’s social familial evaluation scale	7.24	2.08-12.41	.006*
Age	−1.52	−2.77-−0.27	.017*
Being woman	−2.36	−29.7-24.9	.865
Primary school education	3.21	−24.7-31.2	.822
Current smoker or quit <1 year	15.4	−45.3-76.0	.619
Body mass index ≥30 kg/m²	−37.8	−65.6-−9.91	.008*
Poor physical activity	12.5	−44.6-69.6	.668
Unhealthy diet	6.91	−25.6-39.4	.677
Blood pressure ≥140/≥90 mmHg	−3.98	−34.5-26.5	.798
Fasting glucose ≥126 mg/dL	25.1	−5.84-56.1	.112
Total cholesterol ≥240 mg/dL	12	−26.8-50.8	.544

Statistically significant results.

C-reactive protein ratio	β coefficient	95% C.I.	P value
Gijon’s social familial evaluation scale	.03	0.01-0.05	.010*
Age	−.01	−0.01-−0.00	.024*
Being woman	.28	0.15-0.40	<.001*
Primary school education	.06	−0.06-0.19	.306
Current smoker or quit <1 year	.01	−0.26-0.47	.970
Body mass index ≥30 kg/m²	.34	0.22-0.47	<.001*
Poor physical activity	.15	−0.10-0.41	.236
Unhealthy diet	.07	−0.07-0.22	.329
Blood pressure ≥140/≥90 mmHg	−.08	−0.21-0.06	.258
Fasting glucose ≥126 mg/dL	.15	0.02-0.29	.028*
Total cholesterol ≥240 mg/dL	.01	−0.16-0.19	.867

Statistically significant results.

Discussion

In this cross-sectional, population-based study of community-dwellers aged ≥40 years living in rural settings, we observed a significant direct association between higher levels of social risk and biomarkers of inflammation, namely, the SII and the CRP/albumin ratio, in both unadjusted and multivariate models. These findings corroborate the results of previous studies conducted in industrialized urban centers, thereby expanding the applicability of the above-mentioned associations to resource-limited communities, where measurements of social risk and levels of inflammation differ.^8,9

Research conducted in urban centers most often focused on a single component of social risk, such as social relationships or economic status. To mention some of these studies, hostile Scottish individuals with low socio-economic status exhibited higher levels of inflammatory biomarkers compared to their non-hostile counterparts.¹⁹ The authors speculated that hostility may have adverse effect on the endothelium and elaborate on this effect as a potential cause of increased expression of inflammatory biomarkers. In another study conducted in Denmark, New Zealand and the UK, a strong association was found between social isolation with increased inflammation, and it was suggested that this social stressor precedes inflammation and hypothesized that dysregulation of the hypothalamic-pituitary-adrenal axis (related to social isolation) mediated the association between social isolation and inflammation.⁶ In addition, another study using NHANES data demonstrated a significant association between social disadvantage and inflammation, which was more evident among middle-aged Hispanic men and women than in people from other ethnic and age groups.²⁰

A comprehensive evaluation of the association between a construct that includes various components of social risk—like those included in the Gijon’s SFES—and inflammation has not yet been conducted.^6,19,20 Moreover, there remains uncertainty regarding whether the inflammatory biomarkers used in previous studies conducted in developed urban centers are applicable for assessing the association between social risk and biomarkers of inflammation in resource-limited rural communities. In the present study, we relied on a structured and validated field instrument that includes different determinants of social risk, that was specifically designed for individuals living in low- and middle-income countries.¹⁰ Furthermore, the 2 biomarkers of inflammation determined in this study have previously demonstrated reliability in resource-limited rural communities.^21,22

The few available studies in rural areas of low- and middle-income countries suggest that high social risk is a significant issue in these regions. Those studies demonstrated direct associations between social risk and poor cognitive performance,²³ as well as inadequate nutritional status.¹⁴ Additionally, social risk has been linked to detrimental effects on cardiovascular risk factors and the progression of cerebral small vessel disease biomarkers.^15,21 Furthermore, this condition has been found to be associated with increased premature mortality.²⁴

Understanding the link between social risk and inflammation has significant implications for public health interventions. By identifying specific social risk factors that contribute to increased inflammation, targeted interventions can be designed to improve health outcomes. For example, community-based programs that strengthen social networks, provide economic support, or enhance access to healthcare could potentially reduce inflammation and its associated health risks in vulnerable populations.

This study has several strengths. The use of a holistic approach to estimate levels of social risk together with the use of a field instrument (the Gijon’s SFES) specifically designed to be applicable in limited-resource populations, underscore the validity of our findings. This is relevant because social risk factors and their impact on biomarkers of inflammation may differ between high-income and low-income settings due to variations in resources, healthcare access, and social support systems. Likewise, the use of 2 different biomarkers of systemic inflammation enhanced the credibility of our findings since both were independently associated with higher levels of social risk. In addition, the selection of participants taken from the community and the systematic assessment of the different variables of interest ensure that the observed associations are robust and scientifically sound.

Limitations of our study extend beyond its cross-sectional design precluding the evaluation of the direction of the relationship between the main variables investigated. While biological plausibility suggests social risk acts as exposure and inflammation as the outcome, we cannot definitively conclude that higher levels of social risk cause increased expression of inflammatory biomarkers, especially in the light of some studies indicating that this relationship may be bidirectional.⁷ While the selected biomarkers of inflammation are reliable, they only capture a part of the complex inflammatory response, and determinations of other biomarkers could provide a more comprehensive understanding of the relationship between social risk and inflammation. A sizable proportion of eligible candidates (about 15%) refused to participate because “fear of the needle,” a reason commonly observed in rural populations²⁵; this may have resulted in selection bias, although individuals who refused blood drawn had similar characteristics regarding age, sex, and level of education as those included in the study. Additionally, our results might not be generalizable to other rural populations. Subsequent studies should consider diverse populations to validate these findings across various rural settings.

In conclusion, the results of this study demonstrate that high social risk is associated with an increased expression of inflammatory biomarkers. These may, in turn, drive complex biological responses leading to clinical complications related to social risk. Departing from the population enrolled in this cross-sectional study, an ongoing prospective longitudinal study has been designed to demonstrate if inflammation serves as the link between social risk with adverse outcomes. Understanding this connection is critical for a better comprehension of the role of social risk in human health.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319251344427 – Supplemental material for High Social Risk and Biomarkers of Systemic Inflammation: A Population-Based Study in Middle-Aged and Older Adults Living in Rural Communities

Supplemental material, sj-docx-1-jpc-10.1177_21501319251344427 for High Social Risk and Biomarkers of Systemic Inflammation: A Population-Based Study in Middle-Aged and Older Adults Living in Rural Communities by Oscar H. Del Brutto, Denisse A. Rumbea, Emilio E. Arias, Kleber Arriaga and Robertino M. Mera in Journal of Primary Care & Community Health

Footnotes

ORCID iDs

Oscar H. Del Brutto

Denisse A. Rumbea

Author Contributions

OHD: study design, manuscript drafting; DAR: study coordinator, data collection and analysis; EEA: data collection; KA: interpretation of laboratory exams; RMM: statistical analysis, significant intellectual contribution to manuscript content.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Universidad Espíritu Santo—Ecuador. The sponsor had no role in the design of the study, in the collection, analysis and interpretation of data, or in the decision to submit the manuscript for publication.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

References

Marmot

Friel

Bell

, et al. Commission on social determinants of health. Closing the gap in a generation: health equity through action on the social determinants of health. Lancet. 2008;372:1661-1669.

Havranek

Mujahid

Barr

, et al. Social determinants of risk and outcomes for cardiovascular disease. A scientific statement from the American Heart Association. Circulation. 2015;132:873-898.

Ferrario

Veronesi

Kee

Determinants of social inequalities in stroke incidence across Europe: a collaborative analysis of 126 635 individuals from 48 cohort studies. J Epidemiol Community Health. 2017;71:1210-1216.

Yang

Z-G

Sun

Han

, et al. Relationship between social determinants of health and cognitive performance in an older American population: a cross-sectional NHANES study. BMC Geriatr. 2025;25:25.

Moieni

Eisenberger

NI.

Effects of inflammation on social processes and implications for health. Ann N Y Acad Sci. 2018;1428:5-13.

Matthews

Rasmussen

LJH

Ambler

, et al. Social isolation, loneliness, and inflammation: a multi-cohort investigation in early and mid-adulthood. Brain Behav Immun. 2024;115:727-736.

Androulakis

IP.

From cells to society: untangling the web of stress, inflammation, and social determinants of health. Front Sci. 2024;2:1358784.

Weeks

Chang

Pagán

, et al. Rural-urban disparities in health outcomes, clinical care, health behaviors, and social determinants of health and an action-oriented, dynamic tool for visualizing them. PLOS Glob Public Health. 2023;3:E0002420.

Darko

Yar

Owuse-Dabo

, et al. Variations in levels of IL-6 and TNF-a in type 2 diabetes mellitus between rural and urban Ashani region of Ghana. BMC Endocr Disord. 2015;15:50.

10.

Cabrera González

Menéndez Caicoya

Fernández Sánchez

, et al. Evaluación de la fiabilidad y validez de una escala de valoración social en el adulto. Atención Primaria. 1999;23:434-440.

11.

Ministerio de Salud Pública del Ecuador. Guías clínicas geronto-geriátricas de atención primaria de salud para el adulto mayor. Quito, Ecuador. 2008. Accessed March 3, 2025. https://vicenteayalabermeo.files.wordpress.com/2011/04/guc3adas-adulto-mayor.pdf

12.

Mangoni

Zinellu

The diagnostic role of the systemic inflammation index in patients with immunological diseases: a systematic review and meta-analysis. Clin Exp Med. 2024;24:27.

13.

Wandell

Carlsson

Larsson

, et al. The predictive value of cardiovascular outcomes and mortality assessed by the C-reactive protein to albumin ratio in the UK Biobank. BMC Cardiovasc Disord. 2024;24:326.

14.

Del Brutto

Rumbea

Mera

, et al. Social determinants of health (social risk) and nutritional status among community-dwelling older adults living in a rural setting: the Atahualpa Project. J Prim Care Community Health. 2022;13:2150131922.

15.

Del Brutto

Mera

Rumbea

, et al. Detrimental effect of high social risk on the cardiovascular health status of community-dwelling older adults living in rural settings. A population-based, longitudinal prospective study. Int J Cardiol. 2023;375:124-130.

16.

Del Brutto

Mera

Recalde

, et al. High social risk influence progression of white matter hyperintensities of presumed vascular origin: a prospective study in community-dwelling older adults. Stroke. 2022;53:2577-2584.

17.

Lloyd-Jones

Hong

Labarthe

, et al. Defining and setting national goals for cardiovascular health promotion. The American Heart Association’s strategic impact goal through 2020 and beyond. Circulation. 2010;121:586-613.

18.

Lloyd-Jones

Allen

Anderson

CAM

, et al. Life’s Essential 8: updating and enhancing the American Heart Association’s construct of cardiovascular health: a presidential advisory from the American Heart Association. Circulation. 2022;146:e18-e43.

19.

Millar

Lloyd

McLean

, et al. Personality, socio-economic status and inflammation: cross-sectional, population-based study. PLoS ONE. 2013;8:e58256.

20.

Yang

Schorpp

Harris

. Social support, social strain and inflammation: evidence from a national longitudinal study of U.S. adults. Soc Sci Med. 2014;107:124-135.

21.

Del Brutto

Mera

Rumbea

, et al. Systemic immune-inflammation index and progression of white matter hyperintensities of presumed vascular origin. A longitudinal population study in community-dwelling older adults living in rural Ecuador. J Neurol Sci. 2023;452:120741.

22.

Ghaffar

Shahnoor

Khan

, et al. CRP albumin ratio: a novel noninvasive and cost-effective method for assessing the severity of acute pancreatitis. Health Sci Rep. 2024;7:e1801.

23.

Del Brutto

Mera

Rumbea

, et al. Social determinants of health and cognitive performance of older adults living in rural communities; the Three Villages Study. Int J Geriatr Psychiatry. 2022;37.

24.

Del Brutto

Mera

Rumbea

, et al. High social risk and mortality. A prospective study in community-dwelling older adults living in a rural Ecuadorian village. Prev Med Rep. 2023;32:102146.

25.

Del Brutto

Castillo

Sedler

, et al. Reasons for declining consent in a population-based cohort study conducted in a rural South American community. J Environ Public Health. 2018;26:2018:8267948.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB