The relationship between inflammatory biomarkers and macular pigment optical density in hypertensive patients: a cross-sectional study

The cross-sectional study was conducted in a Ban Kongdara Primary Care Unit in Sriracha District, Chonburi Province, Thailand, from January 6 to 8, 2022. The study was approved by Mae Fah Luang University Ethics Committee on Human Research (Certificate of approval No. EC 21177-20). Total correlation sample size was calculated a priori as n = [(Z_α + Z_β)/C]² + 3 = 62 [14] in order to achieve α = 0.025 (2-tailed) (Error rate of type I), b = 0.20 (Rate of Type II errors). The predicted correlation coefficient is r = 0.38 from our pilot study, which is the same as this study at Ban Kongdara Primary Care Unit because there has never had research on this before. Z_α = 2.2414, Z_β = 0.8416, C = 0.5 * ln[(1+r)/(1−r)] = 0.4001.

The participants were recruited by inclusion criterion (1) hypertension alone or in conjunction with other diseases; (2) aged 35–59 years. Because of AMD and the reduction of MPOD, it is more common in people over 60 years of age. Therefore, the research was conducted in the age group younger than 60 years to find factors to prevent or slow the disease. In addition to this primary care unit, the hypertensive clinic had the youngest patients at age 35 years. Exclusion criterion were (1) participants who consume lutein and zeaxanthin, (2) individuals with mild to severe medial opacity (corneal scar, cataract), and who have infection or inflammation, and (3) individuals who have AMD or other ocular illnesses such as glaucoma, optic atrophy, cataract, previous retinal laser treatment or surgery, or previous eye damage. Withdrawal criterion were (1) participants with problems in using the MPS II device, (2) 3 consecutive failures of the MPS test, and (3) participants who had not completed both the blood test and the MPOD measurement. Physical examinations are used as exclusion criteria. Prior to the start of the study, all participants received a research consent form and an information leaflet to sign in, and patients were requested to complete questionnaires on MPOD. All participants received an explanation of how to conduct the MPS test before starting the test. Then, participants had blood checked for hsCRP, had a lipid profile after fasting for 9–12 h, and were measured for blood pressure, WCs, and calculated BMI. For each person, it takes about 15 min to test both eyes and explain the results of the MPS test. In the case of infection with COVID-19, participants were divided into 3 groups to measure MPOD, and it took 3 days. The 69 OPD cardholders met the inclusion criteria and were invited to participate in the study. But 3 patients missed their appointments due to illness, 2 had corneal scarring revealed during physical exams, and 2 failed the MPS II test 3 times. Finally, there remained 62 participants in the study. Computerized statistical analysis was done using Microsoft Excel 2019 and Stata version 16.1(license serial number:401606287634). Descriptive data analysis of the basic parameters were reported in mean ± SD., Min. and Max. Spearman's rank correlation (rs) was used to examine the relationships between biomarkers and MPOD. Multivariate analysis for adjusting confounders was done by logistic regression.

Of 62 participants, 27 (43.55%) were male and 35 (56.45%) female. The sex ratio was 1: 1.30 There were obese people with a BMI over 25 (70.96 %) and smoking 17 (27.42%). The basic characteristics of participants by mean ± SD, Min & Max. are shown in Table 1. In this study, the mean estimated MPOD of hypertension was 0.48 ± 0.14 in the mid-range. Table 2, there was a significant negative correlation (P < 0.05) between hsCRP > 3 and MPOD (r = −0.26, P = 0.04) by Spearman's rank correlation. Linear regression was performed, but there was no linear relationship. BMI, waist circumference, lipid profile, and ratio had no significant correlation. Multivariate analysis of the associated factors between MPOD by logistic regression in Table 3. There were no significant. The associations between sex, smoking, and MPOD were not significant when using MPOD < 0.5 [3] by chi-square analysis in Table 4.

Our estimated mean MPOD of hypertension was 0.48 ± 0.14, and it is below the mean MPOD of healthy people in Korea, which was mean ± SD = 0.59 ± 0.20 [15]. Previous studies reported that hypertension and inflammation are somehow connected; circulating inflammatory molecules are higher in hypertensive patients and their levels indicate the onset of hypertension. The research shows that chronic inflammation can be used as a predictor of hypertension incidence [5]. Therefore, inflammation may contribute to lower MPOD levels.

hsCRP is utilized as an inflammatory marker, and multiple prospective studies in healthy individuals have validated its ability to predict cardiovascular events. The American Heart Association and the Centers for Disease Control and Prevention defined risk groups based on hsCRP levels in 2015 as low risk (< 1.0 mg/L), average risk (1.0 to 3.0 mg/L), and high risk (> 3.0 mg/L) [16]. This study showed hsCRP > 3.0 mg/L(high risk) had a significant negative correlation between MPOD (r = −0.26, P = 0.04). hsCRP levels may be useful to screen or follow clinically to prevent MPOD degradation, which is a risk factor for AMD and should be studied in broader applications.

BMI and WC were used to classify obesity, which is related to persistent systemic inflammation. It is remarkable that body fat and serum inflammatory protein levels have a positive correlation. However, for the relationship of inflammation from obesity, both BMI and WC should be used together, particularly in abdominal obesity, because WC shows a greater correlation with central obesity inflammation [17]. The association between BMI and MPOD was found in the previous study [18].

Several studies have found an association between lipid level and ratio with inflammation in the body. The TC/HDL-C can be used as a clinical predictor in the early screening of AMI [12]. A study discovered a strong link between the high TG/HDL-C ratio and insulin resistance and suggested that it could be utilized as an indication of insulin resistance in the nonobese Chinese middle-aged and elderly population in clinical practice [19]. This study found that BMI, waist circumference, lipid profile and ratio had no significant correlation with MPOD.

This study did not have a control group because it was designed to only look at the association of inflammation with other variables without intervention. Futhermore, the initial design of this study looked at the relationship between inflammation and MPOD. Thus, it focuses on people who are more likely to get inflammation. Smokers (27.42%) were not left out of this, which is a limitation of this study that cannot separate the causes of inflammation in more detail. Although logistic regression was used to adjust the confounder, the result had no significance. Treatments such as anti-hypertensive drugs, lipid-lowering drugs, and the duration of treatment may have an effect on MPOD, which should be researched in detail and controlled for various variables in a case–control study in the future.

Inflammatory biomarkers (hsCRP > 3.0 mg/L) in this study showed significant relationships with MPOD. This indicates that inflammation is linked to MPOD. hsCRP may be helpful to screen or follow clinically to prevent MPOD degradation, which is a risk factor for AMD, especially in patients with chronic inflammatory disease and should be studied in broader applications. Anti-inflammatory supplements such as omega3, an anti-inflammatory diet may play a role in preventing the reduction of MPOD in addition to the use of lutein and zeaxanthin to prevent the reduction of MPOD.