
==== Front
BMC Microbiol
BMC Microbiol
BMC Microbiology
1471-2180
BioMed Central London

3496
10.1186/s12866-024-03496-x
Research
Correlation of gut microbial diversity to sight-threatening diabetic retinopathy
Khan Rehana 12
Sharma Abhishek 3
Ravikumar Raghul 4
Sivaprasad Sobha 5
Raman Rajiv rajivpgraman@gmail.com

1
1 https://ror.org/02k0t9a94 grid.414795.a 0000 0004 1767 4984 Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Sankara Nethralaya, 18 College Road, Chennai, 600 006 Tamil Nadu India
2 https://ror.org/03r8z3t63 grid.1005.4 0000 0004 4902 0432 School of Optometry and Vision Science, University of New South Wales, Sydney, Australia
3 grid.17088.36 0000 0001 2150 1785 Michigan State University College of Human Medicine, East Lansing, MI USA
4 Biohub Data Science Pvt. Ltd, Chennai, Tamil Nadu India
5 https://ror.org/004hydx84 grid.512112.4 NIHR Moorfields Biomedical Research Centre, London and University College, London, UK
13 9 2024
13 9 2024
2024
24 34229 8 2023
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Purpose

To determine the association of gut microbiome diversity and sight-threatening diabetic retinopathy (STDR) amongst patients with pre-existing diabetes.

Methods

A cross-sectional study was performed, wherein 54 participants selected in total were placed into cases cohort if diagnosed with STDR and those without STDR but had a diagnosis of diabetes mellitus of at least 10-year duration were taken as controls. Statistical analysis comparing the gut microbial alpha diversity between cases and control groups as well as patients differentiated based on previously hypothesized Bacteroidetes/Firmicutes(B/F) ratio with an optimal cut-off 1.05 to identify patients with STDR were performed.

Results

Comparing gut microbial alpha diversity did not show any difference between cases and control groups. However, statistically significant difference was noted amongst patients with B/F ratio ≥1.05 when compared to B/F ratio < 1.05; ACE index [Cut-off < 1.05:773.83 ± 362.73; Cut-off > 1.05:728.03 ± 227.37; p-0.016]; Chao1index [Cut-off < 1.05:773.63 ± 361.88; Cut-off > 1.05:728.13 ± 227.58; p-0.016]; Simpson index [Cut-off < 1.05:0.998 ± 0.001; Cut-off > 1.05:0.997 ± 0.001; p-0.006]; Shannon index [Cut-off < 1.05:6.37 ± 0.49; Cut-off > 1.05:6.10 ± 0.43; p-0.003]. Sub-group analysis showed that cases with B/F ratio ≥ 1.05, divided into proliferative diabetic retinopathy (PDR) and clinically significant macular edema (CSME), showed decreased diversity compared to controls (B/F ratio < 1.05). For PDR, all four diversity indices significantly decreased (p < 0.05). However, for CSME, only Shannon and Simpson indices showed significant decrease in diversity (p < 0.05).

Conclusions

Based on clinical diagnosis, decreasing gut microbial diversity was observed among patients with STDR, although not statistically significant. When utilizing B/F ratio, the decreasing gut microbial diversity in STDR patients seems to be associated due to species richness and evenness in PDR when compared to decreasing species richness in CSME.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-024-03496-x.

Keywords

Diabetes mellitus
Sight-threatening diabetic retinopathy
Bacteroidetes
Firmicutes
Gut microbial diversity
Gut dysbiosis
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcBackground

The human body is host to tens of billions of microbes - bacteria, viruses, fungi, and protozoa - and their largest populations reside in the gut (small and large intestine), known as gut microbiota, and the microbiome refers to all the genes inside these microbial cells [1]. These microbes, in particular bacteria, live within humans from birth and create an ecosystem that is integral to health, such as digestion and immunity, both innate and adaptive [2]. Recently, there has been an interest to study the influence of aberrant composition or function of these microbes (dysbiosis) on several acute or chronic diseases such as diabetes mellitus (DM), inflammatory bowel disease, Alzheimer’s disease, Crohn’s disease, multiple sclerosis, muscular dystrophy, fibromyalgia and ocular diseases like diabetic retinopathy (DR), age related macular degeneration, uveitis, and Sjogren’s disease [3–10].

DR progresses from mild non-proliferative diabetic retinopathy (NPDR), to moderate and severe NPDR and ultimately proliferative diabetic retinopathy (PDR). Sight-threatening diabetic retinopathy (STDR), which primarily comprises of clinically significant macular edema (CSME) and proliferative diabetic retinopathy (PDR) with and without macular edema [11]. It is well known that the control of DM is important for reducing the risk of STDR, as suggested by several studies including the UK Prospective Diabetes Study and Diabetic Retinopathy Clinical Research [12]. Patients with STDR need an immediate referral for ophthalmological treatment to prevent loss of vision. All STDR patients need routine comprehensive diagnosis, monitoring, and eventually treatment by an ophthalmologist. Identifying this cohort is essential and timely screening, referral and treatment can prevent or slow down loss of visual acuity [13].

A growing body of evidence shows that type 2 DM is associated with alteration in gut microbiota, dysbiosis; the underlying mechanism is increased inflammation, increased oxidative stress, increased vascular permeability, increased obesity, and insulin resistance, and altered glycemic control, however the role of gut microbiome with respect to ocular diseases is limited [10, 14, 15]. In humans, there have been only a couple of studies reported by Nadine et al. and Das et al. [16, 17], which confirm a role of gut dysbiosis in terms of varying abundance of bacterial genera within patients with DR compared to Type 2 diabetic patients and healthy patients without DR. Similar findings in terms of abundance were identified in our pilot study [3], wherein we also identified Bacteroidetes to Firmicutes relative abundance ratio (B/F Ratio) with an optimal cut-off point of 1.05 above which it presents as a potential biomarker for STDR. However, these studies have not reported the role of gut microbial diversity in terms of species richness and evenness using all available diversity measures in the development of STDR.

The present study therefore investigates the association of dysbiosis in the gut microbiome, with respect to microbial diversity, in subjects with type 2 DM who do not have a diagnosis of DR (controls, with duration of DM of 10 years or more) versus those diagnosed with STDR (cases, any duration of DM). In addition, the association of gut microbial diversity in diabetic patients with a B/F ratio < 1.05 compared to B/F ratio ≥1.05 was also studied.

Materials and methods

Subject recruitment

Between April 2019 and October 2019, 58 eligible subjects were recruited in our initial pilot study from patients presenting to the tertiary eye care centre, Sankara Nethralaya, Chennai, India. Detailed methodology of that study has been discussed in detail in our previous paper [3]. After adjusting for missing data by deletion and utilizing a strict inclusion and exclusion criteria, in total 54 (21 controls and 33 cases) eligible participants were chosen. Subjects with type 2 DM underwent a comprehensive eye examination and were divided into two groups, controls if there was no evidence of DR but at least a 10 year or greater history of DM or cases if the presence of STDR (Clinically significant macular edema (CSME) and/or PDR) was diagnosed. Subjects with ocular pathologies that included but not limited to non-sight threatening DR, vascular retinopathy, ocular inflammatory or degenerative disorders were excluded. In addition, patients with recent antibiotic use within the last six months, as well as presence of any pre-existing systemic or neoplastic disease were also excluded. Of the study participants selected, initial demographic study variables were collected that included age, gender, height and weight, duration of DM, HbA1c (Glycosylated Hemoglobin), dietary preference (vegetarian or non-vegetarian), and associated systemic diseases, such as hypertension, coronary artery disease or dyslipidemia, based on history or medications. Vegetarians were those who were taking dairy and plant-based diets, and eggs, but no intake of fish, meat, or poultry.

The study was approved by the Institutional Review Board, Vision Research Foundation, Chennai. A written informed consent was obtained from study subjects, and the study complied with the tenets of the Declaration of Helsinki.

Fecal sample collection and sample processing for purified DNA (deoxyribonucleic acid)

After complete ophthalmic examination was conducted, a fecal swab was collected from each patient sample and the gut microbial DNA was isolated using Norgen Microbiome DNA isolation Kit (Catalogue number 64100) using spin column chromatography. Homogenized fecal sample was incubated for 5 min at 650C using Lysis additive. The supernatant of the homogenized sample was centrifuged, and the supernatant was collected. Binding buffer I was added to the supernatant. The lysate was incubated in ice for 10 min and centrifuged for 2 min. The supernatant was collected, and equal volume of 70% ethanol was added and loaded to the spin column. Binding Buffer B and Wash solution A was run through the column and the DNA was eluted out using 50 µl Elution Buffer B.

Genomic sequencing

V4 region of 16S rRNA (Ribonucleic acid) were targeted for amplification using the primer pair were 515F (5’-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5’ GGACTACHVGGGTWTCTAAT-3′). Illumina MiSeq with 250*2 paired end chemistry was used for sequencing and was performed by Npedia technologies. A FASTQ file generated from the sequencing was used for downstream analysis (Supplementary Fig. 1).

DADA2(1.14.1) [18], pipeline was used to process the FASTQ files in R version 3.6.3. SILVA v132 was used as a reference database and the downstream analysis was done using R package phyloseq [19]. Generated microbial presence and diversity was taxonomically classified at the phylum level.

Definitions used to study gut-microbial diversity

Alpha Diversity is the term used to measures the variance or diversity of microbes present within a particular sample. Alpha diversity values represent, ‘species richness’ and ‘species evenness.’

Species richness – a count of the number of different species presents in a sample.

Species evenness – a measure of relative abundance of different species that make up the richness. So, relative abundance measures the prevalence of different phyla in the gut.

There are several indices which are currently used in literature to represent these measures as there is no consensus on which is the most accurate and significant index to use [20]:

ACE index: estimates species richness, using sample coverage (sum of the probabilities of the observed species).

Chao1 index: estimates species richness; it gives more weight to the low abundance species.

Simpson’s index: for species richness and species evenness; it gives more weight to the species evenness.

Shannon index: for species richness and species evenness; it gives more weight to the species richness.

B/F ratio in cases and controls [3]

A two-sample t-test was done to compare the B/F abundance ratio between cases and controls. Multivariate linear regression analysis, adjusted for predetermined cofactors, was conducted to assess the significance of the B/F ratio in distinguishing between cases and controls. Employing Youden’s J statistics method, we determined an optimal B/F ratio cutoff point to differentiate the two study groups. Comparison of the most common gut phyla, Bacteroidetes (B) and Firmicutes (F), revealed a significantly higher B/F ratio in cases than controls (cases, ≥1.05; controls, < 1.05; P = 0.049).

Statistical analysis

A microbiome R package [21], and phyloseq R package were used to analyse alpha diversity; all the R visualization was done using ggplot2 (v 3.3.2). Further statistical analyses were performed using a standard software package (Stata, version 16.1, StataCorp). Descriptive statistics on patient characteristics were summarized and compared using univariate analysis. After testing for non-normality of data, Wilcoxon signed-rank sum test was performed to compare the median alpha diversity values between the cases and control groups as well as patients with a B/F ratio < 1.05 versus B/F ratio ≥1.05. Subgroup analysis of clinically diagnosed STDR grouped into PDR with/without Diabetic Macular Edema (DME) and CSME group were compared with the control group that comprised of diabetic patients with no presence of DR. Similarly, sub-group analysis to compare the median alpha diversity values between those with B/F ratio < 1.05 versus B/F ratio ≥1.05 was performed by separating the B/F ratio ≥1.05 group into patients diagnosed with PDR with or without the presence of DME and CSME as well as utilizing the B/F ratio < 1.05 as its own subgroup. These comparisons within each subgroup were once again performed using a wilcoxon signed-rank sum test. Differences between two independent means were calculated (two-sided test; p < 0.05) and a post hoc test was done to calculate power analysis.

Results

Baseline characteristics

Based on strict inclusion and exclusion criteria as well as accounting for complete data availability, a total of 54 sample participants with previously diagnosed type-2 DM were identified. Out of these 33 patients were diagnosed with STDR (CSME and/or PDR) and 21 patients were diagnosed with the absence of DR and with at least a 10-year prior history of clinically diagnosed DM. In addition, the total sample population was divided based on B/F ratio < 1.05 and B/F ratio ≥1.05 to compare baseline characteristics. No statistically significant difference was noted between the baseline characteristics of cases and controls and the only statistically significant difference between the groups divided based on B/F ratio cut-off of 1.05 was noted in terms of dietary intake. Of those cases with B/F ratio < 1.05, 20.83% were vegetarian while of those cases with B/F ratio ≥1.05, 53.33% were vegetarian (p = 0.016), (Table 1).

Table 1 Baseline characteristics of study population with respect to cases and controls as well as B/F ratio cut-off

N = 54	Controls (n = 21)
[DM without DR]	Cases (n = 33)
[STDR]	p	B/F Ratio < 1.05 (n = 24)	B/F Ratio ≥1.05 (n = 30)	p	
Age, mean ± SD	57.50 ± 7.60	57.45 ± 8.19	0.982	60.04 ± 6.68	56.27 ± 8.51	0.082	
Men N (%)	13 (61.90)	22 (66.66)	0.724	15 (62.50)	20 (66.67)	0.752	
Duration of DM, mean ± SD	13.96 ± 5.99	14.17 ± 9.52	0.929	14.38 ± 5.61	13.93 ± 9.51	0.845	
FBS, mean ± SD	156.90 ± 65.89	154.86 ± 70.61	0.916	149.29 ± 77.64	162.33 ± 58.29	0.484	
PPBS, mean ± SD	207.64 ± 86.55	203.07 ± 88.48	0.853	200.50 ± 101.28	208.90 ± 72.36	0.724	
HbA1c, mean ± SD	7.49 ± 1.48	7.48 ± 1.67	0.982	7.36 ± 1.70	7.64 ± 1.32	0.499	
Height, mean ± SD	162.24 ± 12.79	162.74 ± 13.68	0.894	160.46 ± 11.04	164.93 ± 12.83	0.499	
Weight, mean ± SD	69.34 ± 13.29	69.58 ± 13.91	0.950	67.48 ± 10.93	72.17 ± 13.76	0.180	
BMI, mean ± SD	26.53 ± 5.52	26.44 ± 5.99	0.956	26.38 ± 4.68	26.76 ± 5.93	0.799	
Vegetarian, N (%)	7 (33.33)	14 (42.42)	0.508	5 (20.83)	16 (53.33)	0.016	
Associated systemic diseases

(Based on history & medications)

							
Hypertension, N (%)	9 (42.86)	22 (66.66)	0.088	13 (54.17)	18 (60.00)	0.670	
Cardiovascular Disease, N (%)	6 (28.57)	8 (24.24)	0.726	4 (16.67)	10 (33.33)	0.169	
Dyslipidemia, N (%)	3 (14.29)	3 (9.09)	0.557	4 (16.67)	2 (6.67)	0.250	
SD: Standard Deviation; DM: Diabetes Mellitus; DR: Diabetic Retinopathy; B/F Ratio: Bacteroidetes/Firmicutes relative abundance ratio; BMI: Body Mass Index; HbA1c: Glycosylated Haemoglobin; FBS: Fasting Blood Sugar; PPBS: Post-Prandial Blood Sugar; Cases: Subjects with sight-threatening diabetic retinopathy (STDR); Controls: Subjects with diabetes mellitus, but no diabetic retinopathy

Patient characteristics were summarized and compared using descriptive statistics and univariate analysis

Comparison of alpha diversity values

When comparing median alpha diversity values, there seems to be a decreasing trend of diversity in the cases compared to controls in the ACE and Chao1 indices corresponding to species richness. In all four indices, the variations in median diversity values when comparing cases and controls were not found to be statistically significant, (Fig. 1).

Fig. 1 Comparison of median alpha diversity values between cases and controls

However, when comparing median alpha diversity values between patients with respect to B/F ratio, those with B/F ratio ≥1.05 seemed to have a decreasing trend of diversity in all four indices when compared to patients with B/F ratio < 1.05. This association of decreasing alpha diversity values among STDR patients with respect to B/F ratio was found to be statistically significant as well, (Fig. 2).

Fig. 2 Comparison of median alpha diversity values between Bacteroidetes/Firmicutes (B/F) ratio < 1.05 and B/F ratio ≥1.05

Sub-group analysis of clinically diagnosed cases (STDR) grouped into PDR and CSME also showed a decreasing diversity among ACE and Chao 1 indices, with no discernible variation in diversity within the Simpson and Shannon indices, when compared to the control group. (Table 2). The results were not found to be statistically significant.

Table 2 Comparison of alpha diversity values between clinically diagnosed STDR sub-grouped into PDR and CSME versus diabetic patients without DR

Alpha Diversity	Controls [DM without DR] (n = 21)
median (95% CI)	CSME
(n = 16)
median (95% CI)	p
(Control Vs CSME)	Controls [DM without DR]
(n = 21)
median (95% CI)	PDR (n = 17)
median (95% CI)	P (Control Vs PDR)	
ACE	769.65 (660.49–878.84)	753.98 (650.07–873.32)	0.774	769.65 (660.49–878.84)	728.07 (657.25–881.63)	0.488	
Chao1	769.11 (647.60–866.10)	733.58 (649.98–873.01)	0.797	769.11 (647.60–866.10)	728.25 (657.21–881.87)	0.78	
Simpson	0.997 (0.996–0.998)	0.998 (0.995–0.999)	0.916	0.997 (0.996–0.998)	0.998 (0.996–0.999)	0.601	
Shannon	6.19 (6.06–6.35)	6.34 (6.03–6.39)	0.868	6.19 (6.06–6.35)	6.22 (5.97–6.37)	0.977	
CI: Confidence Interval; DM: Diabetes Mellitus; DR: Diabetic Retinopathy; CSME: Clinically Significant Macular Edema; PDR: Proliferative Diabetic Retinopathy; STDR: Sight-Threatening Diabetic Retinopathy

Wilcoxon signed-rank sum test was performed to compare the median alpha diversity values between the cases and control groups

Sub-group analysis based on B/F ratio showed that the PDR and CSME groups with a B/F ratio ≥ 1.05 exhibited decreased diversity across all four indices compared to patients with a B/F ratio < 1.05. In the PDR group, the decreased alpha diversity values in all four indices were statistically significant (p < 0.05). However, in the CSME group, a statistically significant decrease in diversity was noted only in the Shannon and Simpson indices (p < 0.05) (Table 3).

Table 3 Comparison of alpha diversity values between patients with B/F ratio ≥ 1.05 (sub-grouped into PDR and CSME) and those with B/F ratio < 1.05

Alpha Diversity	BF Ratio < 1.05
(n = 24)
median (95% CI)	BF Ratio ≥1.05 (CSME) (n = 12)
median (95% CI)	p	BF Ratio < 1.05
(n = 24)
median (95% CI)	BF Ratio ≥1.05
(PDR) (n = 9)
median (95% CI)	p	
ACE	773.83 (759.34–789.78)	738.91 (612.02–867.74)	0.497	773.83 (759.34– 789.78)	670.74 (484.23–698.20)	0.032	
Chao1	773.63 (759.33–789.58)	738.58 (612.01–867.46)	0.497	773.63 (759.33–789.58)	670.60 (484.21–698.18)	0.032	
Simpson	0.998 (0.996–0.998)	0.997 (0.995–0.999)	0.018	0.998 (0.996–0.998)	0.997 (0.994–0.998)	0.003	
Shannon	6.37 (5.95–6.48)	6.06 (5.86–6.30)	0.045	6.37 (5.95–6.48)	5.94 (5.69–6.11)	0.003	
CI: Confidence Interval; B/F Ratio: Bacteroidetes/Firmicutes relative abundance ratio; CSME: Clinically Significant Macular Edema; PDR: Proliferative Diabetic Retinopathy; STDR: Sight-Threatening Diabetic Retinopathy

Wilcoxon signed-rank sum test was performed to compare the median alpha diversity values between the patients with a B/F ratio < 1.05 vs. B/F ratio ≥1.05

Discussion

Recent studies have focused on studying the human gut microbiota and its relevance to health and disease especially in relation to obesity and a hyperglycemic state by causing chronic inflammation [16, 22]. How could gut microbiome influence the host immune system or cause chronic inflammation? Normally, the intestinal lining prevents the migration of microbes and their metabolites from the gut lumen to the bloodstream. However, a change in the intestinal milieu, intestinal dysbiosis, may deregulate the barrier effect of gut lining, and cause – leaky gut syndrome. Predominant gram-negative bacterial phyla such as Bacteroidetes releases bacterial endotoxin, lipopolysaccharides (LPS), and triggers an innate or natural immunity, and thereby contribute to pro-inflammatory pathways resulting in vascular dysfunction. In a study done on a mice with DM (db/db mice) showed its fecal bacterial composition (predominantly in Bacteroidetes and Firmicutes), presenting with impaired intestinal barrier function and replicating some of the features of DR – acellular capillaries, activation of retina microglia, and infiltration of peripheral immune cells into the retina [23].

One of the strongest determinants of DR is the duration of hyperglycemia; however, in few of the subjects despite many years of DM, no DR is detected [24]. What protect these individuals remains an enigma? Is it due to the influence of gut microbiome? Can we establish a relationship between gut microbiome and DR? Would it lead to a new target for therapeutic intervention? Hence, evaluating gut microbiotome in two groups, each group lying at the two ends of the spectra of DR (either no DR or STDR) might solve a part of this puzzle. This was the driving force that resulted in us conducting our initial pilot study to investigate dysbiosis in the gut microbiome with respect to relative abundance in subjects with type 2 DM and compared the result in those who did not have DR (controls, with duration of DM of 10 years or more) with those who had STDR (cases, any duration of DM). Based on the initial results of our study [3], we concluded that gut dysbiosis in terms of relative abundance of microbial species might play a role in the development and severity of STDR among diabetic patients of a South Indian cohort. Specifically, when reviewing the ratio of Bacteroidetes/Firmicutes (B/F ratio), diabetics with a B/F ratio ≥1 were indicative of the presence of STDR.

Of note, we also found that amongst a growing body of literature describing disease-associated gut microbiota, loss of microbial diversity appears as a common feature when representing gut dysbiosis [25–32]. Hence, in our present study, we wanted to perform a secondary analysis of our pilot study to determine if there is an association of gut microbial diversity and the presence of STDR in diabetic patients. We noted that, the relationship between gut microbial diversity and diseased state tends to have an inverse correlation, a conclusion similarly arrived by previous literature as well [17, 18].

Regarding diversity indices, the clinically diagnosed cases were further grouped into patients either with a diagnosis of CSME, or PDR with or without DME involvement noted that there was no statistically significant variation in gut microbial diversity compared to diabetics with no DR. However, subgroup analysis of the population based on B/F ratio ≥1.05 grouped into patients either with a diagnosis of CSME, or PDR with or without DME involvement showed that there was a decrease in microbial diversity in both subgroups compared to the group which had a B/F ratio < 1.05 in all four alpha diversity indices.

While this decrease in microbial diversity was statistically significant for all four indices with respect to PDR, statistical significance was only noted for Shannon and Simpson indices with respect to CSME. This shows that decreasing microbial diversity based on richness and evenness could be associated with PDR as well as CSME with more weightage to the decreasing species richness associated with PDR alone. We speculate that this difference could be due to the fact that chronic inflammation plays an important role in the pathophysiology for DME, whereas, in eyes with PDR, it is the retinal ischemia that might be a dominating factor compared to just inflammation alone [33–38]. Close monitoring is necessary for the DM without DR group with a B/F ratio ≥ 1.05, as they may be at an increased risk of developing retinopathy changes.

It is also important to ponder whether this observation in the gut-retina axis might be the consequence rather than cause of disease as it is well known that the gut microbiota and the brain have a bidirectional communication mediated via the hypothalamic-pituitary axis (HPA axis) [39, 40]. The dysregulation of HPA-axis has been noted to be significantly higher in patients with moderate-to-severe DR when compared with patients with minimal or no DR [41]. There is a possibility that future prospective studies with a larger sample size might better elucidate such a relationship of gut dysbiosis in terms of decreased diversity with respect to the different diseased states within STDR.

Though the number of STDR (n = 33) subjects was less to correlate the gut microbial diversity, the post hoc power calculation showed a power of 93%, which suggested that the study has sufficient power. The strength of the study was our utilization of cutting edge next-generation sequencing technology in order to find the association between gut dysbiosis and diabetic retinopathy. However, the study has been limited by the fact that it is a cross-sectional study, and hence further prospective studies are required to strengthen this association. Additionally, the high costs involved limited our sample size, affecting the study’s power. Beta diversity should have been included to measure the similarity or dissimilarity of two communities in our analysis. Furthermore, our study is limited to primarily a South Indian population. Involving a sample cohort with multiple races, ethnicity and nationality would help better determine the translation of our study findings to the population as a whole.

Conclusion

A eubiotic gut is essential to the maintenance of human health. Our study shows a novel relationship between changes in the gut microbiota with respect to decrease in diversity and STDR. This work highlights several findings with potential clinical significance. Attempts to increase the gut microbial richness and evenness might have a therapeutic benefit in STDR. The options of altering gut microbiome like intermittent fasting, fecal microbial transplant, pre- and probiotics and antibiotics may find a role in future therapeutics of STDR [22].

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

Mr. Vijay Vaidyanathan, Npedia Tech; Biohub Data Science Pvt. Ltd, Chennai, Tamil Nadu, India.

Author contributions

R.R.N and S.S. contributed to conception and design of the study. R.K. helped in acquisition of the data. R.K. and A.S. wrote the main manuscript text and prepared all the tables. R.K. and A.S. assisted with statistical analyses. R.R assisted with genomic analysis and all authors reviewed the manuscript.

Funding

Novartis health care Pvt. Ltd, India.

Data availability

The sequence data generated during the current study are available in the Sequence Read Archive (SRA) at NCBI under Bioproject: PRJNA1150757.

Declarations

Ethics approval and consent to participate

The study was approved by the Institutional Review Board, Vision Research Foundation, Chennai. A written informed consent was obtained from study subjects, and the study complied with the tenets of the Declaration of Helsinki. All participants gave written informed consent to provide a stool sample and to the availability of the stored samples for additional bioassays after the study protocol was fully explained (Study reference number: 674 A-2018-P).

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Liang D Leung RK Guan W Au WW Involvement of gut microbiome in human health and disease: brief overview, knowledge gaps and research opportunities Gut Pathogens 2018 10 1 1 9 10.1186/s13099-018-0230-4 29375672
Liang D, Leung RK, Guan W, Au WW. Involvement of gut microbiome in human health and disease: brief overview, knowledge gaps and research opportunities. Gut Pathogens. 2018;10(1):1–9.29375672 10.1186/s13099-018-0230-4
2. Belkaid Y Hand TW Role of the microbiota in immunity and inflammation Cell 2014 157 1 121 41 10.1016/j.cell.2014.03.011 24679531
Belkaid Y, Hand TW. Role of the microbiota in immunity and inflammation. Cell. 2014;157(1):121–41.24679531 10.1016/j.cell.2014.03.011
3. Khan R Sharma A Ravikumar R Association between Gut Microbial abundance and Sight-threatening Diabetic Retinopathy Investig Ophthalmol Vis Sci 2021 62 7 19 10.1167/iovs.62.7.19
Khan R, Sharma A, Ravikumar R, et al. Association between Gut Microbial abundance and Sight-threatening Diabetic Retinopathy. Investig Ophthalmol Vis Sci. 2021;62(7):19.10.1167/iovs.62.7.19
4. Barlow GM Yu A Mathur R Role of the gut microbiome in obesity and diabetes mellitus Nutr Clin Pract 2015 30 6 787 97 10.1177/0884533615609896 26452391
Barlow GM, Yu A, Mathur R. Role of the gut microbiome in obesity and diabetes mellitus. Nutr Clin Pract. 2015;30(6):787–97.26452391 10.1177/0884533615609896
5. Scher JU Abramson SB The microbiome and rheumatoid arthritis Nat Rev Rheumatol 2011 7 10 569 10.1038/nrrheum.2011.121 21862983
Scher JU, Abramson SB. The microbiome and rheumatoid arthritis. Nat Rev Rheumatol. 2011;7(10):569.21862983 10.1038/nrrheum.2011.121
6. Jangi S Gandhi R Cox LM Alterations of the human gut microbiome in multiple sclerosis Nat Commun 2016 7 1 1 1 10.1038/ncomms12015
Jangi S, Gandhi R, Cox LM, et al. Alterations of the human gut microbiome in multiple sclerosis. Nat Commun. 2016;7(1):1–1.10.1038/ncomms12015
7. Gopalakrishnan V Helmink BA Spencer CN Reuben A Wargo JA The influence of the gut microbiome on cancer, immunity, and cancer immunotherapy Cancer Cell 2018 33 4 570 80 10.1016/j.ccell.2018.03.015 29634945
Gopalakrishnan V, Helmink BA, Spencer CN, Reuben A, Wargo JA. The influence of the gut microbiome on cancer, immunity, and cancer immunotherapy. Cancer Cell. 2018;33(4):570–80.29634945 10.1016/j.ccell.2018.03.015
8. Malan-Muller S Valles-Colomer M Raes J Lowry CA Seedat S Hemmings SM The gut microbiome and mental health: implications for anxiety-and trauma-related disorders OMICS 2018 22 2 90 107 10.1089/omi.2017.0077 28767318
Malan-Muller S, Valles-Colomer M, Raes J, Lowry CA, Seedat S, Hemmings SM. The gut microbiome and mental health: implications for anxiety-and trauma-related disorders. OMICS. 2018;22(2):90–107.28767318 10.1089/omi.2017.0077
9. Chakravarthy SK Jayasudha R Prashanthi GS Dysbiosis in the gut bacterial microbiome of patients with uveitis, an inflammatory disease of the eye Indian J Microbiol 2018 58 4 457 69 10.1007/s12088-018-0746-9 30262956
Chakravarthy SK, Jayasudha R, Prashanthi GS, et al. Dysbiosis in the gut bacterial microbiome of patients with uveitis, an inflammatory disease of the eye. Indian J Microbiol. 2018;58(4):457–69.30262956 10.1007/s12088-018-0746-9
10. Rowan S Taylor A The role of microbiota in retinal disease Retinal degenerative diseases 2018 Cham Springer 429 35
Rowan S, Taylor A. The role of microbiota in retinal disease. Retinal degenerative diseases. Cham: Springer; 2018. pp. 429–35.
11. Fong DS Aiello L Gardner TW Retinopathy in diabetes Diabetes Care 2004 27 suppl 1 s84 7 10.2337/diacare.27.2007.S84 14693935
Fong DS, Aiello L, Gardner TW, et al. Retinopathy in diabetes. Diabetes Care. 2004;27(suppl 1):s84–7.14693935 10.2337/diacare.27.2007.S84
12. Kohner EM Aldington SJ Stratton IM United Kingdom Prospective Diabetes Study, 30: diabetic retinopathy at diagnosis of non–insulin-dependent diabetes mellitus and associated risk factors Arch Ophthalmol 1998 116 3 297 303 10.1001/archopht.116.3.297 9514482
Kohner EM, Aldington SJ, Stratton IM, et al. United Kingdom Prospective Diabetes Study, 30: diabetic retinopathy at diagnosis of non–insulin-dependent diabetes mellitus and associated risk factors. Arch Ophthalmol. 1998;116(3):297–303.9514482 10.1001/archopht.116.3.297
13. Raman R Ramasamy K Rajalakshmi R Sivaprasad S Natarajan S Diabetic retinopathy screening guidelines in India: All India Ophthalmological Society diabetic retinopathy task force and Vitreoretinal Society of India Consensus Statement Indian J Ophthalmol 2021 69 3 678 10.4103/ijo.IJO_667_20 33269742
Raman R, Ramasamy K, Rajalakshmi R, Sivaprasad S, Natarajan S. Diabetic retinopathy screening guidelines in India: All India Ophthalmological Society diabetic retinopathy task force and Vitreoretinal Society of India Consensus Statement. Indian J Ophthalmol. 2021;69(3):678.33269742 10.4103/ijo.IJO_667_20
14. Karlsson FH Tremaroli V Nookaew I Gut metagenome in European women with normal, impaired, and diabetic glucose control Nature 2013 498 7452 99 103 10.1038/nature12198 23719380
Karlsson FH, Tremaroli V, Nookaew I, et al. Gut metagenome in European women with normal, impaired, and diabetic glucose control. Nature. 2013;498(7452):99–103.23719380 10.1038/nature12198
15. Clavel T Desmarchelier C Haller D Intestinal microbiota in metabolic diseases: from bacterial community structure and functions to species of pathophysiological relevance Gut Microbes 2014 5 4 544 51 10.4161/gmic.29331 25003516
Clavel T, Desmarchelier C, Haller D, et al. Intestinal microbiota in metabolic diseases: from bacterial community structure and functions to species of pathophysiological relevance. Gut Microbes. 2014;5(4):544–51.25003516 10.4161/gmic.29331
16. Moubayed NM Bhat RS Al Farraj D Al Dihani N El Ansary A Fahmy RM Screening and identification of gut anaerobes (Bacteroidetes) from human diabetic stool samples with and without retinopathy in comparison to control subjects Microb Pathog 2019 129 88 92 10.1016/j.micpath.2019.01.025 30708043
Moubayed NM, Bhat RS, Al Farraj D, Al Dihani N, El Ansary A, Fahmy RM. Screening and identification of gut anaerobes (Bacteroidetes) from human diabetic stool samples with and without retinopathy in comparison to control subjects. Microb Pathog. 2019;129:88–92.30708043 10.1016/j.micpath.2019.01.025
17. Das T Jayasudha R Chakravarthy S Alterations in the gut bacterial microbiome in people with type 2 diabetes mellitus and diabetic retinopathy Sci Rep 2012 11 1 1 5
Das T, Jayasudha R, Chakravarthy S, et al. Alterations in the gut bacterial microbiome in people with type 2 diabetes mellitus and diabetic retinopathy. Sci Rep. 2012;11(1):1–5.
18. Callahan BJ McMurdie PJ Rosen MJ DADA2: high-resolution sample inference from Illumina amplicon data Nat Methods 2016 13 7 581 3 10.1038/nmeth.3869 27214047
Callahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3.27214047 10.1038/nmeth.3869
19. McMurdie PJ Holmes S Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data PLoS ONE 2013 8 4 e61217 10.1371/journal.pone.0061217 23630581
McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8(4):e61217.23630581 10.1371/journal.pone.0061217
20. Kim BR Shin J Guevarra RB Deciphering diversity indices for a better understanding of microbial communities J Microbiol Biotechnol 2017 27 12 2089 93 10.4014/jmb.1709.09027 29032640
Kim BR, Shin J, Guevarra RB, et al. Deciphering diversity indices for a better understanding of microbial communities. J Microbiol Biotechnol. 2017;27(12):2089–93.29032640 10.4014/jmb.1709.09027
21. Leo Lahti S, Shetty et al. Tools for microbiome analysis in R. Version2.1.26. URL: http://microbiome.github.com/microbiome. (2017).
22. Floyd JL Grant MB The gut–Eye Axis: lessons learned from murine models Ophthalmol Therapy 2020 2 1 5
Floyd JL, Grant MB. The gut–Eye Axis: lessons learned from murine models. Ophthalmol Therapy. 2020;2:1–5.
23. Fernandes R Viana SD Nunes S Reis F Diabetic gut microbiota dysbiosis as an inflammaging and immunosenescence condition that fosters progression of retinopathy and nephropathy Biochim et Biophys Acta (BBA)-Molecular Basis Disease 2019 1865 7 1876 97 10.1016/j.bbadis.2018.09.032
Fernandes R, Viana SD, Nunes S, Reis F. Diabetic gut microbiota dysbiosis as an inflammaging and immunosenescence condition that fosters progression of retinopathy and nephropathy. Biochim et Biophys Acta (BBA)-Molecular Basis Disease. 2019;1865(7):1876–97.10.1016/j.bbadis.2018.09.032
24. Lee R Wong TY Sabanayagam C Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss Eye Vis 2015 2 1 1 25 10.1186/s40662-015-0026-2
Lee R, Wong TY, Sabanayagam C. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis. 2015;2(1):1–25.10.1186/s40662-015-0026-2
25. Clausen ML Agner T Lilje B Association of disease severity with skin microbiome and filaggrin gene mutations in adult atopic dermatitis JAMA Dermatology 2018 154 3 293 300 10.1001/jamadermatol.2017.5440 29344612
Clausen ML, Agner T, Lilje B, et al. Association of disease severity with skin microbiome and filaggrin gene mutations in adult atopic dermatitis. JAMA Dermatology. 2018;154(3):293–300.29344612 10.1001/jamadermatol.2017.5440
26. Sha S Xu B Wang X The biodiversity and composition of the dominant fecal microbiota in patients with inflammatory bowel disease Diagn Microbiol Infect Dis 2013 75 3 245 51 10.1016/j.diagmicrobio.2012.11.022 23276768
Sha S, Xu B, Wang X, et al. The biodiversity and composition of the dominant fecal microbiota in patients with inflammatory bowel disease. Diagn Microbiol Infect Dis. 2013;75(3):245–51.23276768 10.1016/j.diagmicrobio.2012.11.022
27. Matsuoka K Kanai T The gut microbiota and inflammatory bowel disease InSeminars Immunopathol 2015 37 47 55 10.1007/s00281-014-0454-4
Matsuoka K, Kanai T. The gut microbiota and inflammatory bowel disease. InSeminars Immunopathol. 2015;37:47–55.10.1007/s00281-014-0454-4
28. Ahn J Sinha R Pei Z Human gut microbiome and risk for colorectal cancer J Natl Cancer Inst 2013 105 24 1907 11 10.1093/jnci/djt300 24316595
Ahn J, Sinha R, Pei Z, et al. Human gut microbiome and risk for colorectal cancer. J Natl Cancer Inst. 2013;105(24):1907–11.24316595 10.1093/jnci/djt300
29. Kang DW Park JG Ilhan ZE Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children PLoS ONE 2013 8 7 e68322 10.1371/journal.pone.0068322 23844187
Kang DW, Park JG, Ilhan ZE, et al. Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children. PLoS ONE. 2013;8(7):e68322.23844187 10.1371/journal.pone.0068322
30. De Goffau MC Luopajärvi K Knip M Fecal microbiota composition differs between children with β-cell autoimmunity and those without Diabetes 2013 62 4 1238 44 10.2337/db12-0526 23274889
De Goffau MC, Luopajärvi K, Knip M, et al. Fecal microbiota composition differs between children with β-cell autoimmunity and those without. Diabetes. 2013;62(4):1238–44.23274889 10.2337/db12-0526
31. Jandhyala SM Madhulika A Deepika G Altered intestinal microbiota in patients with chronic pancreatitis: implications in diabetes and metabolic abnormalities Sci Rep 2017 7 43640 10.1038/srep43640 28255158
Jandhyala SM, Madhulika A, Deepika G, et al. Altered intestinal microbiota in patients with chronic pancreatitis: implications in diabetes and metabolic abnormalities. Sci Rep. 2017;7:43640.28255158 10.1038/srep43640
32. Sabharwal A Ganley K Miecznikowski JC Haase EM Barnes V Scannapieco FA The salivary microbiome of diabetic and non-diabetic adults with periodontal disease J Periodontol 2019 90 1 26 34 10.1002/JPER.18-0167 29999529
Sabharwal A, Ganley K, Miecznikowski JC, Haase EM, Barnes V, Scannapieco FA. The salivary microbiome of diabetic and non-diabetic adults with periodontal disease. J Periodontol. 2019;90(1):26–34.29999529 10.1002/JPER.18-0167
33. Romero-Aroca P, Baget-Bernaldiz M, Pareja-Rios A, Lopez-Galvez M, Navarro-Gil R, Verges R. Diabetic macular edema pathophysiology: vasogenic versus inflammatory. J Diabetes Res. 2016;28.
34. Noma H Mimura T Yasuda K Shimura M Role of inflammation in diabetic macular edema Ophthalmologica 2014 232 3 127 35 10.1159/000364955 25342084
Noma H, Mimura T, Yasuda K, Shimura M. Role of inflammation in diabetic macular edema. Ophthalmologica. 2014;232(3):127–35.25342084 10.1159/000364955
35. Das A McGuire PG Rangasamy S Diabetic macular edema: pathophysiology and novel therapeutic targets Ophthalmology 2015 122 7 1375 94 10.1016/j.ophtha.2015.03.024 25935789
Das A, McGuire PG, Rangasamy S. Diabetic macular edema: pathophysiology and novel therapeutic targets. Ophthalmology. 2015;122(7):1375–94.25935789 10.1016/j.ophtha.2015.03.024
36. Bresnick GH De Venecia G Myers FL Harris JA Davis MD Retinal ischemia in diabetic retinopathy Arch Ophthalmol 1975 93 12 1300 10 10.1001/archopht.1975.01010020934002 1200895
Bresnick GH, De Venecia G, Myers FL, Harris JA, Davis MD. Retinal ischemia in diabetic retinopathy. Arch Ophthalmol. 1975;93(12):1300–10.1200895 10.1001/archopht.1975.01010020934002
37. Takagi H Watanabe D Suzuma K Kurimoto M Suzuma I Ohashi H Novel role of erythropoietin in proliferative diabetic retinopathy Diabetes Res Clin Pract 2007 77 3 S62 4 10.1016/j.diabres.2007.01.035 17481772
Takagi H, Watanabe D, Suzuma K, Kurimoto M, Suzuma I, Ohashi H, et al. Novel role of erythropoietin in proliferative diabetic retinopathy. Diabetes Res Clin Pract. 2007;77(3):S62–4.17481772 10.1016/j.diabres.2007.01.035
38. Aouiss A Idrissi DA Kabine M Zaid Y Update of inflammatory proliferative retinopathy: ischemia, hypoxia and angiogenesis Curr Res Translational Med 2019 67 2 62 71 10.1016/j.retram.2019.01.005
Aouiss A, Idrissi DA, Kabine M, Zaid Y. Update of inflammatory proliferative retinopathy: ischemia, hypoxia and angiogenesis. Curr Res Translational Med. 2019;67(2):62–71.10.1016/j.retram.2019.01.005
39. Mosca A Leclerc M Hugot JP Gut microbiota diversity and human diseases: should we reintroduce key predators in our ecosystem? Front Microbiol 2016 7 455 10.3389/fmicb.2016.00455 27065999
Mosca A, Leclerc M, Hugot JP. Gut microbiota diversity and human diseases: should we reintroduce key predators in our ecosystem? Front Microbiol. 2016;7:455.27065999 10.3389/fmicb.2016.00455
40. Bravo JA, Forsythe P, Chew MV, Escaravage E, Savignac HM, Dinan TG et al. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proceedings of the National Academy of Sciences. 2011;108(38):16050-5.
41. Roy M Collier B Roy A Dysregulation of the hypothalamo-pituitary-adrenal axis and duration of diabetes J Diabet Complications 1991 5 4 218 20 10.1016/0891-6632(91)90079-5 1663954
Roy M, Collier B, Roy A. Dysregulation of the hypothalamo-pituitary-adrenal axis and duration of diabetes. J Diabet Complications. 1991;5(4):218–20.1663954 10.1016/0891-6632(91)90079-5
