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Clínica e Investigación en Arteriosclerosis (English Edition) Atherogenic risk in normal-weight workers: Associations with sociodemographic an...
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Vol. 38. Issue 4.
(July - August 2026)
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Vol. 38. Issue 4.
(July - August 2026)
Original article

Atherogenic risk in normal-weight workers: Associations with sociodemographic and healthy lifestyle factors using lipid-based indexes

Riesgo aterogénico en trabajadores con normopeso: asociación con factores sociodemográficos y hábitos de vida saludables mediante índices lipídicos
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Claudia Isabella Tirado Cabreraa, Daniel Guzmán Capotea, Pedro Juan Tárraga Lópezb,
Corresponding author
pjtarraga@sescam.jccm.es

Corresponding author.
, Ángel Arturo López-Gonzáleza
a Multiprofessional Teaching Unit for Occupational Health of the Balearic Islands, Spain
b Faculty of Medicine, University of Castilla-La Mancha, Albacete, Spain
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Abstract
Background

Atherogenic dyslipidemia is a significant but often underrecognized cardiovascular risk factor in individuals with normal weight. Traditional metrics such as body mass index (BMI) may fail to identify metabolically unhealthy phenotypes. This study aimed to assess the prevalence and sociodemographic and lifestyle determinants of atherogenic risk using lipid-based indices in a large cohort of normal-weight Spanish workers.

Methods

A cross-sectional study was conducted in a sample of 166,008 normal-weight workers (BMI 18.5–24.9kg/m2). Four atherogenic risk indices were calculated: TC/HDL-c, LDL/HDL-c, TG/HDL-c, and a composite Dyslipidemia Atherogenic Index (DA). Multivariate logistic regression analyses were performed to assess associations with age, sex, educational level, occupational category (CNAE-11), physical activity (IPAQ), adherence to the Mediterranean diet (MEDAS), smoking, and alcohol consumption.

Results

Male sex, older age, low physical activity, and poor adherence to the Mediterranean diet were independently associated with elevated values in all four atherogenic indices. The strongest associations were observed for DA with sedentary lifestyle (OR: 9.15; 95% CI: 8.08–10.23) and poor diet quality (OR: 5.23; 95% CI: 4.39–6.07). Notably, 17–22% of normal-weight workers showed abnormal lipid ratios suggestive of increased cardiometabolic risk.

Conclusions

Normal BMI does not guarantee cardiometabolic health. A considerable proportion of normal-weight individuals exhibit lipid profiles consistent with atherogenic dyslipidemia, largely driven by modifiable lifestyle factors. Lipid-based indices are valuable tools for early risk identification in occupational settings and should complement traditional anthropometric screening.

Keywords:
Atherogenic indices
Mediterranean diet
Physical activity
Sociodemographic variables
Blue collar
White collar
Resumen
Antecedentes

La dislipidemia aterogénica es un factor de riesgo cardiovascular significativo, pero frecuentemente subestimado en las personas con peso normal. Las métricas tradicionales como el índice de masa corporal (IMC) pueden no identificar fenotipos metabólicamente no saludables. El objetivo de este estudio fue analizar la prevalencia y los determinantes sociodemográficos y de estilo de vida del riesgo aterogénico mediante índices lipídicos en una amplia cohorte de trabajadores españoles con normopeso.

Métodos

Estudio transversal realizado en 16.6008 trabajadores con normopeso (IMC: 18,5-24,9kg/m2). Se calcularon 4 índices de riesgo aterogénico: TC/HDL-c, LDL/HDL-c, TG/HDL-c y un índice compuesto de dislipidemia aterogénica (DA). Se aplicaron modelos de regresión logística multivariante para analizar asociaciones con edad, sexo, nivel educativo, categoría ocupacional (CNAE-11), de actividad física (IPAQ), adherencia a la dieta mediterránea (MEDAS), tabaquismo y consumo de alcohol.

Resultados

El sexo masculino, la mayor edad, la inactividad física y la baja adherencia a la dieta mediterránea se asociaron de forma independiente con valores elevados en los cuatro índices aterogénicos. Las asociaciones más fuertes se observaron para DA con sedentarismo (OR: 9,15; IC 95%: 8,08-10,23) y baja calidad dietética (OR: 5,23; IC 95%: 4,39-6,07). Entre el 17 y el 22% de los trabajadores con normopeso presentaron razones lipídicas anormales compatibles con riesgo cardiometabólico.

Conclusiones

Tener un IMC normal no implica necesariamente buena salud cardiometabólica. Una proporción considerable de personas con peso normal presenta perfiles lipídicos compatibles con DA, en gran medida determinados por factores de estilo de vida modificables. Los índices lipídicos representan herramientas útiles para la detección precoz del riesgo en entornos laborales, y deben complementar la evaluación antropométrica tradicional.

Palabras clave:
Índices aterogénicos
Dieta mediterránea
Actividad física
Variables sociodemográficas
Trabajadores manuales
Trabajadores no manuales

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References
[1]
S. Jebari-Benslaiman, U. Galicia-García, A. Larrea-Sebal, J.R. Olaetxea, I. Alloza, K. Vandenbroeck, et al.
Pathophysiology of atherosclerosis.
Int J Mol Sci, 23 (2022), pp. 3346
[2]
V. Fuster, A. García-Álvarez, A. Devesa, V. Mass, R. Owen, A. Quesada, et al.
Influence of subclinical atherosclerosis burden and progression on mortality.
J Am Coll Cardiol, 84 (2024), pp. 1391-1403
[3]
GBD 2021 Stroke Risk Factor Collaborators.
Global, regional, and national burden of stroke and its risk factors, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021.
Lancet Neurol, 23 (2024), pp. 973-1003
[4]
M. Batty, M.R. Bennett, E. Yu.
The role of oxidative stress in atherosclerosis.
[5]
L.L. Bu, H.H. Yuan, L.L. Xie, M.H. Guo, D.F. Liao, X.L. Zheng.
New dawn for atherosclerosis: vascular endothelial cell senescence and death.
[6]
V. Singh, R. Kaur, P. Kumari, C. Pasricha, R. Singh.
ICAM-1 and VCAM-1: gatekeepers in various inflammatory and cardiovascular disorders.
[7]
S. Robichaud, A. Rasheed, A. Pietrangelo, A. Doyoung Kim, D.M. Boucher, C. Emerton, et al.
Autophagy is differentially regulated in leukocyte and nonleukocyte foam cells during atherosclerosis.
Circ Res, 130 (2022), pp. 831-847
[8]
G.R.Y. De Meyer, M. Zurek, P. Puylaert, W. Martinet.
Programmed death of macrophages in atherosclerosis: mechanisms and therapeutic targets.
Nat Rev Cardiol, 21 (2024), pp. 312-325
[9]
R. Chen, D.G. McVey, D. Shen, X. Huang, S. Ye.
Phenotypic switching of vascular smooth muscle cells in atherosclerosis.
J Am Heart Assoc, 12 (2023),
[10]
Z. Li, Y. Zhao, S. Suguro, R. Suguro.
MicroRNAs regulate function in atherosclerosis and clinical implications.
Oxid Med Cell Longev, 2023 (2023),
[11]
M.S. Hussain, O. Afzal, G. Gupta, A.S.A. Altamimi, W.H. Almalki, S.I. Alzarea, et al.
Probing the links: long non-coding RNAs and NF-κB signalling in atherosclerosis.
Pathol Res Pract, 249 (2023), pp. 154773
[12]
Y.H. Oktaviono, A. Dyah Lamara, P.B.T. Saputra, J.N. Arnindita, D. Pasahari, M.E. Saputra, et al.
The roles of trimethylamine-N-oxide in atherosclerosis and its potential therapeutic aspect: a literature review.
Biomol Biomed, 23 (2023), pp. 936-948
[13]
M. Takase, N. Nakaya, T. Nakamura, M. Kogure, R. Hatanaka, K. Nakaya, et al.
Carotid intima media thickness and risk factor for atherosclerosis: Tohoku medical megabank community-based cohort study.
J Atheroscler Thromb, 30 (2023), pp. 1471-1482
[14]
P. Kumar, M. Bhatia.
Coronary artery calcium data and reporting system (CAC-DRS): a primer.
J Cardiovasc Imaging, 31 (2023), pp. 1-17
[15]
N.S. Nurmohamed, A.R. van Rosendael, I. Danad, Q. Ngo-Metzger, P.R. Taub, K.K. Ray, et al.
Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography.
Eur Heart J, 45 (2024), pp. 1783-1800
[16]
V. Tufaro, P.W. Serruys, L. Räber, M.R. Bennett, R. Torii, S.Z. Gu, et al.
Intravascular imaging assessment of pharmacotherapies targeting atherosclerosis: advantages and limitations in predicting their prognostic implications.
Cardiovasc Res, 119 (2023), pp. 121-135
[17]
K. Yamaji, K. Kanenawa, T. Morofuji, R. Nishikawa, K. Imada, H. Kohjitani, et al.
Serial optical coherence tomography assessment of coronary atherosclerosis and long-term clinical outcomes.
J Am Heart Assoc, 13 (2024),
[18]
A.M. Small, A. Pournamdari, G.E.M. Melloni, B.M. Scirica, D.L. Bhatt, I. Raz, et al.
Lipoprotein(a), C-reactive protein, and cardiovascular risk in primary and secondary prevention populations.
JAMA Cardiol, 9 (2024), pp. 385-391
[19]
D. Yuan, J. Chu, H. Lin, G. Zhu, J. Qian, Y. Yu, et al.
Mechanism of homocysteine-mediated endothelial injury and its consequences for atherosclerosis.
Front Cardiovasc Med, 9 (2023),
[20]
S. Lampsas, M. Xenou, E. Oikonomou, P. Pantelidis, A. Lysandrou, S. Sarantos, et al.
Lipoprotein(a) in atherosclerotic diseases: from pathophysiology to diagnosis and treatment.
[21]
C. Luquain-Costaz, I. Delton.
Oxysterols in vascular cells and role in atherosclerosis.
Adv Exp Med Biol, 1440 (2024), pp. 213-229
[22]
G. Zheng, J. Jin, F. Wang, Q. Zheng, J. Shao, J. Yao, et al.
Association between atherogenic index of plasma and future risk of cardiovascular disease in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3: a nationwide prospective cohort study.
Cardiovasc Diabetol, 24 (2025), pp. 22
[23]
X. Wang, P. Wen, Y. Liao, T. Wu, L. Zeng, Y. Huang, et al.
Association of atherogenic index of plasma and its modified indices with stroke risk in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3: a longitudinal analysis based on CHARLS.
Cardiovasc Diabetol, 24 (2025), pp. 254
[24]
M. Qin, B. Chen.
Association of atherogenic index of plasma with cardiovascular disease mortality and all-cause mortality in the general US adult population: results from NHANES 2005–2018.
Cardiovasc Diabetol, 23 (2024), pp. 255
[25]
J.I. Ramírez-Manent, P. Tomás-Gil, J.L. Coll-Villalonga, P. Marti-Lliteras, A.A. López-González, H. Paublini.
Relationship between atherogenic dyslipidemia and lipid triad with scales that assess non alcoholic liver disease in 418,343 Spanish workers.
Acad J Health Sci, 38 (2023), pp. 66-73
[26]
J.I. Ramírez-Manent, P. Tomás-Gil, J.L. Coll-Villalonga, P. Marti-Lliteras, A.A. López-González, H. Paublini.
Association between atherogenic dyslipidemia and lipid triad with cardiovascular risk scales in 418.343 Spanish workers.
Acad J Health Sci, 39 (2024), pp. 9-15
[27]
N. Gautam, J. Mueller, O. Alqaisi, T. Gandhi, A. Malkawi, T. Tarun, et al.
Machine learning in cardiovascular risk prediction and precision preventive approaches.
Curr Atheroscler Rep, 25 (2023), pp. 1069-1081
[28]
J. Fan, T. Watanabe.
Atherosclerosis: known and unknown.
Pathol Int, 72 (2022), pp. 151-160
[29]
M. Cheng, M. Li, Y. Zhang, X. Gu, W. Gao, S. Zhang, et al.
Exploring the mechanism of PPCPs on human metabolic diseases based on network toxicology and molecular docking.
Environ Int, 196 (2025), pp. 109324
[30]
J.R. Weir-McCall, R. Wereski, J. Chen, J. Zheng, J. Kwiecinski, P.D. Adamson, et al.
Socioeconomic status, cardiovascular risk factors and coronary atherosclerosis: a SCOT-HEART trial analysis.
Eur J Prev Cardiol, (2025), pp. zwaf121
[31]
M. Yalcinkaya, A.R. Tall.
Genetic and epigenetic regulation of inflammasomes: role in atherosclerosis.
[32]
S. Xu, I. Ilyas, P.J. Little, H. Li, D. Kamato, X. Zheng, et al.
Endothelial dysfunction in atherosclerotic cardiovascular diseases and beyond: from mechanism to pharmacotherapies.
Pharmacol Rev, 73 (2021), pp. 924-967
[33]
L. Kawtharany, L. Bessueille, H. Issa, E. Hamade, K. Zibara, D. Magne.
Inflammation and microcalcification: a never-ending vicious cycle in atherosclerosis?.
J Vasc Res, 59 (2022), pp. 137-150
[34]
M. Li, S. Xin, R. Gu, L. Zheng, J. Hu, R. Zhang, et al.
Novel diagnostic biomarkers related to oxidative stress and macrophage ferroptosis in atherosclerosis.
Oxid Med Cell Longev, 2022 (2022),
[35]
F. Sánchez-Cabo, V. Fuster, J.C. Silla-Castro, G. González, E. Lorenzo-Vivas, R. Alvarez, et al.
Subclinical atherosclerosis and accelerated epigenetic age mediated by inflammation: a multi-omics study.
Eur Heart J, 44 (2023), pp. 2698-2709
[36]
F. Bonacina, X. Zhang, N. Manel, L. Yvan-Charvet, B. Razani, G.D. Norata.
Lysosomes in the immunometabolic reprogramming of immune cells in atherosclerosis.
Nat Rev Cardiol, 22 (2025), pp. 149-164
[37]
D. Ramoni, F. Carbone, S. Kraler, D. Di Vece, F. Montecucco, L. Liberale.
Inflammation-driven plaque erosion in atherosclerosis: a focus on complement system pathways.
Curr Atheroscler Rep, 27 (2025), pp. 42
[38]
C. Baeza, M. Ribagorda, C. Maya-Lopez, M. Fresno, T. Sanchez-Diaz, A. Pintor-Chocano, et al.
NIK is a mediator of inflammation and intimal hyperplasia in endothelial denudation-induced vascular injury.
[39]
D.N. Vikulova, D. Pinheiro-Muller, G. Francis, F. Halperin, T. Sedlak, K. Walley, et al.
Cardiovascular risk and subclinical atherosclerosis in first-degree relatives of patients with premature cardiovascular disease.
Am J Prev Cardiol, 19 (2024),
[40]
S.Y. Zhou, F.C. Liu, S.F. Chen, J.X. Li, J. Cao, K.Y. Huang, et al.
Life's essential 8 and risk of subclinical atherosclerosis progression: a prospective cohort study.
J Geriatr Cardiol, 21 (2024), pp. 751-759
[41]
T. Sastre-Alzamora, P. Tomás-Gil, H. Paublini, L. Pallarés, J.I. Ramírez-Manent, A.A. López-González.
Relationship between heart age and insulin resistance risk scales in 139634 Spanish workers.
Acad J Health Sci, 39 (2024), pp. 16-22
[42]
L.M. Vieira, C.B.A. Gottschall, D.B. Vinholes, M.A. Martinez-Gonzalez, A. Marcadenti.
Translation and cross-cultural adaptation of 14-item Mediterranean Diet Adherence Screener and low-fat diet adherence questionnaire.
Clin Nutr ESPEN, 39 (2020), pp. 180-189
[43]
M. Mestre-Font, C. Busquets-Cortés, J.I. Ramírez-Manent, P. Tomás-Gil, H. Paublini, A.A. López-González.
Influence of sociodemographic variables and healthy habits on the values of overweight and obesity scales in 386,924 Spanish workers.
Acad J Health Sci, 39 (2024), pp. 27-35
[44]
E. Franco, J. Urosa, R. Barakat, I. Refoyo.
Physical activity and adherence to the Mediterranean diet among Spanish employees in a health-promotion program before and during the COVID-19 pandemic: the Sanitas-healthy cities challenge.
Int J Environ Res Public Health, 18 (2021), pp. 2735
[45]
M. Mestre Font, C. Busquets-Cortés, J.I. Ramírez-Manent, D. Vallejos, T. Sastre Alzamora, A.A. López-González.
Influence of sociodemographic variables and healthy habits on the values of cardiometabolic risk scales in 386924 Spanish workers.
Acad J Health Sci, 39 (2024), pp. 112-121
[46]
J. Millán, X. Pintó, A. Muñoz, M. Zúñiga, J. Rubiés-Prat, L.F. Pallardo, et al.
Lipoprotein ratios: physiological significance and clinical usefulness in cardiovascular prevention.
Vasc Health Risk Manag, 5 (2009), pp. 757-765
[47]
R.Z. Manzanero, A.A. López-González, P. Tomás-Gil, H. Paublini, A. Martínez-Jover, J.I. Ramírez-Manent.
Determination of cardiometabolic risk scales in 7.962 hotel receptionists.
Acad J Health Sci, 39 (2024), pp. 113-122
[48]
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults.
Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III).
JAMA, 285 (2001), pp. 2486-2497
[49]
B.A. Jané, J.I. Ramírez-Manent, S.A. Bote, P.R. Sbert, H.M. González San Mıguel, Á.A. Lopez González.
Determination of cardiovascular risk in 192,711 underweight and normal weight workers.
Turk J Med Sci, 52 (2022), pp. 926-933
[50]
N. Mohammadian Khonsari, P. Khashayar, E. Shahrestanaki, R. Kelishadi, S. Mohammadpoor Nami, M. Heidari-Beni, et al.
Normal weight obesity and cardiometabolic risk factors: a systematic review and meta-analysis.
Front Endocrinol (Lausanne), 13 (2022), pp. 857930
[51]
A. Jia, S. Xu, Y. Xing, W. Zhang, X. Yu, Y. Zhao, et al.
Prevalence and cardiometabolic risks of normal weight obesity in Chinese population: a nationwide study.
Nutr Metab Cardiovasc Dis, 28 (2018), pp. 1045-1053
[52]
A. Romero-Corral, V.K. Somers, J. Sierra-Johnson, Y. Korenfeld, S. Boarin, J. Korinek, et al.
Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality.
Eur Heart J, 31 (2010), pp. 737-746
[53]
P.L. Valenzuela, A. Santos-Lozano, G. Saco-Ledo, A. Castillo-García, A. Lucia.
Obesity, cardiovascular risk, and lifestyle: cross-sectional and prospective analyses in a nationwide Spanish cohort.
Eur J Prev Cardiol, 30 (2023), pp. 1493-1501
[54]
Y. Wu, D. Li, S.H. Vermund.
Advantages and limitations of the body mass index (BMI) to assess adult obesity.
Int J Environ Res Public Health, 21 (2024), pp. 757
[55]
N.M.A. White-Al Habeeb, V. Higgins, A. Wolska, S.R. Delaney, A.T. Remaley, D.R. Beriault.
The present and future of lipid testing in cardiovascular risk assessment.
Clin Chem, 69 (2023), pp. 456-469
[56]
H.S. Yang.
Lipid biomarkers and cardiometabolic diseases: critical knowledge gaps and future research directions.
Metabolites, 15 (2025), pp. 108
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