Metabolic impact of the VDR rs1544410 in diabetic retinopathy

Caroline Severo de Assis; Tainá Gomes Diniz; João Otávio Scarano Alcântara; Vanessa Polyana Alves de Sousa Brito; Rayner Anderson Ferreira do Nascimento; Mayara Karla dos Santos Nunes; Alexandre Sérgio Silva; Isabella Wanderley de Queiroga Evangelista; Marina Gonçalves Monteiro Viturino; Rafaela Lira Formiga Cavalcanti de Lima; Darlene Camati Persuhn

doi:10.1371/journal.pone.0263346

. 2022 Feb 25;17(2):e0263346. doi: 10.1371/journal.pone.0263346

Metabolic impact of the VDR rs1544410 in diabetic retinopathy

Caroline Severo de Assis ¹, Tainá Gomes Diniz ¹, João Otávio Scarano Alcântara ¹, Vanessa Polyana Alves de Sousa Brito ¹, Rayner Anderson Ferreira do Nascimento ², Mayara Karla dos Santos Nunes ³, Alexandre Sérgio Silva ⁴, Isabella Wanderley de Queiroga Evangelista ⁵, Marina Gonçalves Monteiro Viturino ⁵, Rafaela Lira Formiga Cavalcanti de Lima ⁶, Darlene Camati Persuhn ^7,^*

Editor: Kanhaiya Singh⁸

PMCID: PMC8880825 PMID: 35213542

Abstract

Aims

To investigate the association between BsmI and DM2 in patients with and without DR and to correlate with clinical parameters in a population in northeastern Brazil.

Methods

Cross-sectional case-control study in which data were collected from 285 individuals, including 128 patients with DM2 and 157 with DR. Clinical, biochemical and anthropometric parameters were analyzed, in addition to the single nucleotide polymorphism (SNP) BsmI of the VDR gene (rs1544410), genotyped by PCR-RFLP.

Results

In the DR group we found a greater number of patients using insulin therapy (p = 0.000) and with longer duration of DM2 (p = 0.000), in addition to higher serum creatinine values (p = 0.001). Higher fasting glucose levels and higher frequency of insulinoterapy were independently observed in patients with DR and b allele carriers, when compared to BB.

Conclusion

The association of the bb/Bb genotypes (rs1544410) of the VDR gene with increased blood glucose levels and insulinoterapy may represent worse glicemic control in rs1544410 b allele carriers in DR Latin American individuals.

1. Introduction

Diabetes mellitus is global epidemic health issue that affects around 415 million adults [1] Diabetic retinopathy (DR) is a frequent complication in type 2 Diabetes Mellitus (DM2) [2, 3], and clinical and metabolic factors are associated with the development and progression of DR [4–6].

There is evidence that deficiency of Vitamin D (VD) is related to DM2 [7, 8]. Vitamin D may have a direct effect on the function of pancreatic beta cells, mediated by the vitamin D receptor (VDR) [9], in addition, altered VDR gene transcription may influence fasting glucose levels by two potentially additive effects of vitamin D on adipocytes and pancreas cells [10, 11]. There is evidence that Vitamin D deficiency affects the pathogenesis and progression of DR and that patients with proliferative diabetic retinopathy (PDR) have lower levels of 25(OH)D than those without diabetes [8, 12]. A meta-analysis encompassing 17,000 patients from several continents concluded that vitamin D deficiency increased the risk of DR in T2DM [13].

The relationship between vitamin D levels and DM2 and DR raises interest in investigating the effect of genetic aspects linked to this vitamin regarding its involvement in the etiology or modulation of DR, including single nucleotide polymorphisms (SNPs) in the Vitamin D receptor (VDR). The B allele of the BsmI polymorphism (rs1544410) of the VDR gene was associated with a lower risk of DR in Korean patients with DM2 [14], and the bb genotype was associated with a decrease in (25 [OH] D) in micro and macrovascular complications of DM2 in an Indian population [15]. However, there is no consensus in the literature on these relationships [13, 16–18], and the results seem to be influenced by the to geographic origin of patients.

Given the facts presented in the absence of data in the Brazilian population, we propose here to investigate the association between BsmI and DM2 in patients with and without DR and to correlate with clinical parameters using a case-control approach, in a population of northeastern Brazil.

2. Methods

2.1 Study design and ethical aspects

This cross-sectional case-control study was conducted in 285 patients recruited at the University Hospital Lauro Wanderley and the Basic Health Units of João Pessoa/Paraíba/Brazil. Data collection took place from 2014 to 2021 and patients invited to participate in the study signed an informed consent form. The project was approved by the Ethics Committee for Research with Human Beings of the Federal University of Paraíba (UFPB; Opinions No.: 796.459 (26/08/2014) and 3053068 (03/12/2018) ards of the institution and Resolution 466/2012 of the National Health Council.

Inclusion criteria were diagnosis of DM2 for at least 5 years, being in outpatient care. Exclusion criteria: diagnosis of DM1, insufficient DNA sample or with an inconclusive result in the genotypic analysis.

2.1.1 Clinical characterization

The diagnosis of DR was based on ophthalmoscopy after pupil dilation with 0.5% tropicamide. Images of the retina (macula and central disc) were captured at a 45° angle by a background camera. The images were analyzed according to the standards and recommendations of ACCORD (Action to Control Cardiovascular Risk in Diabetes) and the Early Treatment Diabetic Retinopathy Study (ETDRS). DM2 patients without DR (n = 128) constituted the control group and the group with some degree of retinopathy constituted the DR (n = 157). In the RD group, 49 patients had been diagnosed with DM2 for up to ten years; the other 108 patients had been diagnosed with DM2 for more than ten years. Regarding the classification according to DR stage, 103 participants had non-proliferative diabetic retinopathy (NPDR) and 54 of them had proliferative diabetic retinopathy (PDR). Patients with a medical diagnosis of hypertension or who reported presenting this condition and were taking long-term medication for blood pressure control were considered hypertensive. The classification was performed according to the American Heart Association Guideline, American College of Cardiology, and the American Society of Hypertension (2015) [18].

2.2. Blood sampling

The blood samples were collected by venipuncture after night fasting. For biochemical analysis in general, blood in the presence of clot activator, for the determination of HbA1C in the presence of anticoagulant K3EDTA and for the determination of glucose in the presence of anticoagulant sodium fluoride, the samples were centrifuged at a speed of 3000 rpm for 10 minutes at room temperature for the separation of serum or plasma and subjected to analysis within 2 hours after collection, except for the sample of HbA1C that was analyzed in hemolyzed whole blood.

For DNA extraction, blood collection was performed by a venous puncture in sterile tubes containing 7.2 mg of K3 EDTA. Blood samples were stored for up to 20 days at -20°C until DNA extraction was performed.

2.3. Biochemical analysis and anthropometric measurements

Enzymatic methods were used for total cholesterol, high-density lipoprotein (HDL) and triglyceride analyses. All the analyses were performed using an automated analyzer (Lab-Max 240, Labtest, Lagoa Santa, MG, Brazil) and standardized kits according to the manufacturer’s instructions (Labtest, Lagoa Santa, MG, Brazil).

Low-density lipoprotein (LDL) concentration was calculated using the Friedewald formula: [LDL] = [total cholesterol] − [HDL] − [triglycerides ÷ 5] [19]. Glycated hemoglobin (HbA1c), C-reactive protein (CRP) levels, were determined by an immunoturbidimetry technique (LabMax 240, Labtest, Lagoa Santa, MG, Brazil) using commercial kits (Labtest, Lagoa Santa, MG, Brazil).

For the classification of dyslipidemia, the criteria were established according to the Guidelines of the Brazilian Diabetes Society (2019): Total Cholesterol (<190mg / dL), LDL-C (<130mg / dL), HDL-C (≤40mg / dL) and Triglycerides (≥150mg / dL) [20]. For the values of Hba1c (≤6,5%), total cholesterol (≤190mg / dL), HDL (≥40mg / dL) and LDL (<130mg / dL), the cutoff points adopted followed the recommendations of the Guidelines of the Brazilian Diabetes Society (2019) [20]. For Triglycerides (<150mg / dL), based on the Brazilian Archives of Cardiology (2017) [21]. Serum creatinine according to sex, (women ≤0.995mg / dL; men ≤1.20mg / dL), according to the Brazilian Society of Nephrology (2011) [22]. C Reactive Protein (≤3mg / dL) according to the Brazilian Guidelines on Dyslipidemias and Atherosclerosis Prevention [23], and for Malondialdehyde, the reference value adopted was (≤3.31μM) [24].

The anthropometric variables were body weight (kg), which was measured with the use of a scale, and height (cm). The body mass index (BMI) was calculated by dividing body weight by height squared (in meters) and the individuals were classified according to the presence of overweight and obesity, according to Body Mass Index (BMI) in kg / m². The values for the adult age were: overweight when BMI = 25–29.9 kg/m2 and obesity when BMI ≥ 30kg /m2. For the elderly patients, overweight was defined as BMI = ≥27 kg / m² [25]. Clinical and anthropometric information such as gender, age, T2DM time, BMI and blood pressure were obtained in the clinical evaluation by a nutritionist from the research team and by the team in the endocrinology service.

2.4. Isolation of leukocyte DNA

To obtain leukocyte DNA, appropriate protocols were used. The samples were diluted in an initial lysis solution containing 10mM Tris-HCl pH, 8.5mM EDTA, 0.3M sucrose and 1% Triton-X-100. Centrifugation was performed at 3.200 rpm, and the supernatant was discarded. This process was repeated 3 times to obtain a leukocyte precipitate free from hemoglobin remnants. The precipitate was resuspended in a lysis solution containing 10mM Tris-HCl pH8.0, 0.5% sodium dodecyl sulfate (SDS), 5mM EDTA and 0.2μg proteinase K (Invitrogen, Carlsbad, CA, USA) and incubated at 55°C in a water bath for 7h. Then, 500μl of an aqueous solution of 1mM EDTA and 7.5M ammonium acetate was added and mixed for 30 seconds. The mixture was centrifuged for 10min at 14.000g at 4°C, and 700μl of the supernatant was transferred to a new tube where DNA precipitation was performed with 540μl of iced isopropanol. The DNA precipitate was washed with 70% ethanol, centrifuged (12.000g for 5min), dried and resuspended in Tris-EDTA buffer pH 8.0 [26]. The samples were kept at -20° C until genetic analysis.

2.5. rs1544410 genotyping

Genotypes were determined by PCR-RFLP. Appropriate primers were used to amplify the region of the gene containing the polymorphism [27], and amplification occurred under the following conditions: denaturation 94° C for 5 minutes, 30 cycles of denaturation (1 minute at 94° C), annealing (1 minute at 58° C) and extension (3 minutes at 72° C) with an extra 10 minute extension step minutes. The 825 bp product was digested with BsmI which recognizes and cleaves the polymorphic allele (b) generating two fragments (650 bp and another 175 bp) while the wild allele (B) remains at 825 bp. The genotypes were analyzed by 15% polyacrylamide gel electrophoresis and 0.5% silver nitrate staining.

2.6. Statistical analysis

SPSS 26.0 software (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. Normality in continuous variables was assessed using the Kolmogorov-Smirnov test, and distributions with p> 0.05 were accepted as variables with normal distribution and expressed as mean ± standard deviation values and evaluated by the t test of independent samples. The ’non normally distributed’ were expressed as median values and 95% confidence intervals and compared using the Mann-Whitney U test between groups. Nominal categorical variables were expressed as total number and percentage, analyzed by chi-square. Hardy-Weinberg equilibria were calculated to assess expected and observed genotypic and allelic frequencies were tested by chi-square. Data from the RD group were analyzed according to rs1544410 genotypes by independent T test and its non-parametric counterpart, and then a logistic regression model was used to establish which variables (fasting glucose, insulin therapy, sex, age and time of DM2) could be influenced by rs1544410 genotypes. A p value < 0.05 was considered significant for all analyses.

3. Results

The control (DM) and RD groups were similar regarding the frequency of hypertension, dyslipidemia, family history of DM, smoking, sedentary lifestyle, age, HbA1c, fasting glucose, LDL-c, triglycerides and BMI, however, in the group with DR, male gender (p = 0.001), insulin therapy (p = 0.000), and serum creatinine (p = 0.000) were higher in addition to a longer duration of DM2 (p = 0.000). In the DM group, patients had higher values of total cholesterol (p = 0.017) and HDL-c fraction (p = 0.008) (Table 1).

Table 1. Clinical, biochemical and metabolic parameters of the studied sample.

	DM (128)	RD (157)	p-value OR (IC 95%)
Sex (M%)	31 (24.2%)	68 (43.6%)	0.001^* 0.414 (0.247–0.691)
Hypertension (%)	82 (65.1%)	116 (74.4%)	0.090 0.643 (0.385–1.074)
Dyslipidemia (%)	102 (79.7%)	126 (80.3%)	0.905 0.965 (0.539–1.729)
Family history of DM (%)	87(71.3%)	110(72.8%)	0.778 0.926 (0.545–1.576)
Insulin treatment (%)	37 (29.4%)	112 (71.8%)	0.000^* 0.163 (0.097–0.274)
Tabagism (%)	07 (5.5%)	08 (5.1%)	0.886 1.079 (0.380–3.061)
Sedentary lifestyle (%)	68(53.5%)	81 (51.9%)	0.786 1.067 (0.667–1.706)
Age (years)	59.2 ± 10.1	60.8 ±8.3	0.159
DM2 duration (years)	7 (7.6–9.1)	17 (15–17.5)	0.000 ^*
HbA1c (%)	7.9±1.6	7.8±1.4	0.305
Glucose (mg/dL)	152.4±50.7	141.1±52	0.066
Total cholesterol (mg/dL)	183.4 ±41.1	170.9 ±45.6	0.017 ^*
HDL (mg/dL)	46.4 ±12.4	42.7±11.1	0.008 ^*
LDL (mg/dL)	104.2±37.5	98.1±39	0.185
Triglycerides (mg/dL)	163.5 (154.38–188.6)	115 (125.11–157)	0.094
Serum creatinine (mg/dL)	0.71 (0.71–0.80)	0.84 (0.85–0.95)	0.000 ^*
BMI (kg/m ² )	29.7 ±5.7	29 ±5.1	0.282

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*p <0.05 = statistically significant difference.

Categorical variables analyzed by chi-square test, values expressed as total value (and percentage) and confidence interval. Quantitative variables analyzed by independent t test (when in normal distribution and/or homogeneity) and presented as mean and ±SD; Variables that did not show normal distribution and/or homogeneity were analyzed using the Mann-Whitney U test and represented by median and (95%CI).

Table 2 shows the distributions of genotypes (BB + Bb / bb; Bb + bb / BB; BB + bb / Bb) and alleles (B / b) as a function of the DM control group compared to different DR categorizations; DR with ≤10 years of DM2 diagnosis; DR with >10 years of DM2 diagnosis; groups with RDNP and RDP, respectively. In all groups there was no difference in the genotypic or allelic distribution of the groups. The minor allelle (B) frequency (MAF) was 0.46 in control DM group and 0.43 in the DM RD group.

Table 2. Genotypic and allelic distribution of the polymorphism rs1544410 among type 2 diabetic patients without DR and RD, <10 years DM diagnostic, ≥10 years DM diagnostic, RDNP and RDP.

rs1544410 (BsmI)
Genotypes/Alleles	DM (128)	DR (157)	p-value OR (IC 95%)
BB+Bb / bb	65.63% (84) / 34.37% (44)	61.78% (97) / 38.22% (60)	0.585 1.181 (0.7259–1.921)
Bb+bb / BB	72.66% (93) / 27.34% (35)	75.16% (118) / 24.84% (39)	0.731 0.8782 (0.5163–1.494)
BB+bb / Bb	61.72% (79) / 38.28% (49)	63.06% (99) / 36.94% (58)	0.913 0.9445 (0.5834–1.529)
B /b	46.48% (119) / 53.52% (137)	43.31% (136) / 56.69% (178)	0.501 1.137 (0.8157–1.584)
Genotypes/Alleles	DM (128)	DR (49) (≤10 years DM diagnosis)	p OR (IC 95%)
BB+Bb / bb	65.63% (84) / 34.37% (44)	63.27% (31) / 36.73% (18)	0.906 1.109 (0.5582–2.201)
Bb+bb / BB	72.66% (93) / 27.34% (35)	73.47% (36) / 26.53% (13)	0.913 0.959 (0.4560–2.019)
BB+bb / Bb	61.72% (79) / 38.28% (49)	63.27% (31) / 36.73% (18)	0.987 0.936 (0.4735–1.851)
B /b	46.48% (119) / 53.52% (137)	44.90% (44) / 55.10% (54)	0.8817 1.066 (0.6677–1.702)
Genotypes/ Alleles	DM (128)	DR (108) (>10 years DM diagnosis)	p-value OR (IC 95%)
BB+Bb / bb	65.63% (84) / 34.37% (44)	61.11% (66) / 38.89% (42)	0.561 1.215 (0.7138–2.068)
Bb+bb / BB	72.66% (93) / 27.34% (35)	75.93% (82) / 24.07% (26)	0.673 0.8425 (0.4679–1.517)
BB+bb / Bb	61.72% (79) / 38.28% (49)	62.96% (68) / 37.04% (40)	0.951 0.9484 (0.5589–1.609)
B / b	46.48% (119) / 53.52% (137)	42.59% (92) / 57.41% (124)	0.451 1.171 (0.8128–1.686)
Genotypes/ Alleles	DM (128)	DRNP (103)	p-value OR (IC 95%)
BB+Bb / bb	65.63% (84) / 34.37% (44)	61.17% (63) / 38.83% (40)	0.574 1.212 (0.7073–2.077)
Bb+bb / BB	72.66% (93) / 27.34% (35)	76.70% (79) / 23.30% (24)	0.583 0.8072 (0.4430–1.471)
BB+bb / Bb	61.72% (79) / 38.28% (49)	62.14% (64) / 37.86% (39)	0.943 0.9825 (0.5757–1.677)
B / b	46.48% (119) / 53.52% (137)	42.23% (87) / 57.77% (119)	0.412 1.188 (0.8207–1.720)
Genotypes/Alleles	DM (128)	DRP (54)	p-value OR (IC 95%)
BB+Bb / bb	65.63% (84) / 34.37% (44)	62.96% (34) / 37.04% (20)	0.862 1.123 (0.5792–2.177)
Bb+bb / BB	72.66% (93) / 27.34% (35)	72.22% (39) / 27.78% (15)	0.9152 1.022 (0.5818–2.081)
BB+bb / Bb	61.72% (79) / 38.28% (49)	64.81% (35) / 35.19% (19)	0.821 0.8752 (0.4511–1.698)
B / b	46.48% (119) / 53.52% (137)	45.37% (49) / 54.63% (59)	0.937 1.046 (0.6658–1.643)

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Statistical analysis performed with the Chi-square test. VDR, vitamin D receptor.

Clinical parameters of the RD group (n = 157) were analyzed according to BsmI genotypes (BB x Bb+ bb). In the group with the Bb+bb genotype, insulin therapy was significantly more frequent (p = 0.014) and fasting glucose had a higher mean value in the same group (p = 0.047) (Table 3). A logistic regression model, adopting the BsmI genotypes as the dependent variable and with correction/adjustment variables, revealed that fasting glucose (p = 0.018) and insulin therapy (p = 0.036) were statistically significant predictors of increased odds of risk in the group of patients with the Bb+bb genotype (Table 4). The glycemia difference between the genotypic groups presents a Cohen’s effect size d = 0.5, both comparing only the BB x bb group and Bb+bb x BB. According to the literature, this value means an effect of medium magnitude [28].

Table 3. Clinical, biochemical and metabolic parameters of the RD group according to rs1544410 genotypes.

	BB (n = 39)	Bb+bb (n = 118)	p-value OR (IC 95%)
Sex (M%)	20 (51.3%)	48 (41%)	0.263
Sex (M%)	20 (51.3%)	48 (41%)	1.513 (0.731–3.134)
Hypertension (%)	26 (66.7%)	90 (76.9)	0.204
Hypertension (%)	26 (66.7%)	90 (76.9)	0.600 (0.272–1.325)
Dyslipidemia (%)	08 (29.6%)	25 (33.3%)	0.724
Dyslipidemia (%)	08 (29.6%)	25 (33.3%)	0.842 (0.324–2.189)
Family history of DM (%)	24 (63.2%)	86 (76.1%)	0.121
Family history of DM (%)	24 (63.2%)	86 (76.1%)	0.538 (0.245–1.184)
Insulin treatment (%)	22 (52.4%)	90 (76.9%)	0.014 ^*
Insulin treatment (%)	22 (52.4%)	90 (76.9%)	0.388 (0.181–0.834)
Tabagism (%)	02 (5.1%)	06 (5.1%)	1.000
Tabagism (%)	02 (5.1%)	06 (5.1%)	1.000 (0.193–5.171)
Sedentary lifestyle (%)	20 (51.3%)	61 (52.1%)	0.926
Sedentary lifestyle (%)	20 (51.3%)	61 (52.1%)	0.966 (0.468–1.996)
Age (years)	60.6±8.9	60.8±8.1	0.926
Age (years)	60.6±8.9	60.8±8.1	1.533 (-3.171–2.886)
DM2 duration (Years)	15 (13.2–18.6)	18 (15–17.7)	0.640
HbA1c (%)	7.6±1.3	7.7±1.5	0.593
Glucose (mg/dL)	126.3±44	145.8±54.7	0.047 ^*
Total cholesterol(mg/dL)	176.1±59	171.4±46.8	0.656
HDL	41 (38.6–46.9)	42 (40.6–44.4)	0.855
LDL	87.8 (90.2–104.6)	100 (89–124.5)	0.331
Triglycerides (mg/dL)	115 (112.4–158.2)	124 (136.4–169.5)	0.257
Creatinine (mg/dL)	0.83 (0.81–1.0)	0.84 (0.84–0.95)	0.677
BMI (KG/M ² )	27.5±4.6	28.7±4.6	0.163

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* p <0.05 = statistically significant difference.

Table 4. Logistic regression model analyzing the influence of rs1544410 genotypes and metabolic variables in patients with DR.

	B	p-value OR (IC 95%)
Glucose	0,010±0,004	0.018^* 1,010 (1,002–1.018)
Insulin treatment	0.982±0.470	0.036^* 2.671 (1.064–6.705)
Sex	0.388±0.409	0.342 1.475 (0.662–3.286)
Age (years)	0.017±0.025	0.498 1.017 (0.968–1.069)
DM2 duration (Years)	-0,010±0.028	0.731 0.990 (0.937–1.047)

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Source: research data. Binary Logistic Regression. Dependent variable: BB x Bb+bb (Bb+bb reference);

*p>0.05;

B = beta regression coefficient ± standard error; OR = upper and lower odds limits; Insulin treatment (use of insulin as a reference); Sex (male as reference).

4. Discussion

Vitamin D in its active form acts through its specific receptor (VDR), expressed in human tissues including the retina [29]. In addition to the association of lower levels of Vitamin D and increased risk of DM2 and RD [8, 13], several studies have also related VDR gene polymorphisms as risk factors for DM2 [30], pathogenesis [14, 15, 31], and DR progression [32].

The relationship of polymorphisms with the occurrence of chronic complications in diabetics has also been explored [33–35] and has a pronounced importance in the early identification of individuals at higher risk of developing specific complications. The SNP polymorphism rs1544410 is located in intron 8 in the 3’ regulatory region, which is involved in gene expression, especially through the regulation of messenger RNA stability [36, 37], which can result in reduced expression of the VDR gene [38]. Another possible mechanism is that the alternation of BsmI in the intronic sequence may influence protein expression [39].

The MAF found in this study was 0.47 in the diabetic group and 0.43 in DR patients while in another Brazilian sample from Minas Gerais State (Southeast region) of the country, the MAF found was 0.40 in the group of type 2 diabetics. In the samples of the 1000 genome project, obtained from healthy population, the MAF (A) for the SNP is 0.29. According to the SNP database (ncbi.nlm.gov) in two different Latin American populations the MAF found was 0.36 (n = 798) and 0.23 (n = 3896), in Europeans it was 0.39 and in African Americans 0.26. Interestingly, in Asian populations the MAF is significantly lower, showing that it is a SNP that is highly influenced by ethnic aspects of the population under analysis. These informations evidenciate similarity between the MAF found in this study and another sample of Brazilian diabetics and a Latin American sample.

The genotypic distribution found deviated from the Hardy Weinberg Equilibrium in both groups, DM2 and DM2 with DR. This finding has not been uncommon in studies involving the polymorphism in question and diabetes. The HWE imbalance was present in a study conducted in the USA [40] and another in Chile [41]. In a recent meta-analysis that evaluated the effect of rs1544410 on the risk of DM2, of the 37 studies analyzed, 10 showed a deviation from the HWE in samples from different locations around the world [42].

The “b” allele was associated with lower HDL levels and increased risk for T2DM and obesity in Arabs [43], and the occurrence of DR in Croatian population with DM1 [32] the bb genotype conferred greater insulin resistance in Caucasians [40].

Despite the evidence, there are few studies exploring the association of the SNP rs1544410 and RD; one of them in DM1 [32] and four in DM2 [14, 15, 44, 45]. In these studies, one found an association of the B allele with lower risk of DR in DM2 [14] and two studies found association between RD and the bb genotype in DM1 [32] and DM2 [15], whereas in other studies no genotypic or allelic association was found [44, 45]. The geographic regions involved in these results cover Europe [32, 44] and Asia [14, 15, 45], however there are no studies analyzing the relationship in the Latin American population, which characterizes this study as pioneer. The results of the present study do not show an association of the rs1544410 polymorphism with DR in any of the studied scenarios, either by classifying patients by duration of diabetes, by DR stage or by simple diagnosis of the condition.

The time of diabetes diagnosis was also evaluated by Búcan (2009), Hong (2015), Zhong (2015) and Cyganek (2006), and the results found reinforce the data obtained in the study presented here. A single study [14] used the RD stage classification variable controlling the time since DM2 diagnosis (mean of 13 years) between the groups. In this study, the BB, Bb and the B allele genotypes were related to a lower risk of DR.

Meta-analyses [17, 29] point to the absence of association between rs1544410 and DR in both DM1 and DM2. However, the meta-analysis conducted by Song (2019) indicates an association restricted to the Indian population, but that is the result of a single study [15]. Therefore, we consider it important to explore this possibility in the Brazilian population. As the association did not result in positive data, we decided to investigate the effect of different rs1544410 genotypes in patients with DR on clinical and metabolic parameters.

Among the existing studies with DM2 and DR, only one controlled the duration of DM2 between the groups, with no statistically significant differences [14], while Zhong (2015), Cyganek (2006) and Ezhilarasi (2018) present difference or do not report the comparison. Aware of the great influence of this variable on the studied aspect, in this work, we performed different comparison scenarios, including separating patients with DR up to 10 years of diagnosis and more than 10 years of DM2 diagnosis, comparing genotypic and allelic distributions with the control group. In none of these scenarios were the genotypes or alleles statistically different from the control.

As the association analysis found conflitant results in comparison to some previous studies [14, 15], we decided to investigate the effect of different rs1544410 genotypes on metabolic parameters, in order to assess whether despite not impacting the risk of DR, having knowledge of the genotype would have an impact on the knowledge of the individual’s metabolic profile. We compared the genotypic groups within the profile of DR carriers and identified that carriers of the b allele (Bb and bb) had higher blood glucose levels and a greater chance of insulin therapy than carriers of the BB genotype.

Persistent hyperglycemia triggers most of the processes that promote the development of DR and high blood glucose concentrations and high levels of HbA1C characterize poor glycemic control and associate them with an independent risk factor for DR [5, 6, 46–49].

The association between VDR variants (BsmI) and insulin resistance-related diseases, demonstrate controversial conclusions. While the BB+Bb genotypes and the B allele were related to insulin resistance-related diseases in darkly pigmented Caucasians [50], in a prospective cohort, the B allele was associated with increased insulin secretion in women with a history of gestational DM [51]. The BsmI polymorphism of the VDR gene was also associated with glycemic outcomes in other populations. BsmI was tested in a homogeneous population of healthy young men, at an age before the typical onset of DM2, in which physical activity was also measured. Individuals with the BB genotype had significantly higher fasting glucose levels than individuals with the Bb or bb genotype. This finding was associated with low physical activity (≤3h per week), but the effect was absent in men with a high degree of physical activity [52].

In addition to associations related to the vitamin D receptor gene and BsmI genotypes with glycemic markers, low serum concentrations of 25OHD3 have been associated with reduced insulin sensitivity, impaired glucose metabolism, and metabolic syndrome [53, 54]. The mechanism by which vitamin D affects insulin sensitivity is still unknown. Vitamin D can stimulate the expression of insulin receptors in peripheral tissues and, thus, increase glucose transport [55]. Insulin-mediated glucose uptake is calcium dependent and therefore vitamin D status may indirectly influence glucose uptake [56].

The b allele was considered a risk factor for low serum concentrations of 25-hydroxyvitamin D, in addition the same study reports that BsmI influenced glucose and HOMA-IR concentrations even after adjustments in Brazilian (Amazon State) children [57].

These evidences demonstrate the importance of going beyond the simple genotype-clinical condition relationship. It is possible that the lack of association found in some populations, including this study, is a reflection of the applied experimental design, and the influencing effect of other aspects not measured or analyzed, including other SNPs. The association between higher glucose levels and the b allele is relevant in the context studied, as this same genetic characteristic seems to influence vitamin D levels. The results found here encourage further analysis, taking into account clinical, metabolic and vitamin D levels of patients when evaluating the association of VDR genotypes and DR.

This is the first study to analyze a Latin American population for the influence of rs1544410 in the context of DR. The study has limitations related to its cross-sectional nature. The results found here encourage the performance of studies with other populations and with different experimental models to unravel the effects of other important components for DR.

5. Conclusion

We did not identify the BsmI SNP (rs1544410) of the VDR gene as a risk factor for DR, but the b allele may be indicative of increased glycemic levels and insulinoterapy in the population with DR. The relationship between VDR BsmI and susceptibility to DR in a Latin American population should be further studied.

Supporting information

S1 Data

(XLSX)

Click here for additional data file.^{(46.5KB, xlsx)}

Acknowledgments

We thank at the Lauro Wanderley University Hospital, Federal University of Paraíba and all participants in the study for participation and the financial support institutions, National Council for Scientific and Technological Development (CNPq, Brasilia, Brazil), the Coordination for the Improvement of Higher Education Personnel (CAPES, Brasília, Brazil) and the State of Paraíba Research Support Foundation (FAPESQ, Paraíba, Brazil). The authors declare that they have no competing interests.

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

This study was supported by Public Call n. 005/2020 Programa Pesquisa para o SUS - PPSUS - Paraíba State Research Foundation (FAPESQ, Paraíba, Brazil), National Council for Scientific and Technological Development (CNPq, Brasilia, Brazil) Ministry of Health / Decit/SCTIE (Decit/SCTIE/MS), State Health Secretary (SES/ Paraíba/Brazil); Grant 05/2021, Paraíba State Research Foundation (FAPESQ, Paraíba, Brazil). It was also supported by Public Call n. 03/2020 Produtividade em Pesquisa PROPESQ/PRPG/UFPB grant n. PIA13262-2020 and Coordination for the Improvement of Higher Education Personnel (CAPES) - Financial Code 001. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PONE-D-21-31455Metabolic impact of the VDR rs1544410 in diabetic retinopathyPLOS ONE

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Reviewer #1: The present manuscript by de Assis et al. describes case control association study to investigate association of BsmI and clinical parameters in diabetic retinopathy in a DM2 population in Northeastern Brazil. The authors have conducted clinical, biochemical, and anthropometric analysis, and found no association between rs1544410 and the presence of DR; however, they should provide justifications for following points:

1. The study is based on a small sample size of only 176 subjects (100 patients and 76 controls). Have authors considered power calculation and estimated required sample size?

2. Why have authors not considered healthy individuals as controls? Clinical characterization section mentions that DM2 patients without DR have been taken as control. DM2 patients currently not diagnosed with DR may develop it in future because risk of diabetic retinopathy is always associated with uncontrolled diabetes in long-term.

3. Why have authors included familial cases of DM (as indicated in Table 1) in case control studies? Should it not be based on sporadic cases only? Further, cases of other systemic problems such as hypertension have also been included. Such selection criteria of subjects might not represent true analysis of a complex disease such as diabetic retinopathy.

The authors should take necessary steps to address the above-mentioned points before reaching a conclusion.

Reviewer #2: The study by Severo de Assis et al. has investigated the association between BsmI

polymorphism (rs1544410) of the VDR gene and clinical parameters in diabetic retinopathy in a North-eastern Brazilian cohort consisting of Diabetes mellitus type-2 (DM2) patients. Overall, this is a clear, concise, and well-written manuscript. The introduction is relevant and the discussion section is enriched with sufficient information about the previous study findings for readers to follow the present study rationale and procedures. The methods are appropriate, results are clear. The study has not detected any significant association between the rs1544410 and causation of diabetic retinopathy (DR). However following points should be taken into consideration in the present form of this manuscript.

Major comments

1. In section ‘rs1544410 genotyping’ the RFLP method used for genotyping looks a bit archaic. Incomplete digestion can lead to erroneous genotypes. The authors should confirm genotypes with another method (i.e TaqMan or Sanger sequencing) to demonstrate that the bands they observed in gel represented actual genotypes and there were no errors due to incomplete digestion (i.e. failure to cut completely or failure to cut at all due to technical problems being interpreted erroneously as an absence of cut sites).

2. The authors should investigate rs1544410 based haplotype frequencies are associated with the presence of diabetic retinopathy in the conditions studied. Authors can use freely available computational tools such as SHEsis Online haplotype analysis software for this purpose.

3. The size of cohort is small. The authors should include Genetic power calculation for estimating the sample size in this study.

4. The authors should elaborate the exclusion criteria which were followed during recruitment of patients in the cohort.

5. The authors should include the allelic frequencies of SNP rs1544410 by making a query in the 1000 genomes database (https://www.internationalgenome.org), Single Nucleotide Polymorphism database (dbSNP, http://www.ncbi.nlm.nih.gov/SNP) and ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) database.

Minor comments

Please recheck the figure legends and statistical tables thoroughly for language errors.

Overall, results of the present study do not show an association of the rs1544410 polymorphism with the risk of DR causation. However, the authors have found statistically significant association with a biochemical parameter; fasting blood glucose of the DR group. In my opinion, the authors should significantly enrich the results of this manuscript by increasing the sample size and include Cox proportional hazards regression model or logistic regression models to analyse the effect of different risk factors on the causation of DR. In current study, the authors can also include a systematic and detailed meta-analysis to further enrich this manuscript.

**********

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PLoS One. 2022 Feb 25;17(2):e0263346. doi: 10.1371/journal.pone.0263346.r002

Author response to Decision Letter 0

4 Dec 2021

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: No

Reviewer #2: Partly

R: We added samples to the sample in both experimental groups and used other statistical resources (logistic regression) to demonstrate the consistency of the relationship found between fasting glucose and insulin use with the b allele of the studied polymorphism.

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

R: We hope that we have presented well-founded statistical analyzes in the new version of the article.

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

R: We are providing the complete database.

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

1. The study is based on a small sample size of only 176 subjects (100 patients and 76 controls). Have authors considered power calculation and estimated required sample size?

R: We agree with the reviewer when he indicated that a case-control study consisting of 176 individuals is small. To improve this aspect, we increased the number of groups to 128 in the control group and 157 in the RD group (total = 285).

Taking into account the genotypic distribution found in this study, OR BB x Bb+bb= 1.13, disease frequency= 0.2; MAF = 0.44; significance level 0.05, it would take ~2000 patients divided between cases and controls to achieve an expected power of ~0.8. This would be the number necessary to discriminate a possible difference between cases and controls due to the high allelic frequency and the OR of the genotypic distribution being very close to 1. This number of patients is unfeasible for our recruitment pace. However, in the review published by Song et al (2019), 7 studies compared rs1544410 genotypes in groups of DM x DM patients with DR. In only one of these studies the number of patients with DR was greater than what is presented in this article. Therefore, we consider that the study aggregates by bringing information from a population not yet studied in the literature.

It is also necessary to consider that the main finding of this study is not the lack of association of polymorphism with DR, but the differences in blood glucose between the genotypes.

Song, N., Yang, S., Wang, Y., Tang, S., Zhu, Y., Dai, Q., & Zhang, H. (2019). The Impact of Vitamin D Receptor Gene Polymorphisms on the Susceptibility of Diabetic Vascular Complications: A Meta-Analysis. Genetic Testing and Molecular Biomarkers, 23(8), 533–556. doi:10.1089/gtmb.2019.0037

R: It is true that patients in the control group can develop the complication over time, so we stratified the patient groups in genotypic comparisons not only by the degree of complication (RDNP and RDP) but also by the time of diabetes diagnosis (greater and less than 10 years). In all cases, the distribution of genotypes and alleles was similar, without any trend towards differences between groups. This reinforces the hypothesis that the polymorphism does not influence the occurrence of the complication in the studied group.

The inclusion of a control group without DM is performed in some studies. However, the direct comparison between a group without DM with DM with DR is not as informative as identifying differences when the comparison group consists only of diabetics without the complication. This is because the main interest is to identify risk markers for the complication in diabetes carriers. The advantage of including samples without DM in this study would be to bring a representative group of genotypic distribution in the absence of the pathology. It was not possible to fulfill this reviewer's request because this was not foreseen in the objectives of the original project.

The authors should take necessary steps to address the above-mentioned points before reaching a conclusion.

R: Patients included in the study were not related. The term “Family DM” was mistakenly used and replaced by the “family history of DM”. In this item, we investigated patients who claimed to have at least one relative up to the second degree with DM in their family.

RD is not an isolated clinical condition and affects individuals who usually have more than 5 years of diabetes. One of its main risk factors is high blood pressure, control of glycemic levels and dyslipidemia. More than 70% of patients with DR have SAH, which would make it very difficult to exclude patients with this and other chronic conditions from the sample. These data are in the descriptive presentation of all articles comparing diabetic patients with DM with DR in relation to specific genotypes.

As can be seen in table 3, only blood glucose and insulinoterapy were different when comparing the genotypic groups, although when comparing the clinical groups (Table 1) it is possible to identify other variables that distinguish the patients (duration of diabetes, creatinine, lipid parameters and %male sex). Such variables were analyzed and proved not to be influenced by genotype variation in the study population.

Reviewer #2: The study by Severo de Assis et al. has investigated the association between BsmI

Major comments

R: We appreciate the reviewer's analysis and agree that methods involving sequencing or probes are more recent. But it is possible to obtain genotypic quality data working manually using the following features: positive, negative and blank controls in all runs, guarantee of complete digestion by always repeating heterozygotes and doing random and blind repetition of at least one tenth of the samples analyzed in the study. In our laboratory, we do not have resources for acquiring probes, the expiration time also makes their use in our projects unfeasible. Therefore, our available method is RFLP. In order to demonstrate the data quality with which we work, I share below an image of one of our recent electrophoresis from the work under evaluation:

The 875 and 650pb fragments are distant and allow a clear differentiation between the genotypes. As a guarantee feature of the BB genotype, we have the absence of the 175bp fragment. The spot, which appears close to 400bp, corresponds to the digestion reaction mixture, probably the restriction endonuclease. As it runs well away from the genotypic decision bands, it also does not cause any interference in the interpretation of the result.

R: We found difficulties in accessing the program in question, although it remains used in articles published in 2021. However, we found that its main use is linked to haplotype analyses. The VDR gene has many polymorphisms, but the main ones are FokI, TaqI, ApaI and BsmI (studied in this article). As we did not study the other polymorphisms that could compose the haplotypes, it would not be possible to use this tool in our data, we supose. There are also few studies that assess the haplotypes in relation to the risk of DR, one of them being in Han Chinese type 2 diabetes patients [47] which found an association between the FokI and DR polymorphism and also identified a frequent haplotype in the DR group, which includes the b allele of the BsmI – which reinforces data from other studies that this would be the risk allele for the condition under study. As the study approach is specific to the BsmI polymorphism, we decided not to incorporate such information into the text of the article.

Zhong X, Du Y, Lei Y, Liu N, Guo Y, Pan T. Effects of vitamin D receptor gene polymorphism and clinical characteristics on risk of diabetic retinopathy in Han Chinese type 2 diabetes patients. Gene. 2015 Jul 25;566(2):212-6. doi: 10.1016/j.gene.2015.04.045.

3. The size of cohort is small. The authors should include Genetic power calculation for estimating the sample size in this study.

R: We agree with the reviewer when he indicated that a case-control study consisting of 176 individuals is small. Thinking about improving this aspect of the work, we increased the number of groups to 128 in the control group and 157 in the RD group (total = 285).

Taking into account the genotypic distribution found in this study, OR BB x Bb+bb= 1.13, disease frequency= 0.2; MAF = 0.44; significance level 0.05, it would take ~2000 patients divided between cases and controls to achieve an expected power of ~0.8. This would be the number necessary to discriminate a possible difference between cases and controls due to the high allelic frequency and the OR of the genotypic distribution being very close to 1. This number of patients is unfeasible for our recruitment pace and also for our structure. However, in the review published by Song et al (2019), 7 studies compared rs1544410 genotypes in groups of DM x DM patients with DR. In only one of these studies the number of patients with DR is greater than what is presented in this article. Therefore, we consider that the study aggregates by bringing information from a population not yet studied in the literature.

Regarding the result of the difference between the mean blood glucose levels of the BB X Bb+bb genotypes, the Cohen's calculated effect size d= 0.5, both comparing only the BB x bb group and including the heterozygote together with bb. According to the literature, this value means an effect of medium magnitude. We have added the information to the text of the results in the article.

Cohen J. Statistical power analysis for the behavioral sciences 2d ed. New York: Academic Press. 1988.

4. The authors should elaborate the exclusion criteria which were followed during recruitment of patients in the cohort.

R: The aim of the present study was to analyze the effect of variables that affect the risk of a diabetic individual to develop DR or to present a worse clinical or laboratory condition. The presence of SAH, dyslipidemia, elevation of creatinine were not considered exclusion criteria, but were collected in the clinical evaluation.

Text inserted in the methods:

R: Inclusion criteria were: diagnosis of DM2 for at least 5 years, being in outpatient care. Exclusion criteria: diagnosis of DM1, insufficient DNA sample or with an inconclusive result in the genotypic analysis. (LINES 117 TO 119)

R: Detailed information regarding allelic frequency and HWE was included in two paragraphs of the discussion: (LINES 283 TO 299)

The MAF found in this study was 0.47 in the diabetic group and 0.43 in DR patients while in another Brazilian sample from Minas Gerais State (Southeast region) of the country, the MAF found was 0.40 in the group of type 2 diabetics. In the samples of the 1000 genome project, obtained from healthy population, the MAF (A) for the SNP is 0.29. According to the SNP database (ncbi.nlm.gov) in two different Latin American populations the MAF found was 0.36 (n=798) and 0.23 (n=3896), in Europeans it was 0.39 and in African Americans 0.26. Interestingly, in Asian populations the MAF is significantly lower, showing that it is a SNP that is highly influenced by ethnic aspects of the population under analysis. These informations evidenciate similarity between the MAF found in this study and another sample of Brazilian diabetics and a Latin American sample.

The genotypic distribution found deviated from the Hardy Weinberg Equilibrium in both groups, DM2 and DM2 with DR. This finding has not been uncommon in studies involving samples in studies involving the polymorphism in question and diabetes. The HWE imbalance was present in a study conducted in the USA [42] and another in Chile [58]. In a recent meta-analysis that evaluated the effect of rs1544410 on the risk of DM2, of the 37 studies analyzed, 10 showed a deviation from the HWE in samples from different locations around the world [59].

Minor comments

Please recheck the figure legends and statistical tables thoroughly for language errors.

R: As we included new samples in the study, all tables and footnotes were reworked and rewritten. The entire statistical analysis was redone although the main data remained unchanged compared to the original version of the paper.

R: In fact, our main objective was to investigate whether the studied polymorphism affects metabolic aspects in the clinical context of diabetic retinopathy. Therefore, we analyzed in logistic regression whether the results found in the binary statistics (glycemia and insulin therapy) were in fact influenced by the presence of the b allele. It was not our goal to analyze causes of DR except for the effect of genotypes (which we found no association in the tested scenarios). We believe we met the reviewer's demand as we increased the sample size in both groups and included the logistic regression statistical model as recommended.

The meta-analysis was not included because there are already publications of this nature in the literature on the same subject and covering practically all articles published so far. Our discussion text cites some of them.

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PLoS One. doi: 10.1371/journal.pone.0263346.r003

Decision Letter 1

Kanhaiya Singh

17 Jan 2022

Metabolic impact of the VDR rs1544410 in diabetic retinopathy

PONE-D-21-31455R1

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Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: The current submission has examined the metabolic impact of VDR rs1544410 in diabetic retinopathy. The authors have submitted the revised version of the manuscript. The results and the rebuttal letter confirms that all the queries have been addressed.

**********

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PLoS One. doi: 10.1371/journal.pone.0263346.r004

Acceptance letter

Kanhaiya Singh

11 Feb 2022

PONE-D-21-31455R1

Metabolic impact of the VDR rs1544410 in Diabetic Retinopathy

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PERMALINK

Metabolic impact of the VDR rs1544410 in diabetic retinopathy

Caroline Severo de Assis

Tainá Gomes Diniz

João Otávio Scarano Alcântara

Vanessa Polyana Alves de Sousa Brito

Rayner Anderson Ferreira do Nascimento

Mayara Karla dos Santos Nunes

Alexandre Sérgio Silva

Isabella Wanderley de Queiroga Evangelista

Marina Gonçalves Monteiro Viturino

Rafaela Lira Formiga Cavalcanti de Lima

Darlene Camati Persuhn

Roles

Abstract

Aims

Methods

Results

Conclusion

1. Introduction

2. Methods

2.1 Study design and ethical aspects

2.1.1 Clinical characterization

2.2. Blood sampling

2.3. Biochemical analysis and anthropometric measurements

2.4. Isolation of leukocyte DNA

2.5. rs1544410 genotyping

2.6. Statistical analysis

3. Results

Table 1. Clinical, biochemical and metabolic parameters of the studied sample.

Table 2. Genotypic and allelic distribution of the polymorphism rs1544410 among type 2 diabetic patients without DR and RD, <10 years DM diagnostic, ≥10 years DM diagnostic, RDNP and RDP.

Table 3. Clinical, biochemical and metabolic parameters of the RD group according to rs1544410 genotypes.

Table 4. Logistic regression model analyzing the influence of rs1544410 genotypes and metabolic variables in patients with DR.

4. Discussion

5. Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Kanhaiya Singh

Roles

Author response to Decision Letter 0

Decision Letter 1

Kanhaiya Singh

Roles

Acceptance letter

Kanhaiya Singh

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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