Th authors of ‘A functional polymorphism rs10830963 in melatonin receptor 1B associated with the risk of gestational diabetes mellitus’ (Bioscience Reports (2019) 39, 12) have written a reply in response to the correspondence piece by Rosta et al. (Bioscience Reports (2020) 40, 2).

Keywords:
Gestational diabetes mellitus, MTNR1B, Polymorphism, meta analysis, Trial sequential analysis

To the editor,

Many thanks to Professor Klara Rosta, M.D., Ph.D., Gábor Firneisz, M.D., Ph.D., et al. for their interest on our recently published article, ‘A functional polymorphism rs10830963 in melatonin receptor1B associated with the risk of gestational diabetes mellitus’ [1] and appreciate their comments [2] on it. We believe that peer exchanges among different research groups can promote better research works.

In the recent study, according to 14 reported research data on the association between a functional polymorphism rs10830963 in MTNR1B gene and the risk of gestational diabetes mellitus, we performed a meta-analysis by using Stata software, version 12.0 (Stata Corp LP, College Station, TX, U.S.A.) [3,4]. The false positive report probability (FPRP) analyses were adopted to confirm the positive findings [5,6]. Klara Rosta, M.D., Ph.D., et al. paid attention to one included study (good works from Rosta et al., 2017) in this meta-analysis, then put forward some opinions and suggestions on the minor (rs10830963 G) allele frequencies (MAF), the calculation of effect value (odds ratios, ORs) and the indication of relevant clinical data (mean age and pre-pregnancy BMI). We are here to respond. If there are any inaccuracies in our response, we welcome to communicate again.

Since we read the original literature of Rosta et al., 2017 [7], we found that not the exact genotyping data but an MAF of each studied SNP locus, including rs10830963 was reported. Therefore, we can not extract the accurate sample size data of being successfully genotyped. According to the number of 287 GDM cases meet the International Association of the Diabetes and Pregnancy Study Group (IADPSG) criteria and 533 controls reported in the literature, we estimated the genotype data by using the Hardy–Weinberg equilibrium (HWE) genotype distributions. The approach is recognized. As reminded by the commentary, we have carefully verified the extraction MAF in the literature, and hereby we correct it and other relevant research data.

We recalculate the results using the new genotype data, and the association between the SNP rs10830963 and the risk of GDM is still confirmed (Figures 1–3). Further functional experimental studies are warranted to explore and clarify the potential mechanism. Meanwhile, the variant rs10830963 G allele was estimated significantly associated with an increased GDM risk (CG vs. CC: OR = 1.44, 95% CI = 1.06−1.95; GG vs. CC: OR = 2.06, 95% CI = 1.26−3.37; G vs. C: OR = 1.44, 95% CI = 1.16−1.78) in the meta-analysis for Rosta et al.’s study data (Figures 1–3). There are slightly different of OR and corresponding 95% CI from the original literature. Maybe it was caused by meta-analysis process for different algorithms with stata software.

Figure 1
Figure 1
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Forest plot on the risk of GDM associated with rs10830963 (CG vs. CC)
Figure 1
Figure 1
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Forest plot on the risk of GDM associated with rs10830963 (CG vs. CC)
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Figure 2
Figure 2
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Forest plot on the risk of GDM associated with rs10830963 (GG vs. CC)
Figure 2
Figure 2
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Forest plot on the risk of GDM associated with rs10830963 (GG vs. CC)
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Figure 3
Figure 3
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Forest plot on the risk of GDM associated with rs10830963 (G vs. C)
Figure 3
Figure 3
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Forest plot on the risk of GDM associated with rs10830963 (G vs. C)
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In epidemiological research, it is necessary to clarify the general demographic characteristics, and we have also carried out extraction and display in Tables 1–3. For the mean pre-pregnancy body mass index (BMI) and mean age values with the subjects, we have re-extracted and supplemented in the Table 1. The mean age of cases/controls were 32.04/30.51 in subjects of Austria and 33.70/31.25 of Hungary. Meanwhile, the mean BMI of cases/controls were 28.31/23.40 in Austria and 26.78/23.32 in Hungary (Table 1).

Table 1
Characteristics of the studies included in the meta-analysis
Author, yearCountryDiagnostic criteriaGenotyping methodsControlsNumber of case/controlMAF case/controlMean age of cases/controlsMean BMI of cases/controlsPHWE for controlsNOS score
Deng Z., 2011 China ADA Sequencing NGT 87/91 0.52/0.41 31.8 ± 4.6/29.7 ± 3.5 23.6 ± 3.0/21.5 ± 2.4 0.84 4 
Kim J.Y., 2011 Korea ADA TaqMan NGT 908/966 0.52/0.45 33.1/32.2 23.3 ± 4.0/21.4 ± 2.9 0.53 7 
Wang Y., 2011 China ADA TaqMan NGT 700/1029 0.46/0.43 30.0/32.0 21.5/21.7 0.81 8 
Vlassi M., 2012 Greece ADA PCR-RFLP NGT 77/98 0.41/0.28 35.4 ± 4.4/31.3 ± 5.2 25.8 ± 5.1/26.7 ± 6.2 0.02 4 
Huopio H., 2013 Finland ADA Sequenom Assay/TaqMan NGT 533/407 0.47/0.35 32.6/29.9 26.3 ± 4.7/24.1 ± 3.8 0.98 8 
Li C., 2013 China IADPSG PCR-RFLP NGT 350/480 0.45/0.40 32.4 ± 4.8/31.9 ± 5.2 25.3 ± 5.2/24.6 ± 4.6 0.79 8 
Qi J., 2013 China IADPSG Sequencing NGT 110/110 0.54/0.44 28.7 ± 3.1/28.1 ± 2.4 NA/NA 0.43 6 
Vejrazkova D., 2014 Czech WHO TaqMan NGT 458/422 0.38/0.29 34.1 ± 6.1/34.8 ± 15.1 24.3 ± 4.9/23.7 ± 4.2 0.48 8 
Wang X., 2014 China ADA PCR-RFLP NGT 184/235 0.42/0.45 28.2 ± 3.8/27.9 ± 4.1 21.2 ± 1.8/20.7 ± 1.4 0.53 6 
Junior J.P., 2015 Brazil ADA real-time PCR Healthy pregnant 183/183 0.28/0.20 32/29 32.0/25.4 0.11 7 
Liu Q., 2015 China ADA TaqMan NGT 674/674 0.51/0.44 31.6/32.1 24.4/25.2 0.02 8 
Tarnowski M., 2017 Poland IADPSG TaqMan NGT 204/207 0.39/0.31 31.7 ± 4.5/29.2 ± 5.0 25.1 ± 5.5/23.0 ± 4.0 0.112 7 
Popova P.V., 2017 Russia ADA RT-PCR Healthy pregnant 278/179 0.35/0.31 31.8 ± 4.8/29.4 ± 4.8 25.7 ± 5.9/22.9 ± 4.5 0.426 6 
Rosta K., 2017 Hungary and Austria IADPSG KASP assay Healthy pregnant 287/533 0.36/0.28 Hungary:33.70/31.25; Austria:32.04/30.51 Hungary:26.78/23.32; Austria:28.31/23.40 0.989 5 
Author, yearCountryDiagnostic criteriaGenotyping methodsControlsNumber of case/controlMAF case/controlMean age of cases/controlsMean BMI of cases/controlsPHWE for controlsNOS score
Deng Z., 2011 China ADA Sequencing NGT 87/91 0.52/0.41 31.8 ± 4.6/29.7 ± 3.5 23.6 ± 3.0/21.5 ± 2.4 0.84 4 
Kim J.Y., 2011 Korea ADA TaqMan NGT 908/966 0.52/0.45 33.1/32.2 23.3 ± 4.0/21.4 ± 2.9 0.53 7 
Wang Y., 2011 China ADA TaqMan NGT 700/1029 0.46/0.43 30.0/32.0 21.5/21.7 0.81 8 
Vlassi M., 2012 Greece ADA PCR-RFLP NGT 77/98 0.41/0.28 35.4 ± 4.4/31.3 ± 5.2 25.8 ± 5.1/26.7 ± 6.2 0.02 4 
Huopio H., 2013 Finland ADA Sequenom Assay/TaqMan NGT 533/407 0.47/0.35 32.6/29.9 26.3 ± 4.7/24.1 ± 3.8 0.98 8 
Li C., 2013 China IADPSG PCR-RFLP NGT 350/480 0.45/0.40 32.4 ± 4.8/31.9 ± 5.2 25.3 ± 5.2/24.6 ± 4.6 0.79 8 
Qi J., 2013 China IADPSG Sequencing NGT 110/110 0.54/0.44 28.7 ± 3.1/28.1 ± 2.4 NA/NA 0.43 6 
Vejrazkova D., 2014 Czech WHO TaqMan NGT 458/422 0.38/0.29 34.1 ± 6.1/34.8 ± 15.1 24.3 ± 4.9/23.7 ± 4.2 0.48 8 
Wang X., 2014 China ADA PCR-RFLP NGT 184/235 0.42/0.45 28.2 ± 3.8/27.9 ± 4.1 21.2 ± 1.8/20.7 ± 1.4 0.53 6 
Junior J.P., 2015 Brazil ADA real-time PCR Healthy pregnant 183/183 0.28/0.20 32/29 32.0/25.4 0.11 7 
Liu Q., 2015 China ADA TaqMan NGT 674/674 0.51/0.44 31.6/32.1 24.4/25.2 0.02 8 
Tarnowski M., 2017 Poland IADPSG TaqMan NGT 204/207 0.39/0.31 31.7 ± 4.5/29.2 ± 5.0 25.1 ± 5.5/23.0 ± 4.0 0.112 7 
Popova P.V., 2017 Russia ADA RT-PCR Healthy pregnant 278/179 0.35/0.31 31.8 ± 4.8/29.4 ± 4.8 25.7 ± 5.9/22.9 ± 4.5 0.426 6 
Rosta K., 2017 Hungary and Austria IADPSG KASP assay Healthy pregnant 287/533 0.36/0.28 Hungary:33.70/31.25; Austria:32.04/30.51 Hungary:26.78/23.32; Austria:28.31/23.40 0.989 5 

Abbreviations: ADA, American Diabetes Association; NGT, normal glucose tolerance; NOS, Newcastle–Ottawa Scale.

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Table 2
Meta-analysis of the MTNR1B rs10830963 polymorphism on GDM risk
SubgroupHeterozygous (CG vs. CC)Homozygous (GG vs. CC)Allele mogel (G vs. C)
Number of studiesCase/ControlOR (95% CI)PEffectNumber of studiesCase/ControlOR (95% CI)PEffectNumber of studiesCase/ControlOR(95% CI)PEffect
Overall 14 3952/4736 1.29 (1.16–1.44) <0.001 14 2628/2966 1.88 (1.55–2.27) <0.001 14 10066/11228 1.37 (1.25–1.50) <0.001 
Ethnicity 
Asian 7 2271/2916 1.15 (1.02–1.28) 0.020 7 1543/1796 1.52 (1.23–1.89) <0.001 7 6026/7170 1.23 (1.10–1.37) <0.001 
Caucasian 7 1681/1820 1.50 (1.31–1.72) <0.001 7 1085/1170 2.45 (1.99–3.02) <0.001 7 4040/4058 1.55 (1.41–1.71) <0.001 
SubgroupHeterozygous (CG vs. CC)Homozygous (GG vs. CC)Allele mogel (G vs. C)
Number of studiesCase/ControlOR (95% CI)PEffectNumber of studiesCase/ControlOR (95% CI)PEffectNumber of studiesCase/ControlOR(95% CI)PEffect
Overall 14 3952/4736 1.29 (1.16–1.44) <0.001 14 2628/2966 1.88 (1.55–2.27) <0.001 14 10066/11228 1.37 (1.25–1.50) <0.001 
Ethnicity 
Asian 7 2271/2916 1.15 (1.02–1.28) 0.020 7 1543/1796 1.52 (1.23–1.89) <0.001 7 6026/7170 1.23 (1.10–1.37) <0.001 
Caucasian 7 1681/1820 1.50 (1.31–1.72) <0.001 7 1085/1170 2.45 (1.99–3.02) <0.001 7 4040/4058 1.55 (1.41–1.71) <0.001 
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Table 3
FPRP analysis for the significant associations of the MTNR1B rs10830963 C>G polymorphism and GDM risk
OR (95%CI)Prior probability
0.250.10.010.0010.00010.00001
Overall 
CG vs. CC 1.29 (1.16–1.44) 0.002 0.005 0.056 0.375 0.857 0.984 
GG vs. CC 1.88 (1.55–2.27) 0.002 0.007 0.070 0.433 0.884 0.987 
G vs. C 1.37 (1.25–1.50) 0.001 0.004 0.038 0.286 0.800 0.976 
Asian 
CG vs. CC 1.15 (1.02–1.28) 0.057 0.153 0.664 0.952 0.995 1.000 
GG vs. CC 1.52 (1.23–1.89) 0.003 0.009 0.092 0.506 0.911 0.990 
G vs. C 1.23 (1.10–1.37) 0.003 0.010 0.097 0.519 0.915 0.991 
Caucasian 
CG vs. CC 1.50 (1.31–1.72) 0.002 0.007 0.074 0.446 0.889 0.988 
GG vs. CC 2.45 (1.99–3.02) 0.016 0.047 0.351 0.845 0.982 0.998 
G vs. C 1.55 (1.41–1.71) 0.002 0.005 0.056 0.375 0.857 0.984 
OR (95%CI)Prior probability
0.250.10.010.0010.00010.00001
Overall 
CG vs. CC 1.29 (1.16–1.44) 0.002 0.005 0.056 0.375 0.857 0.984 
GG vs. CC 1.88 (1.55–2.27) 0.002 0.007 0.070 0.433 0.884 0.987 
G vs. C 1.37 (1.25–1.50) 0.001 0.004 0.038 0.286 0.800 0.976 
Asian 
CG vs. CC 1.15 (1.02–1.28) 0.057 0.153 0.664 0.952 0.995 1.000 
GG vs. CC 1.52 (1.23–1.89) 0.003 0.009 0.092 0.506 0.911 0.990 
G vs. C 1.23 (1.10–1.37) 0.003 0.010 0.097 0.519 0.915 0.991 
Caucasian 
CG vs. CC 1.50 (1.31–1.72) 0.002 0.007 0.074 0.446 0.889 0.988 
GG vs. CC 2.45 (1.99–3.02) 0.016 0.047 0.351 0.845 0.982 0.998 
G vs. C 1.55 (1.41–1.71) 0.002 0.005 0.056 0.375 0.857 0.984 
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Thank you very much again for Klara Rosta, M.D., Ph.D., Gábor Firneisz, M.D., Ph.D., et al.’s thoughtfulness and preciseness. Your comments means a great deal to us. Next, we will improve our study work together with the editors of ‘Bioscience Reports’.

The authors declare that there are no competing interests associated with the manuscript.

BMI

body mass index

CI

confidence interval

FPRP

false positive report probability

GDM

gestational diabetes mellitus

MAF

minor allele frequency

OR

odds ratio

SNP

single nucleotide polymorphism

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Author notes

*

These authors are considered co-first authors.

© 2020 The Author(s).
2020
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