Non-HDL cholesterol is better than LDL-c at predicting atherosclerotic cardiovascular disease risk factors clustering , even in subjects with near-to-normal triglycerides : A report from a Venezuelan population

Non-high density lipoprotein cholesterol (non-HDL-c) has Background: emerged as an important tool in primary prevention of atherosclerotic cardiovascular disease (ASCVD), especially among those at high risk. The main objective of this study was to evaluate the predictive value of non-HDL-c for the coexistence aggregation of multiple ASCVD risk factors and compare this with LDL-c in general subjects with normal or near normal triglycerides from Maracaibo city in Venezuela. This is a descriptive, cross-sectional study with a randomized Methods: multistage sampling. 2026 subjects were selected for this study, all were adults ≥18 years old of both genders and inhabitants of Maracaibo city, Venezuela. A complete history and physical medical assessment was performed. A multivariate logistic regression model was used to determine the odds ratio (CI95%) for the coexistence of multiple risk factors for ASCVD. The median (p25-p75) of non-HDL-c was 143 mg/dL (114-174 Results: mg/dL). 52.1% (n=1056) of the sample were women, with a median of 144 mg/dL (115-174 mg/dL) among women and 143 mg/dL (114-17 4mg/dL) among men; p=0.740. Individuals ≥50 years old, smokers, those with hypertension, obesity, diabetes, high waist circumference and elevated hs-C Reactive Protein, all had higher levels of non-HDL-c. A lower median was observed among those <30 years of age with elevated physical activity levels in 1,2 2 2 2 3 2 2 2 2 2 3 4 5,6 1 1 1 1

among men; p=0.740. Individuals ≥50 years old, smokers, those with hypertension, obesity, diabetes, high waist circumference and elevated hs-C Reactive Protein, all had higher levels of non-HDL-c. A lower median was observed among those <30 years of age with elevated physical activity levels in their leisure time. Non-HDL-c between 130-159 mg/dL (OR=2.44; CI 95%=1.48-4.02; p<0.001) and ≥160 mg/dL (OR=3.28; CI 95%=1.72-6.23; p<0.001) was associated with greater risk of coexistent multiple risk factors for ASCVD, albeit LDL-c was not significant in the multivariate model.
Elevated non-HDL-c was associated with conglomeration of Conclusions: multiple risk factors for ASCVD. This suggests evaluation of non-HDL-c may be of better utility in primary care for early identification of subjects for high risk of ASCVD. Future research might focus on the influence of non-HDL-c in cardiovascular mortality.

Introduction
Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of morbidity and mortality in the world, representing 31.5% of deaths, with approximately 17.3 million deaths globally 1 . Hyperlipidemia plays an important role in the pathogenesis of atherosclerosis by inducing chronic inflammation, arterial plaque formation and remodeling, leading to compromised perfusion. Thankfully, hyperlipidemia remains a modifiable risk factor for ASCVD 2,3 .
Historically, the therapeutic goal for ASCVD risk reduction was to reduce cholesterol levels associated with low density lipoproteins (LDL-c), as elevated quantities have been associated with a higher incidence of ASCVD 4 . An important body of evidence, including randomized controlled trials, have demonstrated that statins reduce mortality from ASCVD when used as primary or secondary prevention 5-8 . Nonetheless, other studies have shown that the risk for future cardiac events remain elevated despite achieving LDL-c goals, suggesting that LDL-c might not be the best estimator of ASCVD in some populations 9,10 .
LDL-c levels only reflect the amount of cholesterol contained within the low density lipoproteins, but does not quantify its quantity, size or structure. Additionally, there are other lipoproteins that possess atherogenic properties, such as very low density lipoproteins (VLDL-c), chylomicrons, and lipoprotein remnants. All these have Apo-B, and can participate in atherogenesis by accumulation in the intima and eliciting pro-inflammatory responses 11 . Other disadvantage of using LDL-c is the methodologic limitation of its calculations using Friedewald´s equation, which cannot be used in the setting of hypertriglyceridemia 12 . Recall that elevated triglycerides (TGs) can independently increase the risk for ASCVD 13 . Therefore, non-high density lipoprotein (HDL) cholesterol has emerged as an alternative predictor of ASCVD.
Non-HDL cholesterol essentially represents the sum of all lipoproteins that have atherogenic properties (LDL, VLDL, IDL, lipoprotein remnant) 11 . Studies such as the Emerging Risk Factors Collaboration 14 (N=302,430) suggest that aiming to reduce non-HDL disregarding other lipid parameters might be a new and better approach. This is supported by the fact that patients in this study with elevated non-HDL-c had higher risk of cardiac events (HR=1.50; CI 95%=1.39-1.61) than those with elevated TGs (HR=0.99; CI 95%=0.94-1.05) or with elevated LDL-c (HR=1.38; CI 95%=1.09-1.73). Moreover, non-HDL-c has demonstrated to be a useful predictor for the appearance of metabolic syndrome, which can be of great utility in primary care settings 15 . Lastly, non-HDL-c seems to be a better predictor of metabolic syndrome compared with LDL-c, even in patients with TG <400 mg/dL, and the predictive value was independent from central obesity and insulin resistant states 16 .
Despite all the advantages of non-HDL-c in order to estimate ASCVD risk, the current practice measurement of non-HDL-c is underused. The objective of this study was to evaluate the predictive value of non-HDL-c for the aggregation of multiple ASCVD risk factors and compare it with LDL-c in general subjects with normal or near normal TGs from Maracaibo municipality in Venezuela.

Study design and selection of participants
The Maracaibo City Metabolic Syndrome Prevalence Study (MMSPS) is a descriptive and cross-sectional study carried out by our research group in Maracaibo, Venezuela, with the main goal to determine the prevalence of metabolic syndrome in this population and it´s methodology was described previously 17 . For the purpose of the present sub-study, individuals with no determination of fasting insulin level were excluded; thus, a total of 2026 individuals older than 18 years old were included for this investigation. The study was approved by the Bioethics Committee of the Endocrine and Metabolic Diseases Research Center -University of Zulia (approval number: BEC-006-0305). This ethical approval included all future studies that used the data from the MMSPS. All participants signed written consent before being questioned and physically examined by a trained team.
Clinical evaluation of the participants All individuals underwent a full history and physical exam by trained personnel. During the initial interview, personal and family history of premature ASCVD, endocrine and metabolic diseases were explored. Age, gender, as well as social and economic stratus using Graffar's scale modified by Mendez-Castellano 18 , were recorded. Smoking history was categorized in three different classes: a) current smoker (smoked >100 cigarettes in a lifetime, current smoking, and chronic smoker who stopped for <1 year; b) ex-smoker (smoker who stopped smoking for >1 year); c) non-smoker (never smoked or who smoked <100 cigarettes in a lifetime). Current drinkers were considered to be those having drunk >1 gram a day 20 .
Physical activity was assessed by the Long Form of the International Physical Activity Questionnaire (IPAQ) 21 . This instrument quantifies the amount of minutes invested in transportation, work, homework (gardening, cleaning), and leisure time. The participants were divided into quintiles based on total Metabolic Equivalents (METs)/min/week scores considering a sedentary person those with a MET score of 0 and those individuals with some degree of physical activity (≥1 MET) were stratified into five groups: very low (Q1), low (Q2), moderate (Q3), high (Q4) and very high (Q5) for a total of six categories. Leisure time was classified as follows: Q1 or very low PA in men<296. Blood pressure evaluation Blood pressure was measured by manual methods using a sphygmomanometer and stethoscope to detect 1 st and 5 th Korotkoff's sounds for systolic and diastolic blood pressure, respectively. Participants had a 15 minute resting period before BP determination, they were seating with both feet on the ground. Measurements were repeated three times in 15 minute intervals. Joint National Committee 7 (JNC7) was used to classify BP as normal BP <120/80 mmHg, prehypertension in those with systolic blood pressure (SBP) 120-139 mmHg and/or diastolic blood pressure (PAD) between 80-89 mmHg, and hypertension when BP is ≥140/90 mmHg 22 .

Anthropometric evaluation
Height was determined using a calibrated stadiometer placed on a flat surface. Weight was determined using a digital scale (Tanita, TBF-310 GS Body Composition Analyzer, Tokyo -Japan), with the patient wearing light clothing and barefoot. Body mass index (BMI) was determined using Quetelec´s equation [weight/height 2 ], and using World Health Organization criteria participants were deemed normal weight (BMI <25 kg/m 2 ), overweight (25.0 -29.9 kg/m 2 ), and obese (≥30.0 kg/m 2 ) 23 .
Waist circumference was measured using a standardized metric belt using the metric system in centimeters and millimeters. An anatomic reference was used to measure waist circumference an equidistant point between the lower border of the ribs and the antero-superior iliac spine, according to the National Institutes of Health of the United States 24 . Central obesity was considered if waist circumference was ≥91 cm in women and ≥98 cm in men, according to the specific cut off values proposed for the population of Maracaibo, Venezuela 25 .

Laboratory analyses
Antecubital venous sampling was performed after an eight hour period of fasting. Samples were centrifuged and serum was obtained. Levels of glucose, total cholesterol, and TGs were determined using commercial enzymatic and colorimetric ELISA kits (Human Gesellshoft Biochemica and Diagnostica MBH). Glucose levels were interpreted according to the American Diabetes Association 2017 diagnostic criteria as follows: normal glucose <100 md/dL, impaired fasting glucose when fasting glucose is 100-125 mg/dL, and diabetes mellitus when glucose was ≥126 mg/dL 26 . Before diagnosing diabetes, a confirmatory test was repeated on a different day. Levels of high sensitive C reactive protein (hs-CRP) were determined using immunoturbidimetric analyses (Human Gesellshoft Biochemica and Diagnostica MBH), and the cut off point for an elevated hs-CRP was ≥0.765 mg/L 27 .
Fasting insulin concentration was determined using a commercial kit based on ELISA (DRG International. Inc. USA. New Jersey), with a detection limit of <1 mU/L. Resistance to insulin was calculate by the software HOMA-Calculator v2.2.2 provided by the Oxford Centre for Diabetes Endocrinology and Metabolism. Cutoff value for HOMA2-IR was 2.00 28 .

Definition of composite of multiple risk factors
The aggregation of multiple risk factors was considered when one individual presented with two or more of the following: • Fasting glucose ≥100 mg/dL; • Blood pressure ≥130/85 mmHg; • Waist circumference ≥91 cm in females and ≥98 cm in males • HOMA2IR≥2.

Statistical analysis
Qualitative variables were shown as absolute and relative frequencies. Associations between these variables were explored using χ 2 (Chi square) testing and differences with Z test. Quantitative variables were shown as arithmetic mean ± standard deviation after normality testing was performed using the Geary test. Non-normal distribution variables were logarithmically transformed and analyzed as with parametric testing when normality was achieved. When these variables remained non-normal they were shown as median with interquartile ranges (p25-p75 th ). U Mann Whitney test and Kruskal-Wallis test were used for comparisons between two groups and three or more groups, respectively. A multivariate regression model was created to estimate odds ratio and confidence intervals for prediction of composite of multiple risk factors. The first model was adjusted for age, sex, age group, ethnic group, socio-economic status, literacy, employment status, smoking, alcohol consumption, physical activity during leisure time, hypertension, hs-CRP, LDL-c and non-HDL cholesterol.
SPSS v.21 for Windows (IBM Chicago, IL) was used for statistical analyses and data gathering. We considered results statistically significant at p<0.05.

Results
General Non-HDL-c, chronic diseases and low-grade inflammation: Hypertension, obesity, diabetes and us-CRP Table 3 shows non-HDL-c levels according to clinical, metabolic, and anthropometric variables. Non-HDL-c were significantly higher among those with hypertension compared to those with normal blood pressure (159 vs. 132 mg/dL, respectively; p<0.001). This behavior was also observed when comparing  Table 4.
Non-HDL-c and composite of multiple risk factors for ASCVD Figure 1 shows

Discussion
For nearly 50 years, incredible efforts have been made to identify specific and prevalent ASCVD risk factors, planning and application of primary and secondary prevention strategies, evaluation of population genetics and overall ethnicity genetic risks, and modification due to epigenetics. These risk factors have been of various natures, from anthropometric measurements, such as BMI and waist circumference, lifestyle patterns, to blood lipids sub-fractions, such as LDL-c and HDL-c. In regards to the focus of the present study, lipid profiles and novel lipid fractions and their association with ASCVD have been the main focus of grand scale epidemiological, clinical, and pharmacological investigation 31,32 .
In spite of all the efforts, data has been accumulating that suggests that focusing on one lipid fraction, namely LDL-c, may not be the appropriate approach 33 , due to recently described atherogenic particles, like IDL, Apo B, and non-HDL 33 . The concept of cardiovascular residual risk factor has been intimately associated with cardiovascular disease reduction, being twice as effective as LDL-c 34 . In fact, Helgadottir et al. 35 reported that genetic risk scores using non-HDL-c strongly associates with coronary artery disease, and this genetic risk was considerably lower than that offered by LDL-c. It is no coincidence that non-HDL-c has been shown to correlate with coronary artery disease progression, cardiovascular morbidity, and mortality 34,36 .
The present results show that higher non-HDL-c levels were associated with higher risk of multiple risk factors for ASCVD.
These results are similar to those reported by Kumar et al.
where non-HDL-c had a better predictive value than LDL-c for atherosclerosis among those with TGs >150 mg/dl 37 . This study excluded patients with increased TGs >400 mg/dl; therefore, one cannot assume this association is also seen in this group. Moreover, Arsenault et al. 38 followed over 21 thousand subjects without diabetes or previous coronary heart disease (CHD), demonstrating that high non-HDL-c is associated with increased CHD.
Following the recommendation of the Strong Heart Study 39 , the recent 2016 ACC Expert Consensus Decision Pathway on the Role of Non-Statin Therapies for LDL-Cholesterol Lowering in the Management of Atherosclerotic Cardiovascular Disease Risk proposed a goal of <100 mg/dl for non-HDL-c in diabetic patients 40 . As expected, subjects with diabetes in our population have higher non-HDL-c, which is a recognized risk factor in diabetic subjects at risk for ASCVD 41 . Interestingly, Apo B and non-HDL-c are better predictors of diabetes development than glycated hemoglobin 42 . In line with this notion, the present results also show that non-HDL-c is associated with higher levels of hs-CRP (systemic inflammation), hypertension, and central obesity. We previously described our population as having a high prevalence of obesity and overweight, managing a staggering 65.7% 43 . Thus, the overlapping of risk factors and metabolic syndrome/type 2 diabetes development is imminent and borderline epidemic.
Lastly, Hispanic population seems to be at higher risk for LDL-particle numbers and non-HDL-c discordance 44 .
Kilgore et al. 45 reported that subjects with high non-HDL-c and normal LDL-c were likely to be Hispanic males with metabolic syndrome and other cardiovascular risks. Likewise, using the database from The Hispanic Community Health Study/Study of Latinos, Rodriguez et al. 46 reported that almost two thirds of Latinos have a form of dyslipidemia, with South Americans having high non-HDL-c and high LDL-c. Therefore, ethnicity is of high importance when evaluating clinical risk for ASCVD, including blood lipid profiles and sedentary lifestyles in these groups 47 .
To summarize, this investigation in Hispanic population shows that non-HDL-c is associated with multiple risk aggregation for ASCVD, being associated with hypertension, central obesity and low grade inflammation. The question that arises is: Should non-HDL-c replace LDL-C as the main target of therapy? 33 . The fact that non-HDL-c is a better risk predictor, can be performed in a non-fasting state, and can be easily calculated by extracting HDL-c from total cholesterol without using any other laboratory assay makes it the most advantageous parameter for prediction of ASCVD even in subjects with TAG <200 mg/dl.

Chandrakala Aluganti Narasimhulu
Univeristy of Central Florida , Orlando, Florida, USA The present study "Non-HDL cholesterol is better than LDL-c at predicting atherosclerotic cardiovascular disease risk factors clustering, even in subjects with near-to-normal triglycerides: A report from a Venezuelan population" by Bermúdez et al., is interesting however, the authors must address the following key points: In results, (Table 2) the non-HDL cholesterol levels were represented based on age groups. In addition to this if the authors provide the analysis for men and women separately which will be more accurate. Table 3 to 5: if the analysis done for both men and women separately that provides more insights for both clinicians and researchers.
Authors are suggested to comment/discuss briefly the hormonal influence in disease progression in both genders.

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility? Yes

Are the conclusions drawn adequately supported by the results?
Yes I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
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