Association of Metabolic Syndrome with Macular Thickness and Volume in Older Adults: A Population-Based Optical Coherence Tomography Study

Abstract

Background:

To explore the associations of the metabolic syndrome (MetS) and individual components with macular thickness and volume among rural-dwelling Chinese older adults.

Methods:

This population-based cross-sectional study included 705 participants (age ≥60 years) derived from the MIND-China study. In 2018–2019, we collected data through face-to-face interview, clinical examination, optical coherence tomography (OCT) examination, and blood test. We measured macular thickness and volume using spectral-domain OCT. MetS was defined following the International Diabetes Federation (IDF) criteria, the IDF/American Heart Association (AHA) criteria, the National Cholesterol Education Program-Adult Treatment Panel III criteria, and the Chinese Diabetes Society (CDS) criteria. Data were analyzed with multivariable general linear models.

Results:

MetS was significantly associated with thinner macula in central (multivariable-adjusted β = −5.29; 95% confidence interval: −9.31 to −1.26), parafoveal (−2.85; −5.73 to 0.04) and perifoveal regions (−4.37; −6.79 to −1.95) when using the IDF criteria, in the perifoveal regions (−3.82; −6.18 to −1.47) when using the IDF/AHA criteria, and in the central region (−5.63; −10.25 to −1.02) when using the CDS criteria, and with reduced macular volume when using the IDF (−0.16; −0.26 to −0.07) and IDF/AHA (−0.13; −0.22 to −0.04) criteria. In the parafoveal region, the IDF-defined MetS was significantly associated with thinner retina in men (β = −6.25; −10.94 to −1.56) but not in women. Abdominal obesity (−2.83; −5.41 to −0.25) and elevated fasting blood glucose (−2.65; −5.08 to −0.21) were associated with thinner macular thickness in the perifoveal region.

Conclusion:

MetS is associated with macular thinning and reduced macular volume among rural-dwelling older adults, and the associations vary by the defining criteria of MetS.

Introduction

The metabolic syndrome (MetS) is a constellation of multiple interrelated cardiometabolic risk factors, such as abdominal obesity, dyslipidemia, elevated blood pressure, and impaired fasting blood glucose (FBG). MetS represents one of the major public health challenges owing to its close association with cardiovascular disease and type 2 diabetes.¹ At present, MetS affects ∼25% of world adult population² and global prevalence of MetS has steadily increased in the past decades.³

The retina is a part of central nervous system, with high metabolic activity and oxygen consumption.⁴ The spectral-domain optical coherence tomography (SD-OCT) provides a noninvasive way for cross-sectional images of retinal biological tissues and precise quantification of retinal morphology.⁵ For instance, the SD-OCT studies have shown that diabetes is associated with thinning of retinal nerve fiber layer (RNFL) or macular thickness, even before occurrence of diabetic retinopathy.^6
–8 MetS, characterized by increased oxidative stress, has been associated with retinopathy in large population-based studies.^9

–12 The clinical-based OCT studies have reported the association of MetS with peripapillary RNFL thinning in middle-aged adults.^13,14 In addition, the macula has a high density of neurons and is especially vulnerable to oxidative stress.^15,16

However, most of the previous studies have been conducted among middle-aged adults and in the clinical settings, very few population-based studies have investigated the relationship between MetS and macular parameters in elderly people, and relevant population-based studies from China are currently lacking. Furthermore, given that women have a higher prevalence of vision impairment than men, especially in low- and middle-income countries,¹⁷ potential sex difference in the association of MetS with retinal parameters has not yet been explored. Moreover, MetS defined by different criteria has differential associations with atherosclerotic disorders,¹⁸ and thus, the associations of MetS with retinal or macular parameters may differ by MetS definitions, which has yet to be explored.

Therefore, in this population-based cross-sectional study of rural-dwelling Chinese older adults, we sought to explore the associations of MetS and its individual components with macular thickness and macular volume by using different defining criteria for MetS, as well as the potential sex variations in these associations.

Materials and Methods

Study participants

This is a population-based cross-sectional study. The study participants were derived from the Multimodal Interventions to Delay Dementia and Disability in Rural China (MIND-China) study that targeted residents who were aged 60 years or older and living in the rural area of Yanlou Town (52 villages), Yanggu County, Shandong Province, China, as fully described elsewhere.¹⁹ In brief, MIND-China is a participating project of the World-Wide FINGERS Network, a global interdisciplinary network of clinical trials for prevention and risk reduction of cognitive impairment and dementia.²⁰ In March–September 2018, 5765 participants were examined for MIND-China, during which we collected demographic, epidemiological, clinical, and biochemical data. Of these participants, using cluster (village) sampling approach, 1083 people who were free of self-reported ocular disease (e.g., diabetic retinopathy and glaucoma), ocular surgery, and ocular trauma were invited to undertake SD-OCT scans from March 2018 to June 2018 in Yanlou Town Hospital.

Of these, 378 persons were excluded owing to suboptimal image quality (e.g., signal strength less than six, segmentation errors, movement artifacts or decentration of the scanning center over the fovea) (n = 252), retinal disorders (e.g., epiretinal membrane, macular edema, and macular hole) (n = 125), and missing data on blood pressure (n = 1), leaving 705 subjects for this analysis. Figure 1 provides flowchart of the study participants.

FIG. 1.

Flowchart of the study participants. MIND-China, Multimodal Interventions to Delay Dementia and Disability in Rural China; OCT, optical coherence tomography.

Data collection

Data on demographics (e.g., age, sex, and education), lifestyles (e.g., smoking and alcohol consumption), medical history (e.g., diabetes, hypertension, and hyperlipidemia), and use of medications (e.g., hypoglycemic, antihypertensive, and hypolipidemic agents) were collected by trained medical staff following a structured questionnaire through face-to-face interviews and clinical examinations. After an overnight fast, peripheral blood samples were taken for biochemical analysis at the laboratory of local town hospital (e.g., FBG, total serum cholesterol, high-density lipoprotein cholesterol [HDL-C], and triglycerides). Height and weight were measured with participants wearing light clothes and no shoes. Body mass index (BMI) was calculated as weight (in kilograms) divided by height (in meters) squared. Waist circumference was measured at the midpoint between the lower margin of the rib cage and the iliac crest.^21,22 Arterial blood pressure was measured on the right arm with participants in the sitting position after at least a 5-min rest.

Definition of MetS

MetS was defined according to four sets of criteria that are commonly used in the literature, that is, the National Cholesterol Education Program—Adult Treatment Panel III (NCEP ATP III) criteria,²³ the International Diabetes Federation (IDF) criteria,²⁴ the International Diabetes Federation/American Heart Association (IDF/AHA) criteria,²⁵ and the Chinese Diabetes Society (CDS) criteria.²⁶ The commonality among the four sets of criteria for defining MetS is requiring the presence of at least three of the following five metabolic risk factors: (1) high waist circumference or high BMI; (2) high blood pressure; (3) high FBG; (4) high triglycerides; and (5) low HDL-C. The main difference among the four sets of MetS criteria is based on specific definitions of each of the five component factors.

In addition, the IDF/AHA criteria consider abdominal obesity as a prerequisite, and the CDS criteria consider HDL-C and triglycerides as one risk factor. Details of the four sets of defining criteria for MetS are summarized in Supplementary Table S1.

Ocular examination and SD-OCT image processing

Following the routine ocular examination (e.g., visual acuity and slit-lamp examination), the SD-OCT imaging scan was performed using Primus 200 (Carl Zeiss Meditec, Germany) by an experienced ophthalmic technician in Yanlou Town Hospital. This device utilizes a super luminescence diode with an 870 nm bandwidth, an acquisition rate of 12,000 A-scans/s, a tissue axial resolution of 5 μm and a transversal resolution of <20 μm. Only one eye of each participant was imaged, and the right eye was the first choice. The macular scan was performed in a dark room without pupil dilation, following the macular cube 512 × 32 scan protocol, which covers an area of 6 × 6 mm centered on the fovea. The macular thickness was computed as the distance between the internal limiting membrane and retinal pigment epithelium (RPE).

The built-in OCT software provides the macular thickness readings for nine subfields (i.e., central, inner superior, inner temporal, inner inferior, inner nasal, outer superior, outer temporal, outer inferior, and outer nasal) defined by Early Treatment Diabetic Retinopathy Study (ETDRS),²⁷ as well as the average macular thickness and volume over the entire ETDRS 6-mm circle. The area within the entire 6-mm circle was defined as global region. The center circle, inner ring, and outer ring bound by 1, 3, and 6-mm diameter circle were defined as central, parafoveal, and perifoveal subfields, respectively (Fig. 2).

FIG. 2.

Illustration of the Early Treatment Diabetic Retinopathy Study (ETDRS) subfields. The central, parafoveal, and perifoveal subfields are shaded in blue, yellow, and green, separately. C, central; IS, inner superior; IT, inner temporal; II, inner inferior; IN, inner nasal; OS, outer superior; OT, outer temporal; OI, outer inferior; ON, outer nasal.

Statistical analysis

Characteristics of the study participants by sex were compared using Mann–Whitney U-test for continues variables with non-normal distribution, t-test for continues variables with normal distribution, and chi-square tests for categorical variables. Multivariable general linear regression analysis was performed to estimate β coefficients and 95% confidence interval of macular thickness in different regions and macular volume associated with the presence of MetS defined by the IDF, the IDF/AHA, the CDS, and the NCEP ATP III criteria and individual components (i.e., obesity, high blood pressure, high FBG, low HDL-C, and high triglycerides) defined using the IDF criteria. We performed stratified analysis by sex to examine the association of MetS with macular thickness and macular volume separately in women and men. The statistical interaction was tested by simultaneously including the independent variables and their cross-product term in the same model. We reported the main results from the models that were controlled for age, sex, education, current smoking, and current alcohol consumption.

R version 4.2.1 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria) was used for all statistical analyses. A two-tailed P < 0.05 was considered statistically significant.

Results

Characteristics of study participants

Among the initial 1083 persons who were invited to undertake the SD-OCT scans in Yanlou town hospital, 378 participants were excluded owing to suboptimal image quality, retinal disorders, and missing data. Compared with those excluded participants (n = 378), participants included in the analytical sample (n = 705) were younger (mean age, 70.4 vs. 73.1 years, P < 0.001), less likely to be women (54.2% vs. 61.9%, P < 0.05), received more years of education (mean years of school education, 3.2 vs. 2.7 years, P < 0.05), and had lower systolic blood pressure (mean systolic pressure, 143.3 vs. 147.8 mmHg, P < 0.01), but the two groups did not differ significantly in the distributions of current smoking, alcohol consumption, use of medication, MetS, and other clinical data (Supplementary Table S2).

The characteristics of participants in the analytical sample by sex are given in Table 1. Depending on the defining criteria, the prevalence of MetS ranged from 21.3% by the CDS criteria and 23.3% by the NCEP ATP III criteria to 35.6% by the IDF criteria, and 38.9% by the IDF/AHA criteria. Compared with men, women were less educated (P < 0.001), less likely to smoke or drink alcohol (P < 0.001), had lower diastolic blood pressure (P < 0.001), had higher BMI (P < 0.05), triglycerides (P < 0.001), and HDL-C (P < 0.01), had a higher prevalence of MetS defined by three sets of criteria (i.e., the NCEP ATP III criteria, IDF criteria, and IDF/AHA criteria) (P < 0.001), and had a higher prevalence of obesity, low HDL-C, and high triglycerides defined by the IDF criteria. In addition, women had lower macular volume and thinner retina in all four subfields of the macula (P < 0.001). There was no significant sex difference in mean age, waist circumference, systolic blood pressure, FBG, diabetes, hypertension, the NCEP ATP III-defined MetS, and use of antihypertensive agents, antidiabetic agents, and hypolipidemic agents.

Table 1.

Characteristics of Study Participants by Sex

Characteristics	Total sample (N = 705)	Men (n = 323)	Women (n = 382)	P value^a
Age (years)	70.4 (4.2)	70.1 (4)	70.7 (4.4)	0.11
Education (years)	3.2 (3.6)	5.4 (3.6)	1.4 (2.4)	<0.001
Current smoking, n (%)	149 (21.1)	148 (45.8)	1 (0.3)	<0.001
Current alcohol drinking, n (%)	211 (29.9)	189 (58.5)	22 (5.8)	<0.001
Waist circumference (cm)	88.1 (9.6)	88.7 (9.4)	87.6 (9.7)	0.054
Body mass index (kg/m²)	25 (3.7)	24.7 (3.6)	25.4 (3.8)	0.029
Systolic blood pressure (mmHg)	143.2 (21.5)	142.4 (21.4)	144 (21.5)	0.278
Diastolic blood pressure (mmHg)	85.7 (11.4)	87.3 (11.4)	84.3 (11.2)	<0.001
Fasting blood glucose (mM)	5.5 (1.1)	5.5 (0.9)	5.6 (1.3)	0.537
Triglycerides (mM), median (IQR)	1.2 (0.9–1.7)	1.1 (0.9–1.5)	1.3 (1.0–1.8)	<0.001
HDL-C (mM)	1.6 (0.3)	1.5 (0.3)	1.6 (0.3)	0.003
Diabetes, n (%)	95 (13.5)	37 (11.5)	58 (15.2)	0.149
Hypertension, n (%)	506 (71.8)	238 (73.7)	268 (70.2)	0.300
Use of antihypertensive drugs, n (%)	210 (29.8)	94 (29.1)	116 (30.4)	0.715
Use of antidiabetic drugs, n (%)	33 (4.7)	10 (3.1)	23 (6.0)	0.067
Use of hypolipidemic drugs, n (%)	63 (8.9)	33 (10.2)	30 (7.9)	0.273
Metabolic syndrome, n (%)
CDS criteria (2004)	150 (21.3)	61 (18.9)	89 (23.3)	0.154
NCEP ATP III (2004)	164 (23.3)	50 (15.5)	114 (29.8)	<0.001
IDF criteria (2005)	251 (35.6)	81 (25.1)	170 (44.5)	<0.001
IDF/AHA criteria (2009)	274 (38.9)	99 (30.7)	175 (45.8)	<0.001
Metabolic abnormalities defined by the IDF criteria, n (%)
Obesity	465 (66.0)	152 (47.1)	313 (81.9)	<0.001
High blood pressure	568 (80.6)	263 (81.4)	305 (79.8)	0.665
High fasting blood glucose	229 (32.5)	100 (31.0)	129 (33.8)	0.476
Low HDL-C	95 (13.5)	33 (10.2)	62 (16.2)	0.026
High triglycerides	212 (30.1)	81 (25.1)	131 (34.3)	0.010
Macular volume (mm³)	9.76 (0.59)	9.85 (0.61)	9.69 (0.56)	<0.001
Macular thickness by regions (μm)
Global	271.2 (16.3)	273.7 (16.8)	269.1 (15.6)	<0.001
Central	245.4 (26.3)	252.5 (26.3)	239.4 (24.8)	<0.001
Parafoveal	309.4 (18.8)	314.2 (18.7)	305.3 (17.9)	<0.001
Perifoveal	271.2 (15.6)	273.7 (16.0)	269.2 (15.0)	<0.001

Data are given as mean (SD), unless otherwise specified.

P value is for test of difference between men and women.

IQR, interquartile range; HDL-C, high-density lipoprotein cholesterol; IDF, International Diabetes Federation; AHA, American Heart Association; CDS, Chinese Diabetes Society, NCEP ATP III, National Cholesterol Education Program—Adult Treatment Panel III.

Associations of MetS with macular thickness and volume

After controlling for age, sex, education, current smoking, and alcohol consumption, the IDF-defined MetS was significantly associated with thinner macula in the global (P < 0.001), central (P = 0.010), and perifoveal subfields (P < 0.001), and the IDF/AHA-defined MetS was significantly associated with thinner macula in the global (P = 0.004) and perifoveal subfields (P = 0.001), whereas the CDS-defined MetS was only significantly associated with thinner macula in the central subfield (P = 0.015) (Table 2). There was no significant association of any macular parameters with the NCEP ATP III-defined MetS. Moreover, MetS defined by the IDF (P < 0.001) and IDF/AHA (P = 0.004) criteria was significantly associated with reduced macular volume. In addition, there was no significant association between MetS and visual acuity (P > 0.05).

Table 2.

β Coefficient (95% Confidence Interval) of Macular Thickness and Macular Volume Associated with the Metabolic Syndrome

MetS defined by four sets of criteria	β coefficient (95% confidence interval) ^a , macular thickness (μm) or volume (mm³)
	Macular thickness				Macular volume
	Global	Central	Parafoveal	Perifoveal	Macular volume
IDF criteria	−4.45 (−6.99 to −1.92)^d	−5.29 (−9.31 to −1.26)^b	−2.85 (−5.73 to 0.04)	−4.37 (−6.79 to −1.95)^d	−0.16 (−0.26 to −0.07)^d
IDF/AHA criteria	−3.64 (−6.11 to −1.17)^c	−3.57 (−7.49 to 0.36)	−1.89 (−4.71 to 0.92)	−3.82 (−6.18 to −1.47)^c	−0.13 (−0.22 to −0.04)^c
CDS criteria	−2.04 (−4.97 to 0.88)	−5.63 (−10.25 to −1.02)^b	−1.3 (−4.62 to 2.01)	−2.72 (−5.51 to 0.07)	−0.08 (−0.18 to 0.03)
NCEP ATP III criteria	−1.66 (−4.53 to 1.21)	−3.42 (−7.96 to 1.11)	−0.26 (−3.51 to 3)	−1.73 (−4.47 to 1)	−0.06 (−0.16 to 0.04)

β coefficient (95% confidence interval) was adjusted for age, sex, education, current alcohol drinking, and current smoking.

0.01 ≤ P < 0.05.

0.001 ≤ P < 0.01.

P < 0.001.

MetS, metabolic syndrome.

We assessed the statistical interactions of MetS defined by all four sets of criteria with sex on macular parameters, and we only detected a statistical interaction of the IDF-defined MetS with sex on macular thickness in the parafoveal subfield (P_interaction = 0.044). Further analyses stratified by sex suggested that the IDF-defined MetS was significantly associated with thinner parafoveal retinal thickness in men (multivariable-adjusted β = −6.25; 95% CI: −10.94 to −1.56; P = 0.009), but not in women (multivariable-adjusted β = −1.01; 95% CI: −4.65 to 2.62; P = 0.583) (Fig. 3).

FIG. 3.

Association of the metabolic syndrome with parafoveal retinal thickness by sex. The metabolic syndrome was defined according to the International Diabetes Federation criteria. β coefficient (95% confidence interval) was controlled for age, education, current smoking, and current alcohol drinking, and if applicable, for sex. **P < 0.05. MetS, metabolic syndrome; CI, confidence interval.

Associations of individual MetS components with macular thickness and volume

We defined individual components of MetS using the IDF criteria. After controlling for age, sex, education, current smoking, and alcohol consumption, obesity (P = 0.031) and high FBG (P = 0.033) were both significantly associated with thinner macula in the perifoveal region, and high FBG was also significantly associated with thinner macula in the global region (P = 0.023) and reduced macular volume (P = 0.024) (Table 3).

Table 3.

Association of Macular Thickness and Macular Volume with Individual Components of the Metabolic Syndrome Defined by the IDF Criteria (n = 705)

Traits of MetS defined by IDF criteria	β coefficient (95% confidence interval) ^a . macular thickness (μm) or volume (mm³)
	Macular thickness				Macular volume
	Global	Central	Parafoveal	Perifoveal	Macular volume
Obesity (n = 465)	−2.57 (−5.27 to 0.13)	−3.33 (−7.61 to 0.95)	−2.93 (−5.99 to 0.14)	−2.83 (−5.41 to −0.25)^b	−0.1 (−0.19 to 0)
High blood pressure (n = 568)	0.95 (−2.08 to 3.98)	3.01 (−1.78 to 7.8)	−0.64 (−4.07 to 2.79)	−1.73 (−4.62 to 1.16)	0.04 (−0.07 to 0.15)
High FBG (n = 229)	−2.95 (−5.5 to −0.4)^b	−2.63 (−6.67 to 1.41)	−1.56 (−4.45 to 1.34)	−2.65 (−5.08 to −0.21)^b	−0.11 (−0.2 to −0.01)^b
Low HDL-C (n = 95)	1.56 (−1.95 to 5.07)	0.7 (−4.86 to 6.25)	2.6 (−1.38 to 6.57)	1.43 (−1.92 to 4.78)	0.06 (−0.07 to 0.18)
High triglycerides (n = 212)	−2.22 (−4.84 to 0.39)	−2.89 (−7.03 to 1.25)	−1.85 (−4.81 to 1.12)	−2.04 (−4.53 to 0.46)	−0.08 (−0.18 to 0.01)

β coefficient (95% confidence interval) was controlled for age, sex, education, current smoking, and current alcohol drinking.

P < 0.05.

FBG, fasting blood glucose.

Discussion

In this population-based study of rural-dwelling Chinese older adults, we found that (1) MetS was associated with thinner macular thickness and reduced macular volume, but the associations varied by the MetS-defining criteria, (2) the association of the IDF-defined MetS with parafoveal retinal thickness varied by sex, such that MetS was associated with thinner parafoveal retinal thickness in men, but not in women, and (3) of the five MetS components defined by the IDF criteria, abdominal obesity and high FBG were associated with thinner perifoveal retinal thickness.

Our data showed that MetS was associated with thinner macular thickness in different subfields, depending on the MetS-defining criteria (i.e., the IDF, IDF/AHA, and CDS criteria). To the best of our knowledge, no population-based studies have thus far examined the association of MetS with retinal thickness parameters by using different diagnostic criteria to define MetS. Previously, the clinical-based studies of middle-aged adults showed an association of MetS with thinner macular thickness in the central and parafoveal subfields, in which MetS was defined using the NCEP ATP III criteria.^28,29 Our population-based study showed no association of the NCEP ATP III-defined MetS with retinal thickness parameters and the discrepant results may be attributed, in part, to differences in the study settings (clinical vs. the general population setting), demographic and clinical characteristics of study participants, and relatively high cutoff value of waist circumference in the NCEP ATP III criteria for Chinese adult population.

Another clinical-based study of middle-aged adults suggested that the IDF-defined MetS was associated with macular thinning in the central and inferior parafoveal subfields,¹³ which is partly consistent with our results. Our population-based study revealed the associations of the IDF-defined MetS with widespread macular thinning in the central and perifoveal regions. This suggests that MetS defined using the IDF criteria may be more sensitive than other defining criteria with regard to its association with macular markers in Chinese rural elderly people. In addition, we found that MetS was associated with thinner macula in perifoveal and global regions when using the IDF/AHA criteria, and only in central region when using the CDS criteria. Abdominal obesity is a prerequisite for defining MetS in the IDF criteria, but not in the IDF/AHA and CDS criteria. It is possible that abdominal obesity could exacerbate retinal thinning, depending on other coexisting metabolic abnormalities.

Furthermore, the strict cutoffs for high blood pressure and high blood glucose in the CDS criteria may increase diagnostic specificity but decrease the sensitivity for identifying retinal alterations. The various degrees of macular thinning across different subfields might be attributable to different cellular composition in these subfields. For instance, central fovea has a high concentration of cone photoreceptors and a low density of ganglion cells, whereas parafovea has a higher density of ganglion cells and perifovea has a higher concentration of rod photoreceptors.³⁰ However, we were unable to further examine specific retinal layers or cell types owing to lack of data on different retinal layers.

The mechanisms underlying the association of MetS with macular thinning are not fully understood and can only be speculated. First, the chronic oxidative stress in MetS may lead to retinal inflammation, especially in macular region, which is one of the most metabolized parts of human body,⁴ with a high density of photoreceptors that are polyunsaturated fatty acid-rich and are susceptible to oxidative damage.¹⁵ Elevated oxidative stress is mainly caused by mitochondrial dysfunction, proinflammatory state, and impaired antioxidant system.³¹ Second, MetS is known to be correlated with endothelial dysfunction,^32,33 partly owing to oxidative stress, hyperglycemia, hypertension, and dyslipidemia. These conditions may cause retinal microvascular alterations and blood–retinal barrier dysfunction. Indeed, MetS was associated with retinal microvascular changes, independent of diabetes, among middle-aged population.^9,10 Subsequently, experimental research has detected impaired blood–retinal barrier permeability and neuronal impairment in the retina of mouse model of MetS.³⁴

Third, hyperglycemia- and obesity-related insulin resistance plays an important role in pathophysiology of MetS.³⁵ Deficiency in insulin signaling pathway impairs the regulation of synaptic plasticity and neuronal survival,³⁶ which may contribute to retinal neurodegeneration. Finally, it has been hypothesized that the abnormal metabolic conditions may induce retinal neuronal loss,³⁴ thereby leading to macular thinning in people with MetS.

Previous studies have reported that women, older age, and Asians tend to have thinner macular thickness.³⁷ Our data showed that women had thinner macular thickness than men in all subfields, which is in line with previous studies.^37

–40 Furthermore, we identified a potential interactive effect between sex and the IDF-defined MetS on parafoveal retinal thickness, such that the association of the IDF-defined MetS with thinner parafoveal retinal thickness was evident only in men but not in women. Previous studies have not reported the sex difference in the association of MetS with retinal thickness. Indeed, sex hormones affect the regulation of retinal physiopathology through sex hormone receptors in the retina for both genomic and nongenomic pathways.^41,42

In addition, it has been well documented that women are less susceptible to oxidative stress than men, which might be owing partly to antioxidative properties of estrogen, sex differences in the expression and activities of antioxidant enzymes, or other yet undefined mechanisms.⁴³ Whereas female participants in our study were mostly postmenopausal with low levels of estrogen, we hypothesized that this sex difference in late life might have resulted from a complex interplay of lifelong sex hormones with sex differences in oxidative stress mechanisms and retinal physiopathology.^41

–45 Further prospective cohort studies are needed to explore the potential role of age-related alteration in sex hormones of both estrogen and androgens in pathophysiology of MetS. However, women were more likely to be excluded from the final analyses than men mainly owing to suboptimal image quality and retinal disorders, which might potentially lead to an underestimation of association between MetS and macular thickness in women.

We also examined the associations of the MetS components with macular thickness and volume. Our study showed that of the five MetS components, reduced macular thickness and volume were mostly associated with obesity and high FBG. Accumulation of visceral fat, which is thought to be involved in abdominal obesity,⁴⁶ has been associated with thinner RNFL thickness in a clinical-based study.⁴⁷ Our study showed that abdominal obesity (defined as waist circumference ≥90 cm for men and ≥80 cm for women) is associated with thinner perifoveal retinal thickness. We did not find evidence supporting an association of macular thickness with elevated triglycerides, reduced HDL-C, or elevated blood pressure, which is similar to the findings from previous studies.^38,48
–50

Long-term diabetes is known to cause retinopathy, and may have an impact on early retinal neurodegeneration, thus causing thinning of the RNFL, ganglion cell complex, and ganglion cell-inner plexiform layer.^7,8,51 The Maastricht study from the Netherlands suggested that macular thickness was reduced in people with prediabetes or diabetes, even before the development of clinical diabetic retinopathy.⁸ In line with this report, our study observed an association of high FBG (≥5.6 mM) with thinner macular thickness in global and perifoveal subfields.

Our study is the first population-based study to explore the association of MetS with macular parameters among rural-dwelling Chinese older adults. In addition, we used four different sets of criteria to define MetS; thus, we were able to compare different definitions and identify the MetS defined by which criteria is most strongly associated with macular thickness and volume in Chinese elderly population. Our study also has some limitations. First, this was a cross-sectional study, which cannot establish any causal relationship and the cross-sectional association might be subject to selective survival bias. Second, owing to lack of data on the thickness of retinal layers, we were unable to assess which specific retinal layers or retinal cell types were associated with MetS.

Third, we did not have OCT angiography data or quantitative assessments of drusen and RPE thickness; thus, we were not able to evaluate the association of MetS with parameters of microvascular structural and oxidative stress-related retinal alterations, which would shed more light on the neurodegenerative mechanisms underlying the association between MetS and macular thinning. Fourth, the control group in the analysis included individuals with one or two MetS components, which might lead to the underestimation of the association between MetS and health outcomes (e.g., macular parameters), as previously reported.⁵² Finally, our study sample was derived from one rural area of western Shandong province, where older adults had limited education and relatively low income. Thus, caution is needed when generalizing our study findings to other populations.

Conclusion

In conclusion, we found that MetS, especially the IDF-defined MetS, was associated with thinner macular thickness and reduced macular volume among older adults living in rural China, and that the association of the IDF-defined MetS with parafoveal retinal thickness was evident only in men. These findings suggest that MetS may be involved in the development of retinal neurodegeneration. Retinal examination among local rural-dwelling older adults with MetS may have a potential implication for early detection and precision prevention of retinal neurodegeneration. Further longitudinal studies are needed to explore the potential causal role of MetS and its components in retinal neurodegeneration in older adults as well as the underlying inflammatory mechanism and potential effect of anti-inflammatory therapy on the MetS-induced retinal neurodegeneration.

Footnotes

Acknowledgments

The authors thank all the participants of the MIND-China OCT subproject as well as clinical and research staff in MIND-China Study Group for their collaboration in data collection and management. In addition, the authors thank Dr Heather Snyder for valuable comments to help improve the article.

Authors' Contributions

C.Z., Q.Z., Y.W., Y.Du, and C.Q. contributed to the study design. C.Z., Q.Z., Y.W., R.L., Y.Dong, Z.S., and Y.S. contributed to the data collection and assessments. C.Z. and R.L. conducted the data analysis. C.Z. drafted the first version of the article. Y.Du and C.Q. supervised this study. All authors contributed to the critical revision and approved the final version of the article.

Ethics Approval and Consent to Participate

The study was approved by the Ethics Committee at Shandong Provincial Hospital affiliated to Shandong University, Jinan, Shandong, China (LCYJ: NO. 2018–014). Written informed consent was obtained from all participants or, in the case of cognitively impaired persons and illiterate persons, from a proxy (usually a family member). MIND-China was registered in the Chinese Clinical Trial Registry (Registration No.: ChiCTR1800017758, 13/08/2018).

Availability of Data and Materials

The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request and approval by the MIND-China Steering Committee at Shandong Provincial Hospital, Jinan, Shandong, China.

Author Disclosure Statement

The authors declare that they have no competing interests.

Funding Information

This study was supported in part by grants from the National Key Research and Development Program of China (Grant No. 2017YFC1310100), the Academic Promotion Program of Shandong First Medical University, and the Taishan Scholar Program of Shandong Province, China, Shandong Province postdoctoral innovation research program. C Qiu received grants from the Swedish Research Council (VR, Grant Nos. 2017–05819 and 2020–01574), the Swedish Foundation for International Cooperation in Research and Higher Education (STINT, Grant No. CH2019–8320) for the Joint China-Sweden Mobility Program, and Karolinska Institutet (Grant Nos. 2018–01854 and 2020–01456), Stockholm, Sweden. The funding agency had no role in the study design, data collection and analysis, writing of this article, or decision on publishing.

Supplementary Material

Supplementary Table S1

Supplementary Table S2

References

McNeill

, Rosamond

, Girman

, et al. The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study. Diabetes Care, 2005; 28(2):385–390; doi: 10.2337/diacare.28.2.385

Elhadad

, Wilson

, Zaghlool

, et al. Metabolic syndrome and the plasma proteome: From association to causation. Cardiovasc Diabetol, 2021; 20(1):111; doi: 10.1186/s12933-021-01299-2

O'Neill

, O'Driscoll

. Metabolic syndrome: A closer look at the growing epidemic and its associated pathologies. Obes Rev, 2015; 16(1):1–12; doi: 10.1111/obr.12229

Joyal

, Gantner

, Smith

LEH

. Retinal energy demands control vascular supply of the retina in development and disease: The role of neuronal lipid and glucose metabolism. Prog Retin Eye Res, 2018; 64:131–156; doi: 10.1016/j.preteyeres.2017.11.002

Snyder

, Alber

, Alt

, et al. Retinal imaging in Alzheimer's and neurodegenerative diseases. Alzheimers Dement, 2021; 17(1):103–111; doi: 10.1002/alz.12179

Jia

, Zhong

, Bao

, et al. Evaluation of early retinal nerve injury in type 2 diabetes patients without diabetic retinopathy. Front Endocrinol (Lausanne), 2020; 11:475672; doi: 10.3389/fendo.2020.475672

De Clerck

EEB

, Schouten

JSAG

, Berendschot

TTJM

, et al. Loss of temporal peripapillary retinal nerve fibers in prediabetes or type 2 diabetes without diabetic retinopathy: The Maastricht Study. Invest Ophthalmol Vis Sci, 2017; 58(2):1017–1027; doi: 10.1167/iovs.16-19638

De Clerck

EEB

, Schouten

, Berendschot

, et al. Macular thinning in prediabetes or type 2 diabetes without diabetic retinopathy: The Maastricht Study. Acta Ophthalmol, 2018; 96(2):174–182; doi: 10.1111/aos.13570

Wong

, Duncan

, Golden

, et al. Associations between the metabolic syndrome and retinal microvascular signs: The Atherosclerosis Risk In Communities study. Invest Ophthalmol Vis Sci, 2004; 45(9):2949–2954; doi: 10.1167/iovs.04-0069

10.

Zhao

, Yang

, Wang

, et al. Associations between metabolic syndrome and syndrome components and retinal microvascular signs in a rural Chinese population: The Handan Eye Study. Graefes Arch Clin Exp Ophthalmol, 2012; 250(12):1755–1763; doi: 10.1007/s00417-012-2109-2

11.

Keenan

, Fan

, Klein

. Retinopathy in nondiabetic persons with the metabolic syndrome: Findings from the Third National Health and Nutrition Examination Survey. Am J Ophthalmol, 2009; 147(5):934–944, 944.e1–e2; doi: 10.1016/j.ajo.2008.12.009

12.

Peng

, Wang

, Liang

, et al. Retinopathy in persons without diabetes: The Handan Eye Study. Ophthalmology, 2010; 117(3):531–537, 537.e1–e2; doi: 10.1016/j.ophtha.2009.07.045

13.

New

, Leow

, Vasudevan

, et al. Relationship between anthropometric and biochemical changes of metabolic syndrome with retinal nerve fiber layer and macular thickness. PLoS One, 2021; 16(2):e0246830; doi: 10.1371/journal.pone.0246830

14.

Zarei

, Anvari

, Eslami

, et al. Retinal nerve fibre layer thickness is reduced in metabolic syndrome. Diabet Med, 2017; 34(8):1061–1066; doi: 10.1111/dme.13369

15.

Baksheeva

, Tiulina

, Tikhomirova

, et al. Suppression of light-induced oxidative stress in the retina by mitochondria-targeted antioxidant. Antioxidants (Basel), 2018; 8(1):3; doi: 10.3390/antiox8010003

16.

Curcio

, Allen

. Topography of ganglion cells in human retina. J Comp Neurol, 1990; 300(1):5–25; doi: 10.1002/cne.903000103

17.

Bourne

RRA

, Flaxman

, Braithwaite

, et al. Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: A systematic review and meta-analysis. Lancet Glob Health, 2017; 5(9):e888–e897; doi: 10.1016/S2214-109X(17)30293-0

18.

Kassi

, Pervanidou

, Kaltsas

, et al. Metabolic syndrome: Definitions and controversies. BMC Med, 2011; 9:48; doi: 10.1186/1741-7015-9-48

19.

Wang

, Han

, Zhang

, et al. Health status and risk profiles for brain aging of rural-dwelling older adults: Data from the interdisciplinary baseline assessments in MIND-China. Alzheimers Dement (N Y), 2022; 8(1):e12254; doi: 10.1002/trc2.12254

20.

Kivipelto

, Mangialasche

, Snyder

, et al. World-Wide FINGERS Network: A global approach to risk reduction and prevention of dementia. Alzheimers Dement, 2020; 16(7):1078–1094; doi: 10.1002/alz.12123

21.

Ross

, Neeland

, Yamashita

, et al. Waist circumference as a vital sign in clinical practice: A Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol, 2020; 16(3):177–189; doi: 10.1038/s41574-019-0310-7

22.

Wang

, Zhang

, Hou

, et al. Differential associations of 6 adiposity indices with dementia in older adults: The MIND-China Study. J Am Med Dir Assoc, 2023; 24(9):1412–1419.e4; doi: 10.1016/j.jamda.2023.06.029

23.

Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA, 2001; 285(19):2486–2497; doi: 10.1001/jama.285.19.2486

24.

Alberti

, Zimmet

, Shaw

. Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med, 2006; 23(5):469–480; doi: 10.1111/j.1464-5491.2006.01858.x

25.

Alberti

, Eckel

, Grundy

, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation, 2009; 120(16):1640–1645; doi: 10.1161/circulationaha.109.192644

26.

Metabolic syndrome research collaboration group from Chinese Diabetes Society. Suggestions on metabolic syndrome from Chinese Diabetes Society. Chin J Diabetes, 2004; 12(3):151–161.

27.

Early Treatment Diabetic Retinopathy Study Research Group. Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs—An Extension of the Modified Airlie House Classification. Ophthalmology, 1991; 98(5):786–806; doi: 10.1016/s0161-6420(13)38012-9

28.

Karaca

, Karaca

. Beyond hyperglycemia, evidence for retinal neurodegeneration in metabolic syndrome. Invest Ophthalmol Vis Sci, 2018; 59(3):1360–1367; doi: 10.1167/iovs.17-23376

29.

Polat

, Celik

, Togac

, et al. Retinal neurodegeneration in metabolic syndrome: A spectral optical coherence tomography study. Int J Ophthalmol, 2023; 16(2):224–232; doi: 10.1824/0/ijo.2023.02.08

30.

Kolb

, Nelson

, Ahnelt

, et al. The Architecture of the Human Fovea. In: Webvision: The Organization of the Retina and Visual System. ( Kolb

, Fernandez

, Nelson

eds.) University of Utah Health Sciences Center: Salt Lake City (UT); 1995.

31.

Monserrat-Mesquida

, Quetglas-Llabrés

, Capó

, et al. Metabolic syndrome is associated with oxidative stress and proinflammatory state. Antioxidants (Basel), 2020; 9(3):236; doi: 10.3390/antiox9030236

32.

Janus

, Szahidewicz-Krupska

, Mazur

, et al. Insulin resistance and endothelial dysfunction constitute a common therapeutic target in cardiometabolic disorders. Mediators Inflamm, 2016; 2016:3634948; doi: 10.1155/2016/3634948

33.

Grandl

, Wolfrum

. Hemostasis, endothelial stress, inflammation, and the metabolic syndrome. Semin Immunopathol, 2018; 40(2):215–224; doi: 10.1007/s00281-017-0666-5

34.

Paz

, Barcelona

, Subirada

, et al. Metabolic syndrome triggered by fructose diet impairs neuronal function and vascular integrity in ApoE-KO mouse retinas: Implications of autophagy deficient activation. Front Cell Dev Biol, 2020; 8:573987; doi: 10.3389/fcell.2020.573987

35.

Laclaustra

, Corella

, Ordovas

. Metabolic syndrome pathophysiology: The role of adipose tissue. Nutr Metab Cardiovasc Dis, 2007; 17(2):125–139; doi: 10.1016/j.numecd.2006.10.005

36.

Benarroch

. Insulin-like growth factors in the brain and their potential clinical implications. Neurology, 2012; 79(21):2148–2153; doi: 10.1212/WNL.0b013e3182752eef

37.

Myers

, Klein

BEK

, Meuer

, et al. Retinal thickness measured by spectral-domain optical coherence tomography in eyes without retinal abnormalities: The Beaver Dam Eye Study. Am J Ophthalmol, 2015; 159(3):445–456.e1; doi: 10.1016/j.ajo.2014.11.025

38.

Patel

, Foster

, Grossi

, et al. Spectral-domain optical coherence tomography imaging in 67,321 adults: Associations with Macular Thickness in the UK Biobank Study. Ophthalmology, 2016; 123(4):829–840; doi: 10.1016/j.ophtha.2015.11.009

39.

Gupta

, Sidhartha

, Tham

, et al. Determinants of macular thickness using spectral domain optical coherence tomography in healthy eyes: The Singapore Chinese Eye study. Invest Ophthalmol Vis Sci, 2013; 54(13):7968–7976; doi: 10.1167/iovs.13-12436

40.

von Hanno

, Lade

, Mathiesen

, et al. Macular thickness in healthy eyes of adults (N = 4508) and relation to sex, age and refraction: The Tromsø Eye Study (2007–2008). Acta Ophthalmol, 2017; 95(3):262–269; doi: 10.1111/aos.13337

41.

Ogueta

, Schwartz

, Yamashita

, et al. Estrogen receptor in the human eye: Influence of gender and age on gene expression. Invest Ophthalmol Vis Sci, 1999; 40(9):1906–1911.

42.

Gupta

, Johar

, Nagpal

, et al. Sex hormone receptors in the human eye. Surv Ophthalmol, 2005; 50(3):274–284; doi: 10.1016/j.survophthal.2005.02.005.

43.

Kander

, Cui

, Liu

. Gender difference in oxidative stress: A new look at the mechanisms for cardiovascular diseases. J Cell Mol Med, 2017; 21(5):1024–1032; doi: 10.1111/jcmm.13038

44.

Faulkner

, Belin de Chantemele

. Sex hormones, aging and cardiometabolic syndrome. Biol Sex Differ, 2019; 10(1):30; doi: 10.1186/s13293-019-0246-6

45.

Miller

, De Silva

, Jackman

, et al. Effect of gender and sex hormones on vascular oxidative stress. Clin Exp Pharmacol Physiol, 2007; 34(10):1037–1043; doi: 10.1111/j.1440-1681.2007.04732.x

46.

Oussaada

, van Galen

, Cooiman

, et al. The pathogenesis of obesity. Metabolism, 2019; 92:26–36; doi: 10.1016/j.metabol.2018.12.012

47.

Shiba

, Shiba

, Takahashi

, et al. Relationships among serum lipoprotein lipase mass, visceral fat, and retinal nerve fiber layer thickness. Graefes Arch Clin Exp Ophthalmol, 2015; 253(11):1883–1888; doi: 10.1007/s00417-014-2898-6

48.

Bloch

, Yonova-Doing

, Jones-Odeh

, et al. Genetic and environmental factors associated with the ganglion cell complex in a healthy aging British Cohort. JAMA Ophthalmol, 2017; 135(1):31–38; doi: 10.1001/jamaophthalmol.2016.4486

49.

Koh

, Tham

, Cheung

, et al. Determinants of ganglion cell-inner plexiform layer thickness measured by high-definition optical coherence tomography. Invest Ophthalmol Vis Sci, 2012; 53(9):5853–5859; doi: 10.1167/iovs.12-10414.

50.

Tham

, Chee

, Dai

, et al. Profiles of Ganglion Cell-Inner Plexiform Layer Thickness in a Multi-Ethnic Asian Population: The Singapore Epidemiology of Eye Diseases Study. Ophthalmology, 2020; 127(8):1064–1076; doi: 10.1016/j.ophtha.2020.01.055

51.

Khawaja

, Chua

, Hysi

, et al. Comparison of associations with different macular inner retinal thickness parameters in a Large Cohort: The UK Biobank. Ophthalmology, 2020; 127(1):62–71; doi: 10.1016/j.ophtha.2019.08.015

52.

Liang

, Yan

, Hao

, et al. Metabolic syndrome in patients with first-ever ischemic stroke: Prevalence and association with coronary heart disease. Sci Rep, 2022; 12(1):13042; doi: 10.1038/s41598-022-17369-8

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