Atherogenic index of plasma in newly diagnosed psoriasis: A cross-sectional study from Southern India

INTRODUCTION

The prevalence of psoriasis in India is said to range from 0.44% to 2.8%.^[1] Psoriasis was initially considered a purely cutaneous disease; however, it is now recognized as a condition associated with systemic inflammation.^[2]

Systemic inflammation observed in patients with psoriasis gives rise to the concept of the “Psoriatic March.” Coined by Boehncke et al. in 2010, this concept captures the complex link between psoriasis, chronic inflammation, and cardiovascular risk.^[3] It also emphasizes the reciprocal relationship between psoriasis and systemic inflammation, contributing to insulin resistance, endothelial dysfunction, and atherosclerosis.

Individuals with psoriasis exhibit elevated plasma lipid and lipoprotein levels, which predispose them to atherosclerosis.^[4] Lipid abnormalities may be identified up to five years before the clinical onset of the disease.^[5]

As is already known, higher high-density lipoprotein cholesterol (HDL-C) levels decrease cardiovascular morbidity risk, whereas elevated triglycerides (TG) contribute to atherosclerosis and metabolic dysfunction. The atherogenic index of plasma (AIP), calculated as log₁₀ (TG/HDL-C), is an emerging, inexpensive surrogate biomarker of cardiovascular risk that reflects the balance of pro- and anti-atherogenic forces. Elevated AIP values are suggested to be associated with mitral stenosis, obesity, and insulin resistance, thereby increasing the possibilities of cardiovascular disease (CVD) due to atherosclerosis.^[2,4,5]

Despite the established understanding of psoriasis as an atherogenic condition, only a handful of studies have validated this.^[6] Contemporary research supports AIP as a reliable, cost-effective marker of atherosclerosis.^[2] This study aims to fill gaps in the existing literature and to assess and evaluate this index.

MATERIALS AND METHODS

This study had the following objectives:

1. To compare the fasting lipid profile of newly diagnosed cases of psoriasis with that of the normal population.

2. To estimate and compare the risk of developing cardiovascular morbidity in newly diagnosed psoriasis cases with that of the normal population using the AIP.

3. To compare the relation between AIP and disease severity, calculated by the psoriasis area and severity index (PASI).

The two-sample t-test formula proposed by Kirkwood and Sterne was employed in the sample size estimation.^[7] To detect a mean difference in AIP between psoriasis cases and controls, a two-sided α of 0.05 and a study power of 95% were assumed. The calculation was based on the expected mean (μ) and standard deviation (σ) of AIP reported by Sunitha et al.^[2] for newly diagnosed psoriasis cases (μ₁ = 0.80, σ₁ = 0.30) and healthy controls (μ₀ = 0.56, σ₀ = 0.25). Using these estimates, a minimum of 45 subjects in each study group was required. To account for potential attrition, 50 newly diagnosed, treatment-naïve psoriasis patients and an equal number of age- and gender-matched apparently healthy controls were enrolled in the study. Written informed consent was obtained. The Institutional Ethics Committee reviewed and approved the study protocol (EC No. 266, Date of approval: March 14, 2023). The study adhered to the principles detailed in the Declaration of Helsinki.

The following formed the exclusion criteria for the study group: psoriatic erythroderma, patients with diseases causing secondary hyperlipidemia (e.g., nephrotic syndrome, renal insufficiency, liver disorders, connective tissue disorders), patients on corticosteroids, retinoids, cyclosporine, or lipid-lowering agents, conditions causing secondary hypolipidemia or hypoproteinemia (e.g., malnutrition, liver failure, malabsorption syndromes) and conditions causing secondary hyperproteinemia. The participants were enrolled from the Dermatology Outpatient Department during the study period (June 2023 to January 2025). The study design eliminated the need for randomization. Each participant underwent a routine clinical evaluation and basic laboratory screening. A detailed personal and family history of psoriasis and CVD was obtained.

Individuals with a personal or family history of psoriasis were excluded from the control group. In addition, individuals with any evidence of systemic disease on history or examination, and those with other cutaneous disorders known to cause dyslipidemia, such as acne vulgaris, dermatosis papulosa nigra, acanthosis nigricans, lichen planus, discoid lupus erythematosus, pemphigus, granuloma annulare, and histiocytosis, were excluded.

Disease duration was defined as the interval between the patient-reported onset of cutaneous lesions and the initial clinical diagnosis established at the time of first hospital presentation. Newly diagnosed psoriasis was operationally defined as follows: A patient with clinically confirmed psoriasis diagnosed for the first time at the index visit, with a disease duration of ≤15 months from patient-reported onset of cutaneous lesions, and no prior topical or systemic antipsoriatic therapy. PASI was used to assess psoriasis severity, with two dermatologists independently scoring patients to derive mean scores. Psoriasis cases were subclassified as severe (PASI>12), moderate (PASI 7 -12), and mild (PASI <7), based on the classification proposed by Schmitt and Wozel.^[8]

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in both groups. Height and weight were also recorded to determine body mass index (BMI), calculated as weight (kg) divided by the square of height (m²).^[9]

Waist circumference (WC) was measured at the midpoint between the lower border of the rib cage and the iliac crest. Hip circumference (HC) was measured at the widest portion of the buttocks. Waist–hip ratio (WHR) was derived as the ratio of WC to HC.^[10]

Lipid accumulation product (LAP) index was calculated using sex-specific formulae as follows: (LAP = [WC (cm) – 65] × TG [mmol/L]) for men and (LAP = [WC (cm) – 58] × TG (mmol/L)) for women.^[11]

After a 12-h period of fasting, 5 mL of venous blood was drawn from the antecubital vein. Within 30 min, after the separation of serum, routine biochemical tests, including the lipid profile, were performed on all subjects using enzymatic colorimetric assays on a calibrated Olympus AU400 automated clinical chemistry analyzer (Olympus Corporation, Tokyo). Internal quality control and external proficiency testing were performed as per the laboratory’s standard operating procedures.

AIP was calculated as the base-10 logarithm of the molar ratio of TG to HDL-C, that is, AIP = log₁₀ (TG [mmol/L]/HDL-C [mmol/L]).^[12] For computation, serum TG and HDL-C values measured in mg/dL were converted to mmol/L using TG (mmol/L) = TG (mg/dL)/88.57 and HDL-C (mmol/L) = HDL-C (mg/dL)/38.67. Cardiovascular risk categories based on AIP were as follows: an AIP of −0.3 to <0.11 indicated low risk, 0.11 to <0.24 denoted intermediate risk, and a value of ≥0.24 suggested high risk.^[13]

Castelli’s Risk Index–1 (CRI-I) was measured as the ratio of total cholesterol (TC) to HDL-C, while CRI-II was calculated as the ratio of low-density lipoprotein (LDL) to HDL-c. A CRI-I value above 4 suggests a higher risk of CVD, as it indicates the presence of coronary plaque formation. Similarly, a CRI-II level exceeding 3 predicts an elevated risk of CVD, acute myocardial infarction, and insulin resistance.^[13]

The atherogenic coefficient (AC) is another ratio that predicts CVD risk relying on HDL-c. It was calculated as: AC = (TCHDL-c)/HDL-c.^[14]

The comprehensive lipid tetrad index (CLTI) was derived by multiplying TC, TG, and lipoprotein (a) and dividing the product by HDL-c. CLTI = (TC × TG × lipoprotein [a])/HDL-c.^[15]

Non-HDL cholesterol (non-HDL-C) was calculated as TC–HDL-C as defined by the NCEP ATP III guidelines.^[16] It represents the aggregate of LDL, very LDL (VLDL), intermediate-density lipoprotein, and lipoprotein (a). The Framingham risk score (FRS) for 10-year cardiovascular risk estimation was calculated using the standard Framingham algorithm.^[17]

RESULTS

Among the participants enrolled, the demographic profile revealed a middle-aged cohort with a male predominance [Table 1]. The largest group comprised thirteen cases (26%) aged 50–59, closely followed by nine cases (18%) in the 40– 49-year range and eight cases (16%) in the 20–29-year range. Seven cases (14% each) were observed in the 30–39 and 60–69 age groups, four cases (8%) in the 10–19-year range, and two cases (4%) in the 70–79 age group. The mean age was 43.38 ± 15.95 years. The youngest participant was 12, whereas the oldest was 71. Thirty-two (64%) were males, and 18 (36%) were females. Thus, the male-to-female ratio was 1.77:1.

The disease duration from onset was documented in months for each case. It ranged from a minimum of 1 month to a maximum of 15 months, with an average of 7.2 ± 4.24 months.

A higher prevalence of lifestyle risk factors and metabolic comorbidities was noted in the psoriasis group. A larger percentage of psoriasis patients reported alcohol consumption (32% versus 24%) and smoking (40% versus 26%); however, these did not assume statistical significance (p = 0.373 and 0.137, respectively). Hypertension and diabetes were present in 36% and 32% of cases, respectively. Hemodynamic assessment revealed a statistically significant elevation in SBP among psoriasis patients (129.0 ± 12.84 vs. 122.0 ± 12.17 mmHg; p = 0.009), while the differences in DBP were not significant (p = 0.107).

As mentioned in Table 1, the anthropometric analysis revealed a distinct pattern of central adiposity. Although common obesity indicators such as BMI (p = 0.157) and WHR (p = 0.917) showed no significant differences between groups, markers of visceral fat were markedly different. WC (p = 0.029) and HC (p = 0.016) were notably higher in the case group. Furthermore, the LAP index, a sensitive marker of visceral fat, was markedly elevated in psoriasis patients (p <0.001).

The minimum and maximum recorded PASI were 0.4 and 13.60, respectively, with an overall mean of 5.90 ± 4.10. The study cohort included 30 patients (60%) with mild psoriasis, 13 (26%) with moderate psoriasis, and 7 (14%) with severe psoriasis.

Table 1 summarizes the comparison of fasting lipid profile parameters between the cases and controls. Patients with psoriasis exhibited notably higher TG and VLDL levels (p <0.001). HDL-C levels were significantly lower (p = 0.011). On the contrary, there was no significant difference in TC and LDL levels (p = 0.444 and p = 0.443, respectively).

Traditional atherogenic ratios, such as AC (p = 0.073) and CRI-I and CRI-II (p = 0.073 and p = 0.193), showed no significant differences between cases and controls.

The mean AIP was significantly higher in psoriasis cases (0.115 ± 0.092) than in controls (0.0427 ± 0.0781) (t = 4.22, p <0.001), with a large effect size (Cohen’s d = 0.843). The average difference between the groups was 0.0719 (95% CI: 0.038 to 0.106). Using conventionally applied AIP categories for cardiovascular risk assessment, a notable disparity emerged: 16.0% of psoriasis patients were labelled high risk, whereas no controls fell into this category; 38.0% were deemed intermediate risk, compared to only 20.0% of controls (p <0.001). AIP correlated strongly with PASI scores (r = 0.638, p <0.001). Consistent with this correlation, the mean AIP rose progressively with disease severity: 0.064 ± 0.067 in mild disease, 0.166 ± 0.072 in moderate disease, and 0.236 ± 0.037 in severe disease. The statistical significance of these differences was confirmed by one-way analysis of variance (p <0.001).

Correlation analysis in the psoriasis group revealed that AIP was closely linked to several parameters. These findings are detailed in Table 2 and illustrated in Figure 1. The strongest positive correlations were with PASI (r = 0.638, p <0.001) and the LAP index (r = 0.524, p <0.001). In addition, significant connections were found with disease duration (r = 0.412, p <0.001). Among anthropometric measures, BMI showed a weak but statistically significant positive correlation (r = 0.225, p = 0.025).

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Figure 1: Scatter plots illustrating the correlation between the atherogenic index of plasma and psoriasis area and severity index, disease duration, and the lipid accumulation product index among psoriasis cases. AIP: Atherogenic index of plasma, PASI: Psoriasis area and severity index, LAP: Lipid accumulation product.

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A comprehensive multiple linear regression analysis was conducted to identify independent predictors of AIP and to control for possible confounders such as comorbidities and lifestyle factors [Table 3]. The model incorporated age, PASI, disease duration, BMI, smoking, alcohol use, hypertension, and diabetes. Results showed that PASI score remained a significant independent predictor of AIP (β = 0.011, p <0.001), even after adjusting for all confounders. Interestingly, traditional cardiovascular risk factors such as BMI (p = 0.435), smoking (p = 0.447), alcohol consumption (p = 0.108), diabetes (p = 0.297), and hypertension (p = 0.162) did not have a significant independent association with AIP within this cohort. The model explained 76.3% of the variance in AIP (adjusted R² = 0.763).

ROC analysis was conducted to evaluate AIP’s ability to discriminate atherogenic risk in psoriasis patients [Figure 2]. The AUC was 0.716 (95% CI: 0.615–0.817). Using Youden’s Index, an optimal cut-off value of 0.131 was determined. At this threshold, AIP demonstrated a specificity of 92.0%, sensitivity of 46.0%, and a positive LR+ of 5.75, indicating a moderate increase in the likelihood of identifying atherogenic risk in cases. The LR was 0.59. Furthermore, ROC analysis with DeLong’s test demonstrated that AIP performed significantly better diagnostically than traditional lipid ratios.

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Figure 2: Combined receiver operating characteristic curves for atherogenic index of plasma, atherogenic coefficient, Castelli’s Risk Index 1 (CRI-I), CRI-II, lipid accumulation product index, comprehensive lipid tetrad index, and non-high-density lipoprotein cholesterol. The diagonal line represents the reference line of no discrimination.

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A sensitivity analysis was performed to evaluate the impact of disease severity on diagnostic accuracy by excluding mild cases and concentrating solely on patients with moderate-to-severe psoriasis (n = 20). In this subgroup, the diagnostic performance of AIP improved dramatically. The AUC rose to 0.924 (95% CI: 0.859–0.990), reclassifying the test as having “Excellent” clinical utility [Figure 3]. At the adjusted optimal cut-off of 0.114, AIP demonstrated a sensitivity of 85.0% and a specificity of 84.0%, with an LR+ of 5.31. Pairwise comparison in this subgroup showed that AIP maintained superior diagnostic performance over non-HDLc (p <0.001) and AC (p <0.001).

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Figure 3: Sensitivity analysis: Receiver operating characteristic curves for atherogenic indices in the subgroup of patients with moderate-to-severe psoriasis (n = 20).

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DISCUSSION

The current study involving a Southern Indian cohort found that AIP levels were higher in newly diagnosed, treatment-naïve psoriasis patients, correlating with the severity of disease, duration, as well as measures of adiposity such as BMI, WHR, and LAP index. Table 4 presents a comparison between the present study’s findings and those of earlier research.

Psoriasis can appear at any age, but two main onset periods are recognized: One between 20 and 30 years old, and another between 50 and 60 years.^[19] This may explain the mean age at onset of 43.38 ± 15.95 among the enrolled psoriasis patients.

A male-to-female ratio of 1.77:1 was observed, depicting a male predominance, consistent with most previous reports. However, Aksoy et al. identified a higher female prevalence in their study.^[20] Although psoriasis is often considered equally common in both genders, a higher prevalence among men is noted in most Indian studies. This is possibly due to greater healthcare-seeking behavior among men and sociocultural stigma that deters women from reporting symptoms.^[6,21]

The mean duration of symptoms since onset was 7.2 ± 4.24 months. This was comparable to a survey by Mallbris et al., wherein the average time from onset of disease to cutaneous evaluation was four months for guttate and seven months for non-guttate patients.^[22] However, Wadhwa et al. reported a mean duration of 77.34 ± 85.49.^[5] This could be plausibly due to the inclusion of only newly diagnosed, treatment-naïve patients. This also supports the lower mean PASI of 5.90 ± 4.10, compared to higher mean scores in other studies. PASI not only considers the severity, but also the extent of lesions. A higher PASI indicates a more severe disease.^[23]

In the study, 40% of patients with psoriasis gave a positive history of smoking, and 32% of the patients admitted to consuming alcohol, consistent with findings reported by Sunitha et al. and Wadhwa et al.^[2,5] Smoking plays a role in the onset of severe psoriasis and in the development of psoriatic arthritis. Excessive alcohol intake is linked to moderate to severe psoriasis, psychological issues, and metabolic derangement.^[23] Patients with psoriasis should be screened for smoking and alcohol misuse, and necessary support should be provided to help them quit.

In the present study, coexisting hypertension and diabetes were present in 36% and 32% of the patients with psoriasis, respectively, comparable to Aksoy et al.’s study.^[20]. According to recent meta-analytical data, there is a 2.31-fold increased likelihood of developing hypertension, comparable odds for diabetes, and a 2.07-fold elevation in the probability of developing metabolic syndrome in a patient with psoriasis. This underscores the necessity of systematic cardiometabolic evaluation in affected individuals.^[20]

The mean values of SBP and DBP in patients with psoriasis (129.00 ± 12.84/82.80 ± 8.16 mmHg) are comparable to those in Mannangi et al.’s study.^[24] Psoriasis and hypertension are often regarded as comorbid conditions. The risk of developing hypertension remains even after eliminating confounding factors such as age, sex, smoking, obesity, diabetes, and nonsteroidal anti-inflammatory drugs.^[25]

Psoriasis patients have elevated angiotensin-converting enzyme and increased plasma renin levels, known to cause hypertension. It has been proposed that this association is due to angiotensin-II, which increases vascular tone. Research shows that angiotensin-II-induced hypertension may be promoted by elevated circulating interleukin-17 (IL-17), an important mediator in the pathogenesis and a prominent target for treatment of psoriasis. Angiotensin II, antidiuretic hormone, and other peptides stimulate the vascular endothelium to release endothelin-1, which also causes vasoconstriction.^[25]

The mean BMI of the patients with psoriasis in our study was 25.04 ± 4.72, comparable to that reported by Asha et al.^[26] Psoriasis and obesity share complex pathophysiological mechanisms. Activated macrophages from adipose tissue induce the secretion of cytokines such as tumor necrosis factor-α, IL-1, IL-6, and IL-8, potentially contributing to the development of psoriasis.^[27]

The TG-to-HDL-C ratio was identified to be a strong predictor of myocardial infarction. To more accurately represent this relationship, the log-transformed molar ratio of TG/HDL-C, called the AIP, was used. AIP is in logarithmic form because it offers better correlations and normal probability plots, making it more statistically appropriate than the simple TG/HDL-C ratio. The correlation plot between the simple ratio and HDL-C and LDL particle sizes is curvilinear, but the log-transformed ratio is linear.^[12]

The mean AIP in psoriasis patients was higher than in the control group. The observed trend toward higher AIP in moderate and severe psoriasis aligns with the proposition that increasing systemic inflammation contributes to dyslipidemia. Vata et al., too, reported higher AIP levels in psoriasis patients with elevated PASI scores.^[27] AIP showed a strong correlation with both disease severity and duration, along with the LAP index. This indicates that chronic underlying inflammation, coupled with adiposity, is the predominant driver of atherogenicity. Chronic cutaneous inflammation in psoriasis progresses to systemic inflammation, triggering a cascade of events and increasing oxidative stress. Chronic Th1-mediated inflammation, along with increased influx of inflammatory cells and cytokines such as TNF-α, leads to oxidative stress and the generation of reactive oxygen species. Oxidative stress, dyslipidemia, endothelial dysfunction, and insulin resistance all heighten the risk of cardiovascular morbidity in individuals with psoriasis.^[2,5]

Cytokine-maintained inflammation in psoriasis causes atherogenic dyslipidemia. LDL oxidation promotes monocyte infiltration and proliferation of smooth muscle cells, which facilitates the formation of atherosclerotic plaques. HDL-C, in contrast, participates in reverse cholesterol transport and inhibits monocytic infiltration, thus suppressing atherogenicity.^[2]

Recent immunologic evidence indicates that psoriasis and atherosclerosis share a common Th1/Th17-mediated inflammatory pathway. Chronic activation of Th1 and Th17 cells in psoriasis results in the release of cytokines such as IL-17, IL-22, TNF-α, and IFN-γ, which act on endothelial cells. This induces the expression of adhesion molecules, causes oxidative stress, and also results in vascular inflammation. These cytokines recruit and activate macrophages and dendritic cells, which, in turn, amplify the inflammatory loop by producing IL-23, IL-1β, and TNF-α, thereby sustaining leukocyte infiltration and lipid oxidation within the vessel wall. The resulting endothelial dysfunction and changes in lipoprotein handling promote an atherogenic lipid profiles, which account for the elevated AIP seen even in early psoriasis. This upholds the necessity to screen for early metabolic alterations, even in mild cases.^[28]

Interestingly, the correlation between AIP and the FRS was significant in cases of psoriasis (r = 0.392, p = 0.005). The FRS is a well-established tool for estimating the 10-year cardiovascular risk in the general population.^[17] The observed association between AIP and FRS may indicate that AIP reflects early lipid disturbances related to inflammation. Extended studies on larger populations are necessary to determine whether this is true across different disease severities and over time.

Several other lipid-derived ratios have also been utilized in the assessment of cardiovascular risk. Among these, AC, CRI-I and II, CLTI, and non-HDL cholesterol are well established and have been validated in extensive population studies as reliable predictors of CVD. Wadhwa et al. compared these indices in psoriasis patients and found that AIP and AC showed stronger associations with disease severity and oxidative stress.^[5] Unlike cholesterol-based ratios, AIP reflects the TG-HDL-C interaction and the predominance of small, dense LDL particles, which are frequently described in psoriasis and are linked to inflammation and insulin resistance.

No significant differences were observed in traditional cholesterol-based ratios, including AC, the Castelli indices, CLTI, and non-HDL-C, between cases and controls. This highlights the potentially superior discriminatory value of AIP, particularly in new-onset psoriasis. Conversely, AIP showed marked separation between groups, with a strong dose–response relationship to increasing PASI scores. This diagnostic advantage was statistically substantiated by DeLong’s test, indicating that AIP may be more sensitive than conventional markers in capturing the subtle, inflammation-driven lipid derangements characteristic of the disease. AIP appears to offer a practical advantage for detecting subclinical atherogenic risk that might otherwise remain occult when relying solely on standard lipid ratios, particularly in patients with moderate-to-severe disease phenotypes. Establishing long-term follow-up and objective assessments is essential to verify the accuracy, sensitivity, specificity, reliability, reproducibility, and consistency of this index across larger, diverse populations.

There are several unique features in the present study that differentiate it from previous research and offer initial observations that could serve as the basis for future studies.

A primary distinction was the focus on newly diagnosed, treatment-naïve patients, which aimed to minimize the confounding effects of systemic therapy, chronic disease duration, and metabolic adaptations secondary to prolonged inflammation, thereby permitting an assessment of lipid profiles closer to the disease’s initial onset. In addition, a positive correlation between AIP and FRS is a finding that, while preliminary and not conclusive, provides scope for future studies to validate AIP against established cardiovascular risk models in this specific patient population.

Our analysis showed that the association between PASI and AIP remains significant even after adjusting for confounding factors such as BMI, smoking, alcohol consumption, hypertension, and diabetes. This implies that the elevated atherogenic risk in these patients is not merely a bystander effect of their lifestyle or comorbidities but is directly driven by the psoriatic disease process itself, aligning with the “Psoriatic March” concept, where chronic skin inflammation fuels systemic vascular inflammation. In our regression model, smoking and alcohol did not reach statistical significance, although they are known to influence lipid levels. This is possibly because of the overwhelming inflammatory signal from active psoriatic disease in the treatment-naïve cohort, which may mask the subtler contributions of these lifestyle factors in the short term.

LIMITATIONS

The study’s limitations are hereby acknowledged. Being cross-sectional in design, it could not establish causality or assess long-term cardiovascular outcomes. Longitudinal studies, if conducted in the future, can help evaluate AIP as a predictive marker. Since this was a single-center study of a small population, the findings cannot be generalized to the broader population. Selection bias may have been introduced as numerous other dermatoses were excluded from the control group. Comprehensive cardiovascular imaging, such as carotid intima–media thickness, coronary calcium scoring, or echocardiography, was not performed. The aforementioned imaging, if concomitantly performed, might have provided objective evidence of subclinical atherosclerosis. Details regarding dietary habits and levels of physical activity, alongside other factors such as inflammatory markers, were not evaluated. Finally, although the AIP was found to have good discriminatory ability as per the ROC analysis, its prognostic significance remains exploratory and requires validation through future research incorporating objective cardiovascular endpoints.

CONCLUSION

Psoriasis is a chronic, systemic inflammatory condition associated with metabolic alterations that may increase the risk of atherosclerosis. This study found that the AIP was elevated in newly diagnosed, treatment-naïve psoriasis patients and showed positive associations with disease severity that persisted independently of traditional risk factors, thereby suggesting that this index may serve as a potentially useful indicator for early cardiovascular risk assessment in psoriasis, even at the time of initial diagnosis. However, its prognostic significance remains to be established. Larger, multicenter prospective studies involving diverse populations, supported by objective cardiovascular imaging and long-term follow-up, are needed to validate these findings and determine their clinical relevance.