Part1: Renal Function And Lipid Metabolism Are Major Predictors Of Circumpapillary Retinal Nerve Fiber Layer Thickness—the LIFE-Adult Study

For more information: tina.xiang@wecistanche.com

Abstract

Background: circumpapillary retinal nerve fiber layer thickness (cpRNFLT) as assessed by spectral-domain optical coherence tomography (SD-OCT) is a new technique used for the detection and evaluation of glaucoma and other optic neuropathies. Before translating cpRNFLT into clinics, it is crucially important to investigate anthropometric, biochemical, and clinical parameters potentially affecting cpRNFLT in a large population-based dataset.

Methods: The population-based LIFE-Adult Study randomly selected 10,000 participants from the population registry of Leipzig, Germany. All participants underwent standardized systemic assessment of various cardiometabolic risk markers and ocular imaging, including cpRNFLT measurement using SD-OCT(Spectralis, Heidelberg Engineering). After employing strict SD-OCT quality criteria, 8952 individuals were analyzed. Multivariable linear regression analyses were used to evaluate the independent associations of various cardiometabolic risk markers with sector-specific cpRNFLT For significant markers, the relative strength of the observed associations as compared to each other to identify the most relevant factors influencing cpRNFLT. In all analyses, the false discovery rate method for multiple comparisons Was applied.

Results: In the entire cohort, female subjects had significantly thicker global and also sectoral cpRNFLT compared to male subjects (p < 0.05). Multivariable linear regression analyses revealed a significant and independent association between global and sectoral cpRNFLT with biomarkers of renal function and lipid profile. Thus, thinner cpRNFLT was associated with worse renal function as assessed by cystatin C and estimated glomerular filtration rate. Furthermore, an adverse lipid profile (i.e., low high-density lipoprotein (HDL) cholesterol, as well as high total, high non-HDL, high low-density lipoprotein cholesterol, and high apolipoprotein B) was independently and statistically significantly related to thicker cpRNFLT. In contrast, we do not observe a significant association between cpRNFLT and markers of inflammation, glucose homeostasis, liver function, blood pressure, or obesity in our sector-specific analysis and globally. Conclusions: Markers of renal function and lipid metabolism are predictors of sectoral cpRNFLT in a large and deeply phenotyped population-based study independently of previously established covariates. Future studies on cpRNFLT should include these biomarkers and need to investigate whether incorporation will improve the diagnosis of early eye diseases based on cpRNFLT. Keywords: Retinal nerve fiber layer, Glaucoma, Biomarkers, Renal function, Optical coherence tomography, Lipid profile, LDL cholesterol, HDL cholesterol, eGFR, Cystatin C, Apolipoprotein B, Apolipoprotein A1

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Background

Retinal nerve fiber layer (RNFL)defects are early signs of glaucoma and optic disc deformation [1]. RNFL thickness is, therefore, a major tool in the evaluation of glaucoma and other optic neuropathies [2]. Spectral-domain optical coherence tomography (SD-OCT) is an appropriate [3], non-invasive, in vivo technique for the analysis of the optic nerve, and recent advances have allowed im-proved image quality for circumpapillary RNFL thickness(cpRNFLT)[4]. Very recently, cpRNFLT has been associated with distinct, basic anthropometric, and biochemical measures in different studies. For instance, Ho et al. [5] demonstrate a positive association of global cpRNFLT with low-density lipoprotein (LDL)cholesterol and a negative correlation with diabetes prevalence in three different Asian ethnic cohorts. Furthermore, age and a history of stroke or hypertension were negative, whereas smoking status was positive, related to global cpRNFLT in a cross-sectional meta-analysis of eight European, population-based studies [6]. In contrast, Lamparter et al.[7] did not find an independent association between global cpRNFLT and cardiovascular disease in multivariable analyses in the Gutenberg Health Study. Taken together, the association of global cpRNFLT shows conflicting results with the presence of cardiometabolic disease states. Before translating the cpRNFLT method into clinics, it is important to investigate anthropometric, biochemical, and clinical parameters potentially affecting cpRNFLT independent of other well-established predictors, i.e., age, sex, and scan radius. Furthermore, other factors influencing cpRNFLT need to be carefully defined to aid the early diagnosis of eye diseases and to prevent misclassification of impaired cpRNFLT due to other clinical and biochemical biomarkers. However, previous studies on cpRNFLT show the following limitations: they (a) included cohorts of smaller sample size;(b)have analyzed global cpRNFLT but not sector-specific data;(c)excluded subjects with different cardiometabolic disease states, e.g, type 2 diabetes or hypertension;(d)did not include a wide range of anthropometric, biochemical, and cardiometabolic markers and other patient-level data; and (e) did not use thoroughly adjusted multivariable models to investigate the independent predictors of cpRNFLT.

We, therefore, investigated a large panel of different anthropometric and cardiometabolic biomarkers and a wide range of clinical phenotypes and their associations with the sector-specific cpRNFLT profile measured by SD-OCT in a large(N=8952 subjects), unselected, and deeply phenotyped population-based study in Germany. We have applied a highly standardized ophthalmologic and non-ophthalmologic investigation procedure and statistical adjustment with correction for multiple testing.

Methods Participants

This analysis is part of the population-based LIFE-Adult Study conducted by the Leipzig Research Centre for Civilization Diseases at Leipzig University between August 2011 and November 2014 [8]. The LIFE-Adult Study includes 10,000 randomly selected participants from the population registry of just over half a million inhabitants of Leipzig, a city located in the east of Germany.

The LIFE-Adult Study recruitment was performed in an age-and sex-stratified manner mainly focusing on subjects with an age between 40 and 79 years [8]. For this purpose, the overall population consisted of 9600 subjects between 40 and 79 years of age, as well as 400 subjects between 19 and 39 years of age. Each age interval (by decade) was balanced with respect to the number of subjects and sex. The study was approved by the Ethical Committee at the Medical Faculty of Leipzig University (approval number: 263-2009-14122009) and adheres to the Declaration of Helsinki and all federal and state laws. Prior to inclusion, informed written consent was obtained from all participants.

Data collection/inclusion and exclusion criteria

During the baseline examination, study participants were deeply phenotyped, including ophthalmological image data, structured interviews, questionnaires, physical examinations, and blood and urine tests [8]. As part of the ophthalmic assessment, SD-OCT imaging (Spectralis, Heidelberg Engineering, Heidelberg, Germany) was performed, yielding cpRNFLT scans around the optic nerve head. The location of the cpRNFLT circle and the co-ordinate system have been described previously [4]. We excluded subjects with missing SD-OCT scans (excluded N=931)or SD-OCT scans using the following quality criteria:(1)B-scan number per location<50,(2)signal to noise ratio<20 dB, and(3) missing or unreliable RNFLT A-scans >5%(excluded N = 117). For the remaining 8952 subjects, one eye was randomly selected if both eyes of an included subject were reliable [4]. For validation analyses, we classified optic nerve head (ONH) abnormalities if any of the following were present: excavation (suspected glaucoma [i.e., violation of the inferior-superior-nasal-temporal rule, vertically oval with cup-to-disc ratio>0.7], optic disc pit, or coloboma of the optic disc), optic disc hemorrhage, neovascularization, optic atrophy, sectoral paleness, ONH swelling, papilledema, or optic disc drusen [4]. Furthermore, patient information on a previous diagnosis of glaucoma, as well as glaucoma medication, was collected.

Anthropometric and biochemical markers

Classical anthropometric(e.g, body mass index [BM], waist and hip circumferences, blood pressure) measurements were assessed according to standardized procedures by trained study nurses. In all subjects, fasting blood samples were drawn routinely and a panel of laboratory tests was performed on the day of sample collection [8]. The biomarkers of the panel have been described previously [8] and included measurements of total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein(LDL)cholesterol, triglycerides (TG), apolipoprotein (apo)B, apoAl, lipoprotein (Lp)(a), glucose, insulin, glycated hemoglobin (HbAlc), liver enzymes, interleukin-6, high-sensitivity C-reactive protein (hsCRP), cystatin C, and urinary albumin and creatinine, all being quantified in a central lab by standard methods [8]. In all subjects, the estimated glomerular filtration rate (eGFR)was calculated using the cystatin C-based chronic kidney disease(CKD) epidemiology collaboration equation [9]. As the aim of the current study was to investigate the associations between several cardiometabolic risk markers and cpRNFLT, we have used only the cystatin C-based equation which has been recently reported as the best equation for the assessment of cardiovascular risk [10]. CKD status was defined as a urinary albumin/creatinine ratio ≥ 30 mg/g and/or a de-creased eGFR<60 ml/min/1.73 m²， and the cohort was divided into five eGFR categories(i.e., G1-G5 combining G3a and G3b into one G3 category), as well as four CKD risk categories (i.e., low, moderately increased, high, and very high risk), according to KDIGO[11].

Statistical analysis

All statistical analyses were performed in an R environment using version 3.5(R Foundation for Statistical Computing, Vienna, Austria). For comparisons between female and male subjects, the unpaired Student t-test (for continuous variables) or chi-squared test (for categorical variables)were used, respectively.

As a next step, we examined the associations of various anthropometric, as well as cardiometabolic, bio-markers on the sectoral RNFLT. For this purpose, multivariable linear regression analyses were carried out for individual markers adjusted for age, sex, and scanning circle radius in all models. Using these covariates as independent variables in the respective models, the association of each marker with sectoral cpRNFLT (dependent variable) were separately calculated for the temporal (T),superior-temporal (TS), superonasal(NS), nasal (N), inferonasal (NI), and inferotemporal (TI)sectors, and globally (G).

Scanning circle radius was included as an independent variable in all models since eye size and optical characteristics of the human lens confound the cpRNFLT measurement [12-14]. The true scanning circle radius (mm)is estimated from the focus settings used by the Spectralis machine, according to a widely used model [15]. As sex[4] and age [14] were shown to affect cpRNFLT, these markers were also included as independent covariates in each model.

We next sought to compare the relative strength of the associations of all biomarkers with sectoral cpRNFLT. Therefore, a sectoral and global heatmap of standardized β values from multivariable analyses for each sectoral cpRNFLT was produced for all biomarkers, and standardized β values were employed in the figure's color code representing the strength of each association.

As a sensitivity analysis, we further validated the results of the linear regression analyses for lipid markers with sectoral cpRNFLT by stratifying the cohort into subjects on statin treatment compared to non-statin users. For this purpose, we used the Anatomical Therapeutic Chemical (ATC) classification codes to extract participants treated with 3-hydroxy-3-methylglutaryl co-enzyme A reductase inhibitors(i.e., statins), thereby reducing cholesterol synthesis. To investigate the potential mediating effects of smoking status on the association between cpRNFLT and the lipid profile, the Bayesian information criterion difference(ABIC)was computed for model comparisons. For this purpose, two different linear regression models were calculated with age, sex, measurement radius, and the respective lipid marker, as regressors (model A), as well as an additional model comprising of model A+smoking status(model B). The BIC difference(ABIC) was calculated by △BIC= BICmo-del A-BICmodel B. An ABIC>2 was regarded as statistically relevant according to Madrigal-Gonzalez et al. [16], as well as Kass and Raftery [17].

In all other analyses, a p value<0.05 was considered statistically significant. The false discovery rate(FDR)method was applied to correct all p values for multiple Comparisons.

Fig.1 Heatmap of standardized β coefficients for all investigated biomarkers and global, as well as sectoral, circumpapillary retinal nerve fiber layer thickness (qpRNFLT. Separate multivariable linear regression analyses were aired out for each of the biomarkers (independent variable) and the respective sectoral or global cpRNFLT (dependent variable). All multivariable models were adjusted for age, sex, and scanning circle radius The false-positive discovery rate method was applied to correct p values for multiple comparisons. If the linear regression models did not show an overall significance (indicating that the standardized is not valid in this sector), a white (empty) square is depicted. For all significant sectors, strength, as assessed by standardized B, as well as the direction, of the associations, are color-coded. Thus, positive (in red/warmer colorS), and negative (in blue/cooler colors) associations are shaded based on the respective standardized β coefficients Abbreviations ALAT, alanine aminotransferase, alkaline phosphatase ApoA1. apolipoprotein A1; ApoB, apolipoprotein B;ASAT aspartate aminotransferase; BMI, body mass index; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, cystatin C-based estimated glomerular filtration rate; GGT, gamma-glutamyl transferase; HbAIc, glycated hemoglobin AIc; HDL high-density lipoprotein; hsCRP, high-sensitivity C-reactive protein; IL interleukin;LDL, low-density lipoprotein; Lp(a), lipoprotein(a); SBP, systolic blood pressure; TG, triglycerides; WHR waist-to-hip ratio. Optic nerve head sectors; N, nasal sector NL inferonasal sector: NS. superonasal sector. , temporal sector: Jl, infero-temporal sector TS, supero-temporal sector; G, alobal (mean overall)

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