Atherosclerosis Risk Factors: Systematic Review with ☸️SAIMSARA

DOI: 10.62487/saimsarac3086c87

• Generated: 2026-02-14 02:26:54 CET
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• Source: Europe PMC
• Scope: Titles/Abstracts (tiab)
• Keyword Gate: Fuzzy (≥60% of required terms, minimum 2 terms matched in title/abstract)
• Total Abstracts/Papers: 26255
• Downloaded Abstracts/Papers: 26255
• Included original Abstracts/Papers: 14940
• Total study participants (naïve ΣN): 32682984

• Bias: Qualitatively inferred from study design fields. Cross-sectional studies may infer associations but cannot establish causality, while cohort studies offer stronger evidence for risk factors over time. Case-control studies are prone to recall bias. Retrospective designs are limited by existing data. Mixed study designs vary in their inherent bias. Single case reports or animal studies provide limited generalizability.

• Traditional Modifiable Risk Factors: Hypertension [4, 8, 13, 60, 67, 112, 116, 220, 273, 344, 359, 360, 370, 4050, 4134, 4153, 4680, 4717, 5114, 540, 6163, 657, 662, 675, 7757, 7779, 7790, 8218, 8836, 8991, 9102, 9133, 9340, 9455, 9556, 9593, 962, 10090, 10375, 10450, 10689, 10885, 10913, 10961, 11085, 11333, 11432, 11449, 1178, 12038, 12294, 12433, 12535, 12601, 12641, 13242, 13355, 13803, 13884, 14010, 14103, 14443, 14584, 14592, 14611, 14655, 14677, 14913, 14920], dyslipidemia (high LDL-C, low HDL-C, hyperlipidemia, hypercholesterolemia, hypertriglyceridemia, Lp(a)) [4, 5, 13, 21, 60, 67, 69, 112, 114, 116, 126, 220, 2228, 273, 323, 344, 359, 3859, 4050, 4177, 462, 4680, 5114, 5695, 6108, 656, 662, 7744, 7779, 8218, 8676, 8730, 8991, 9065, 9133, 9152, 9159, 9455, 962, 9804, 9815, 10090, 10375, 10454, 10622, 10885, 10892, 11085, 11182, 11355, 11432, 1178, 12038, 12167, 12294, 12315, 12364, 13130, 13242, 13355, 13613, 13626, 13803, 13987, 14006, 14103, 14443, 14474, 14584, 14592, 14677, 14744, 14796, 14913, 14920], diabetes/hyperglycemia [4, 8, 10, 13, 21, 60, 67, 69, 103, 112, 116, 126, 220, 261, 278, 323, 344, 359, 370, 373, 4050, 411, 462, 4680, 5052, 5099, 540, 5496, 6047, 6121, 657, 662, 675, 7779, 7790, 8218, 8454, 8654, 8661, 8991, 9133, 9159, 9455, 962, 9815, 10090, 10375, 10608, 10622, 10826, 10885, 10892, 10913, 11085, 11285, 11432, 11449, 1178, 12038, 1217, 12294, 12433, 12641, 12697, 12832, 12884, 13101, 13114, 13242, 13355, 13420, 13629, 13974, 14010, 14103, 14244, 14445, 14584, 14592, 14630, 14920], and smoking [4, 8, 10, 13, 21, 60, 112, 116, 220, 273, 344, 370, 4050, 4134, 4148, 4153, 462, 4680, 4749, 4999, 5114, 5985, 6163, 656, 662, 675, 7757, 7790, 8218, 8991, 9065, 9102, 9133, 9340, 9455, 962, 10090, 10351, 10450, 10689, 10694, 10885, 10913, 11085, 11333, 11355, 11432, 1178, 12038, 12433, 12601, 12641, 13242, 13572, 13629, 13803, 13884, 14103, 14443, 14584, 14655, 14677, 14709, 14913, 14920].
• Inflammation & Immune Dysregulation: Chronic inflammation, including elevated hsCRP, IL-6, and TNF-α, is a key driver of atherosclerosis [14, 45, 102, 111, 183, 186, 227, 262, 268, 319, 378, 4026, 4057, 4168, 463, 4811, 5427, 6033, 6047, 637, 651, 6666, 671, 7067, 7153, 7155, 7603, 7688, 7700, 7719, 8218, 8232, 8307, 8453, 8587, 8661, 8991, 9152, 9246, 9556, 957, 9664, 10024, 10109, 10187, 10277, 10310, 10384, 10467, 10647, 10840, 10961, 11153, 11449, 12139, 13410, 13446, 13753, 13873, 13974, 14920]. Autoimmune diseases (e.g., RA, SLE, APS, pSS, AxSpA) are independently associated with accelerated atherosclerosis [14, 59, 111, 378, 463, 604, 8587, 9160, 10024, 10384, 11432, 14920].
• Metabolic Dysfunction & Organ-Specific Conditions: Metabolic syndrome components [21, 278, 3051, 3160, 3814, 4050, 4209, 6085, 7700, 10310, 10622, 10746, 11763, 12038], MASLD/NAFLD [21, 99, 1767, 1779, 2342, 3467, 3499, 1450, 1568, 10826], and chronic kidney disease (CKD) [483, 2209, 2258, 3597, 4168, 6330, 6377, 8485, 8863, 9819, 9929, 10277, 10840, 10913, 12688, 13101, 13355, 13410, 13244, 2048, 4418, 4944, 5099] significantly increase atherosclerosis risk.
• Vascular & Imaging Markers: Carotid intima-media thickness (CIMT) [6, 106, 116, 131, 6121, 6276, 649, 6665, 7153, 7565, 8539, 8738, 10024, 10511, 10592, 11086, 11300, 11333, 11773, 13430, 14325, 2049, 2051, 2093, 2102, 2267, 2810, 3152, 3208, 3259, 3535, 3799, 3981, 4250, 4649, 4968, 5114, 5363, 5399, 5695, 6798, 7719], arterial stiffness (PWV) [3, 131, 5273, 5682, 6065, 6121, 6255, 6557, 8054, 8836, 9764, 10668, 10754, 11903, 12592, 13031, 3065], carotid plaque composition (lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH)) [1, 807, 11397, 9102], and coronary artery calcification (CAC) [47, 99, 2037, 2316, 2464, 3663, 3688, 3818, 3860, 5008, 6391, 8002, 8229, 10362, 10645, 10689, 11936, 13064, 14087, 416] are key indicators of subclinical atherosclerosis and disease progression. Coronary computed tomography angiography (CCTA) and dual-impedance cardiography (d-ICG) pulse wave velocity (PWV) are reliable assessment methods [3, 47, 535].
• Genetic & Epigenetic Predisposition: Genetic factors, such as the ApoE ε4 allele [2899, 10948], PLA2G7 polymorphisms [11339], and polygenic risk scores (PRS) [2059, 2347, 4500], are associated with atherosclerosis risk. Epigenetic age acceleration is also a potential risk marker [5553, 7337].
• Novel & Emerging Biomarkers: The Metabolic Vulnerability Index (MVX) [28], FIB-4 index [99, 1450], serum uric acid (SUA) [319, 649, 1857, 2340, 2810, 9593], homocysteine (HCY) [957, 1883, 2340, 14325], lipoprotein(a) (Lp(a)) [126, 1626, 1661, 2092, 2464, 5695, 6283, 12315], and inflammatory markers (e.g., sFas, CCL23, osteopontin, Gal-3BP, sSortilin, NLR/CVAI, AISI, proNT, TropI, sCD36, NT-proBNP) [28, 99, 138, 1693, 1878, 319, 323, 3710, 397, 462, 8229, 831, 8520, 957, 12688, 1450, 2102, 3856, 7155] offer additional predictive value beyond traditional risk factors.
• Lifestyle & Environmental Influences: Unhealthy lifestyle factors including smoking, high-salt diet, sedentary lifestyle, and obesity are identified as significant risk factors [4, 60, 344, 1497]. Visit-to-visit variability in SBP, BMI, and HDL-C is associated with increased CVD risk [5]. Night-time light pollution [691], air pollution [1609, 2861, 4649], and poor sleep quality/duration [3044, 5273, 5682, 10109, 13851, 1685, 2349, 4446] are also emerging risk factors.
• Comorbidities & Disease-Specific Contexts: Atherosclerosis risk is heightened in specific conditions like HIV [102, 268, 3085, 3486, 4057, 540, 2641, 6954], rheumatoid arthritis (RA) [14, 1532, 1681, 1773, 1849, 2375, 3259, 3590, 7153, 8587, 8661, 9160, 10024, 10384, 11432, 14920], systemic lupus erythematosus (SLE) [1704, 1849, 3799, 5363, 7700, 8587, 10384, 11432, 14920], type 1 diabetes (T1D) [261, 373, 6767, 9815, 13420], and polycystic ovary syndrome (PCOS) [2122, 10375, 9786].
• Early Life & Progression: Atherosclerosis begins in youth and progresses in response to cardiovascular risk factors [177, 12832, 12309, 206, 3153, 4150, 5287, 6121, 11953]. The degree of progression is directly associated with the cumulative number of cardiometabolic risk factors [21, 112, 3162].

• Early Intervention: Atherosclerosis starts in youth, necessitating intensified efforts to identify and intervene on risk factors in children and young adults [177, 12832, 12309].
• Comprehensive Risk Management: Holistic control of cardiovascular risk factors, including lifestyle modifications (diet, exercise, weight management, smoking cessation) and pharmacotherapy (statins, antihypertensives, glucose-lowering agents), is crucial for prevention and progression mitigation [70, 278, 373, 1029, 1497, 2602, 7933, 10885, 11182, 11355, 13629, 14208, 14796].
• Advanced Diagnostics: Imaging techniques like CCTA, CIMT, and PWV offer valuable tools for early detection and risk stratification, even in asymptomatic individuals, improving prediction beyond traditional risk factors [3, 47, 105, 114, 227, 535, 1571, 2066, 2314, 3663, 387, 3987, 505, 516, 5247, 8794, 9027, 10511, 14087].
• Comorbidity-Specific Screening: Patients with autoimmune diseases (e.g., RA, SLE, AxSpA, APS, pSS), HIV, MASLD/NAFLD, or CKD require enhanced screening and management due to their heightened and often independent risk of accelerated atherosclerosis [14, 59, 102, 111, 2122, 261, 268, 378, 463, 604, 651, 8587, 9160, 9929, 10024, 10384, 11432, 14920, 1532, 1681, 1704, 1773, 1849, 2048, 2375, 3259, 3486, 3590, 4057, 4811, 540, 7153, 7700, 8661, 10375, 10826].

• Longitudinal Biomarker Validation: Further prospective studies are needed to validate emerging biomarkers (e.g., MVX, FIB-4, sFas, CCL23, osteopontin, Gal-3BP, sSortilin, NLR/CVAI, AISI, proNT, HCY, Lp-PLA2, CAR, inositol, indoxyl sulfate, TropI, sCD36, NT-proBNP, EV-circRNAs, protein S-nitrosylation) as independent predictors of atherosclerosis progression and clinical events across diverse populations [28, 99, 138, 1693, 1878, 319, 323, 3710, 397, 462, 8229, 831, 8520, 957, 12688, 1450, 2102, 3856, 7155, 1796, 69].
• Environmental Risk Factor Mechanisms: Research should explore the precise biological mechanisms by which environmental factors like night-time light pollution and air pollution contribute to atherogenesis, including their impact on endothelial function, inflammation, and oxidative stress [691, 1609, 2861, 4649, 9593, 13757, 9556].
• Personalized Risk Prediction Models: Development and validation of advanced machine learning models integrating genetic, multi-omics, imaging, and traditional risk factors are needed to improve individualized risk assessment and guide precision medicine approaches [114, 116, 387, 4500, 505, 2066, 2143, 2347, 2899, 3282, 349, 4033, 4940, 5008, 5328, 8771, 9044, 971].
• Targeted Interventions for Non-Traditional Factors: Clinical trials are warranted to evaluate interventions specifically targeting non-traditional risk factors (e.g., chronic inflammation, sleep disorders, psychological stress, specific genetic predispositions) and their impact on atherosclerosis outcomes [14, 45, 183, 186, 197, 3044, 3208, 378, 4026, 4079, 4737, 4770, 5273, 5682, 6033, 6064, 7153, 7471, 8587, 9184, 951, 10109, 10384, 11153, 11432, 11903, 13851, 14195, 14920].

• Heterogeneity of Study Designs — The diverse range of study designs (cohort, cross-sectional, RCT, mixed) limits direct comparisons and meta-analysis, impacting the strength of overall conclusions.
• Variability in Population & Setting — Studies spanned various populations (e.g., middle-aged adults, specific ethnic groups, patients with comorbidities) and settings, affecting the generalizability of findings.
• Inconsistent Outcome Metrics — Different studies reported various quantitative measures (e.g., HR, OR, percentages, CIMT values, plaque volumes) for different risk factors, hindering a unified numerical synthesis.
• Lack of Causal Inference — Many studies, particularly cross-sectional designs, identify associations rather than causal relationships, which limits definitive conclusions about risk factor etiology.
• Missing Data & Unspecified Details — Several summaries lacked specific statistical details (e.g., N/A for statistics, follow-up, or sample size), making it challenging to fully assess the rigor and scope of individual findings.

• Standardize Outcome Measures — Adopt consistent metrics for atherosclerosis risk factors across studies.
• Conduct Large-Scale RCTs — Evaluate novel interventions for non-traditional risk factors.
• Integrate Multi-Omics Data — Develop more comprehensive and predictive risk models.
• Investigate Early Life Interventions — Focus on preventing atherosclerosis initiation in youth.
• Explore Environmental Impact — Research the mechanisms of environmental pollutants on vascular health.