TECHNICAL FIELD

The present invention relates generally to the field of molecular diagnostics and prognostics. More particularly, it relates to the use of genome variations for prognostication for type 2 diabetes mellitus.

BACKGROUND OF THE INVENTION

Genetic variability contributes an estimated 30-70% of T2DM risk, and previous studies show that at least 20 common genetic variants are associated with T2DM (Hedayati et al., 2016). The association of some SNPs with T2DM were replicated using genome-wide association studies (GWAS) across several population groups. For example, among European populations, With the advent of molecular biotechniques, candidate gene approaches and genome-wide association studies have made possible the interrogation of several genes to human diseases. Several of these genes code for regulators of transcription, transmembrane proteins, transporter molecules, receptors and channels that regulate glucose, insulin and potassium levels, or have unknown functions (McCarthy & Zeggini, 2009). This genetic linkage offers the opportunity to recognize as early as birth who are prone to become diabetics in the future.

Applications for prognostication are important in the presence of actionable parameters to prevent diabetes if one knows if s/he is susceptible. First, dietary changes can be adapted as carbohydrate intake can be crucial in the development of type 2 diabetes. Second, one can be made conscious of maintaining a good weight, as obesity is a risk factor for insulin resistance and T2DM. Here, exercise and activity may be good recommendations in addition to diet. Third, at-risk individuals may be encouraged to monitor their bodies more closely to watch out for signs for diabetes.

The association of variants with phenotypes or outcomes such as T2DM and its complications may vary significantly among different populations. Inter-ethnic variability is best demonstrated in population sub-studies. In a meta-analysis on ADIPOQ variants rsl6861194 and rsl7300539, a significant increase in T2DM risk was shown among Europeans but not among Asians, and the variant rs266729 increases risk of T2DM in both Europeans and Asians alike (H. Chu et al., 2013). Another meta-analysis examined the association of PPARGC1A variant rs8192678 with the different components of metabolic syndrome. It showed that among non-Asians, those with the AA genotype have lower fasting blood glucose and total cholesterol levels than those with the GG + GA genotypes, while among Asians, those with the GG genotype have significantly higher body mass index, showing that the same variant may confer different risks to different populations (Bhatta et al., 2020). This variability makes it necessary to perform population specific genetic applications.

Currently, although there are several applications for T2DM markers, there are still no specific commercialized genetic-based diabetes prognosticator, especially in the Philippines. Some known patent applications that refers to T2DM genomic markers were filed by La Roche (W02006063703A2 [A3,A8]). Lee (KR1020090035068 and KR1020140102933), Collier and colleagues (W02008058332), Yang (W02020232034), and Salonen and colleagues (WO2007128884), to name a few. There may be several factors for this lack of commercial kits. First, it seems that most application foresee the markers as diagnostics rather than prognostics. In these cases, the impact is depreciated considering that T2DM testing is less complicated, inexpensive and very reliable with the routine and established metabolite assays. Second, the inherent difficulty in validating the preventive effect due to the expensive and impractical experimental designs can be a deterrent for clinical verification. Third, the diversity of markers, the interethnic variations and the significant influence of the environment can put into question the genetic labels the test can put into an individual. The counterargument to both these is this: conservative approaches of reacting proactively as if someone is at-risk as a slightest hint could be life-saving in the long-term. Besides, the present invention recommends population-specific validation before clinical recommendation. Because of these lifestylecentered intervention, the present invention can provide public health impact with minimal cost for patients. Long-term benefits include higher and longer quality of life and savings from costly maintenance and monitoring.

The variants of interest being associated with T2DM differs from previous disclosures and inventions if some aspects are considered. Prior art points to associations of some variants to T2DM, emphasizing on scientific publications and patent applications. SNP rs7119 is in exon 11 of HMG20A (high mobility group 20 A a gene responsible for regulating metabolismsecretion coupling genes as well as functional maturity of beta cells. It was associated with T2DM in a population composed of Chinese, Malay and Indian ethnicities (Sim et al., 2011). SNP rs708272, also known as the TaqlB polymorphism, is located in the first intron of CETP (cholesteryl ester transfer protein gene). CETP is a carrier enzyme responsible for transporting cholesterol esters and triglycerides between VLDL, LDL, and HDL; hence, a low level of Multiethnic association with T2DM was observed among Egyptians (El-Lebedy, 2018), Iranians (Rahimmi et al., 2011), and Han Chinese (Jiang et al., 2005). SNP rs7766070 is in intron 5 of CDKAL1 (cyclin-dependent kinase 5 regulatory subunit associated protein 1 (CDKRAP l)-like 7), a gene with unknown function and encodes a protein member of the methylthiotransferase family. A study reported association with T2DM among Lebanese subjects (Ghassibe-Sabbagh et al., 2014). Moreover, a systematic review involving multiethnic populations has demonstrated its significant association with T2DM (Cook et al., 2016). SNP rs2383208 is found llkb downstream from CDKN2B-AS1 in the CDKN2B-CDKN2A gene cluster of chromosome 9, a significant locus for genetic susceptibility to cardiovascular disease, T2DM and cancer. It was reported to be associated with T2DM among Japanese subjects (Takeuchi et al., 2009), Greeks (Christodoulou et al., 2019), and is a large study involving diverse ethnicities (European American, African American, Hispanic, East Asian, American Indian, and Native Hawaiian ancestry) (Haiman et al., 2012). SNP rs391300 is an intronic variant of the candidate gene SRR (serine racemase), which codes for a protein responsible for dehydratase activity that catalyzes the synthesis of D-serine (a glutamate co-agonist) from L- serine at NMDA (N-methyl-D-aspartate) receptors (Weizmann Institute of Science, 2015). The variant was associated with T2DM among Han Chinese (Tsai et. al., 2010). SNP rs659366 is an upstream variant of UCP2 (uncoupling protein 2), which codes for a mitochondrial transporter protein that uncouples oxidative phosphorylation from ATP synthesis by creating proton leaks across the inner mitochondrial membrane, dissipating energy in the form of heat. Association was observed among Danish subjects (Andersen et al., 2013), Indians (Vimaleswaran et al., 2011), and Turkish populations (Gozel and Dakilic, 2022). Interethnic differences may be evident as a meta-analysis found a lack of association of the UCP2 variant among Europeans, but, a statistically significant on populations of Asian descent (Xu et al., 2011). However, the effect was observed among Italians (Bullota et al., 2005).

However, several aspects may remain novel. First, variants such as SERPINF1 rsl2150053 and TMEFF2 rsl0497721have no known prior art as to the inventors and can be considered as novel on their association with T2DM. SNP rsl2150053 is a 2kb upstream variant of SERPINF1 (serpin family F member 7), a gene that encodes a neurotrophic protein that induces neuronal differentiation and serves as a potent inhibitor of angiogenesis. SNP rsl0497721 is an intronic variant of TMEFF2 (transmembrane protein with EGF-like and two follistatin-like domains 2), which encodes a transmembrane protein member of the tomoregulin family that functions both as an oncogene and tumor suppressor. As a consequence, the use of sets and panels in relation to the novel variants should be novel as well. Second, interethnic variability is observed in genotype-phenotype associations, especially among complex conditions such as T2DM. As there are no known similar tests among Filipinos, it is assumed that at least the proposed invention is novel with respect to this population.

This invention offers several advantages. Compared with traditional T2DM test, the present invention can test one’s susceptibility to T2DM despite the condition not yet occurring. This is important in fulfilling the its prognostic function. Compared with other T2DM genetic markers, these set of markers were derived from a specific population - the Filipinos - that provide them with a unique niche. Nevertheless, the utility of this invention in its application to other populations is still possible upon proper verification. Third, compared with the dynamic gene expression markers (RNA, proteins and methylation), as well as metabolite markers, the static genomic marker offers better prognostic significance especially among complex long-term diseases. Lastly, such genetic linkages strong imply causative possibilities that can therapeutically targeted in the future.

OBJECTIVE OF THE INVENTION

The object of this invention is to create a means of detecting susceptibility to type 2 diabetes mellitus, mainly applicable to nondiabetics, so they can be informed of such that can lead to lifestyle changes (such as dietary modifications and weight monitoring) to prevent or delay significantly the occurrence of T2DM.

SUMMARY OF THE INVENTION

The present invention relates to a means for determining susceptibility to type 2 diabetes mellitus by detecting the presence of one or a set of genetic markers from human biological samples comprising the following steps: (1) hybridizing DNA segments to an assay molecule, (2) detecting the presence of risk alleles, and (3) providing a prognosis based on the presence of risk alleles, wherein additional specific genetic markers are selected from: rsl2150053 and rsl0497721. Variations in the abovementioned variants have been associated with risk for T2DM among human participants. Using a case-control design, the variants confer odd ratios of 2 or more in T2DM cases. In addition, it is feasible to make a panel for the remaining significant SNPs for the Filipinos: rs7119, rs7766070, rs708272, rs2383208, rs391300, and rs659366. The invention is mainly a method for determining susceptibility to T2DM by detecting the presence of one or a set of genetic markers from human biological samples comprising the following steps: (1) hybridizing DNA segments to an assay molecule, (2) detecting the presence of risk alleles of the loci, and (3) providing a prognosis based on the presence of risk alleles, wherein the specific genetic markers are selected from the above markers.

In addition, any derivable platform for testing the variants is also implied. These platforms will include sequencing platform, blots and lateral flow assays and microarrays. With or without polymerase chain reaction procedures.

Because DNA is static, despite the DNA being of blood in origin, the DNA can come from other biological samples, such as saliva, buccal swab and mucosal cells, urine, solid tissue and other human cells.

For better understanding of the present invention and to show how the same may be performed, a preferred embodiment thereof will now be described, by way of non-limiting example only.

DETAILED DESCRIPTION OF THE INVENTION

The detailed description of the technical features of the present invention of a method involving a molecular marker for early detection for detecting susceptibility to T2DM will be explained in more detail with reference to a preferred embodiment thereof as shown in the accompanying figures and tables. The tables are, however, for illustration only and should not be used to limit the scope of the present invention.

A preferred embodiment should be a means for determining susceptibility to T2DM by detecting the presence of one or a set of genetic markers from human biological samples comprising the following steps: (1) hybridizing DNA segments to an assay molecule, (2) detecting the presence of risk alleles, and (3) providing a prognosis based on the presence of risk alleles, wherein additional specific genetic markers are selected from: rsl2150053 and rs 10497721.

The method can then be applied to manufacture a testing kit for the prognostication of T2DM. The hybridizing molecule is necessary for the testing of the variants, as the binding is necessary for either the eventual generation of the signal of the specific variants. As the detection requires the nucleic acid sequence in and flanking the variants, the sequences flanking 500 bp up- and downstream of the variants are provided and designated as SEQ ID l to SEQ ID 8 for rs7119, rs7766070, rs708272, rsl2150053 rs2383208, rs391300, rs659366 and rsl0497721, correspondingly (See Sequence List). Note that primers can be obtained from flanking regions and probes in the variant site.

Regardless of the platform, the prognostic kit should comprise of a receptacle for the placement of biological sample, a system involving hybridization of the nucleic acid with the variant sequence, and a detector system to detect the variant sequence. The biological sample is preferably blood or its derivatives. The test molecule could be in the form of DNA, RNA, cDNA, or cRNA. Note that the probes used could be used for hybridization assay as capture molecules or as reporter molecules. For amplification-based assay, the hybridizing molecules are primers. However, it should be obvious to the person skilled in the art that any unique primer or probe obtained from the gene sequence and its variants, as well as its RNA and its variants could be derived with the proper parameter.

Regardless of platform, the possibility of having a multi -marker panel is feasible, with the most obvious being other SNPs associated with T2DM in this population: rs7119, rs7766070, rs708272, rs2383208, rs391300, and rs659366.

PREFERRED EMBODIMENT

The platform or technique of preference is targeted sequencing. This can be performed using, but not limited to, capillary sequencing or single strand sequencing. These are chosen because of their availability, less cost and even portability. To summarize, the essential steps for the methods comprise: obtaining a biological sample from a patient, extracting the nucleic acid from cells, and perform the detecting assay which is essentially obtaining a signal that is indicative of the presence of the sequences of interest.

It should be noted that sequencing technologies have inherent variability among its different assays, differing, for example, from the amount of RNA being used to the conditions used for amplification, to the detection systems, and even to the standards being compared with, among others. Thus, albeit sequencing technologies are being cited, only an example is further presented to provide an enabling disclosure.

The initial step involves taking a biological sample from a patient. Preferably blood, the sample is taken using a hollowed needle from an intravenous source, although any other blood source can be used. The sample is preferably whole blood, although fractions containing nucleated cells can be considered. The sample should be enough to generate at least 1 copy of DNA strand of interest, through an appropriate extraction method preferably by, but not limited to, organic extraction, silica-based extraction or magnetic particle-based extraction. This could be obtained typically from a blood sample of at least 200 uL, preferably 2 to 5 mL. A detailed example of DNA extraction is provided under the heading “DNA extraction from blood cells” under “Supporting Experimental Studies” below.

The extracted DNA can then be used for amplification by polymerase chain reaction. All preparations are preferably performed on ice. About 100 ug of DNA is most preferable, although. An amount of at least equivalent to 1 copy of the genome is at least preferable. Primers were designed to have at least one of the primer pair per segment of interest to flank the SNP of interest. The primers preferably should be about 20-30 base pairs in length, complementary to bases located at most 500 bases in both sides of the variant, with annealing temperatures that differ by at most 1°C. However, in general the primers can be any pair that can attach to both sides of the variants with differing temperatures. The primer concentration may range from 10-30 uM, but may vary across assays. Upon addition of appropriate buffers and component (preferably buffered using Tris-HCL at pH 8-9; containing KCL and magnesium ions), DNA polymerase (preferably thermostable such as Taq or Pfu polymerase), the samples should preferably run in the following conditions: initial denaturation at 95°C to 100°C for 5-10 min, cyclic denaturation at 95°C to 100°C for 30 sec, annealing at at 54°C to 62°C for 30 sec, elongation at 72°C for 30 sec, and 4°C onwards. Note that these conditions may differ depending on the kit used. Quality controls can be used to optimize the interpretation of the assay, such as positive and negative template controls.

Most preferably, after amplification, targeted sequencing follows. This can be done either by capillary sequencing or single strand sequencing approaches using the approaches suggested from individual manufacturers.

ALTERNATIVE EMBODIMENTS

In addition to targeted sequencing approaches, other platforms can be possible. Genotyping is also possible using quantitative PCR or the use of electrophoresis using prior nested PCR fragment or fragmentation with DNA endonucleases with subsequent detection of fragments. Regardless of the methodology, the use of hybridizing molecule (probe or primer) is deemed necessary for the assays to work. Furthermore, genotyping using differential melt curves can also be done, mainly through a quantitative PCR procedure.

Also, probe-based assays such as some DNA microarrays or probes using DNA gels or blots can also be possible.

It is also possible to use a microarray platform or a higher throughput sequencing approach to determine the variants, although in a much higher cost.

Note that if the methodology involves an immunoassay (for example, lateral flow assays, Western blot, ELISA or dipstick) the hybridizing molecule is preferably an antibody that can detect epitopes derived from nucleic acids of interest.

UTILITY OF THE INVENTION

As the relationship of the genotypes to susceptibility to T2DM can be present across ethnicities, the utility of the present invention can be extended to non-Filipinos, provided that significant association can be established.

SUPPORTING EXPERIMENTAL STUDIES

The following details are presented in support of the preferred embodiment of the invention where the multiple variants had been associated with T2DM susceptibility.

METHODOLOGY

STUDY DESIGN

The experiment is a case-control study design comparing participants with type 2 diabetes mellitus (T2DM) and non-T2DM controls to determine genetic variants of associated with T2DM.

INCLUSION AND EXCLUSION CRITERIA

The inclusion criteria were: (1) Filipinos; (2) above the age of 18; (3) unrelated up to 3 ^rd degree of consanguinity, and (4) able to independently provide informed consent were invited to enroll as participants. The exclusion criteria were: (1) previously diagnosed with type 1 diabetes mellitus, (2) currently pregnant or lactating, (3) with active alcohol abuse or illicit drug use within the past three months, (4) with malignancy with active systemic disease, or (5) with malignancy that are disease-free for less than five (5) years.

DEFINITION OF CASES AND CONTROL

The T2DM cases were defined as those who satisfied the general criteria and the criteria for T2DM as defined by the American Diabetes Association (ADA, 2018), that is the presence of any of the following: fasting blood sugar (FBS) > 126 mg/dL (7.0 mmol/L) with fasting defined as without caloric intake for at least 8 hours; 2-hour plasma glucose > 200 mg/dL (11.1 mmol/L) during oral glucose tolerance test (2-hr OGTT), which is performed using a glucose load containing the equivalent of 75g anhydrous glucose dissolved in water (WHO-NMH- MND, 2013); glycosylated hemoglobin Ale (HbAlc) > 6.5% (48 mmol/L) , which is performed in a laboratory using methods certified by the National Glycohemoglobin Standardization Program (NGSP), standardized to the Diabetes Control and Complications Trial (DCCT) assay; or a random plasma glucose > 200 mg/dL (11.1 mmol/L) in the presence of classic symptoms of hyperglycemia or hyperglycemic crisis.

The non-diabetic controls were defined as participants who satisfied the general criteria and the following criteria for non-diabetics (ADA, 2018), that is in the presence of all the following: FBS < 100 mg/dL (5.6 mmol/L); 2-hr OGTT < 140 mg/dL; and HbAlc < 6.5%.

DNA EXTRACTION AND QUANTIFICATION

Blood samples collected were stored in EDTA tubes at 4°C, and DNA was extracted using the QiaAmp DNA Blood Mini Kit (QIAGEN GmbH) following the spin protocol for blood buffy coat specified in the manufacturer’s instruction manual. Briefly, the buffy coat was prepared by whole blood centrifugation at 3500 rpm for 10 minutes at room temperature, followed by extraction of the resulting intermediate layer. Afterwards, 200 pL of sample was transferred into a 1.5 mL micro-centrifuge tube containing proteinase K (20 pL). 200 pL of lysis buffer (Buffer AL) was added to the mixture before being incubated at 56°C for 10 minutes. 200 pL of ethanol (96100%) was then added to precipitate the DNA. The mixture was then vortexed and transferred to a spin column placed in a 2 mL collection tube, centrifuged at full speed (-14000 rpm; 20000 x g) for 1 minute to bind the DNA in the silica membrane. The flow-through was properly discarded. 500 pL of washing buffer (Buffer AW1) containing guanidine hydrochloride was added to the column to denature proteins, centrifuged at 8000 rpm (6000 x g) for 1 minute. The column was placed in a new 2 mL collection tube, as the DNA was washed with 500 pL of washing buffer (Buffer AW2) containing sodium azide and centrifuged at full speed for 3 minutes. DNA was eluted by placing the column in a new 1.5 mL tube, filling the column with 200 pL elution buffer (Buffer AE), allowing the column to stand for 5 minutes at room temperature and centrifuging at 8000 rpm for 1 minute.

The eluted DNA was quantified using the NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) at 260 nm and stored at -20°C until microarray genotyping. Only samples with A260/280 between 1.8 to 2 were considered viable for further testing. CUSTOMIZATION OF MICRO ARRAY CHIPS

A customized beadchip was designed using candidate SNPs from both coding and noncoding regions, including intergenic and intronic SNPs which have shown evidence of association with T2DM and its complications. Variants were selected from the following databases: PharmGKB database, National Human Genome Research Institute (NHGRI) GWAS Catalog, PubMED, and patent databases (e.g. Patentscope and Espacenet) where risk and protective odds ratios (OR) were provided. Although variants with crude ORs of > 2.0 or < 0.5 were preferentially selected from thousands of markers for clinical relevance, other SNPs with less established or smaller ORs were also included to assess frequency of these alleles among Filipinos. The variants were scored to determine the suitability to discriminate and estimate specificity.

GENOTYPING

Customized genotyping of candidate SNPs was performed using DNA microarray technology following the GoldenGate Genotyping (GGGT) and Illumina Infmium i Select assay protocols specified in their respective manufacturer’s manual. Screening for SNPs among genes clinically associated with type 2 diabetes mellitus and its complications were done by imaging beadchips on the HiScan system and utilizing the GenomeStudio v2.0 software.

QUALITY CONTROL

GenomeStudio v2.0 was used to evaluate the quality of sample data and identify or remove participants and SNPs with incomplete data. Only genotyping data with a call frequency > 95% from samples with a call rate > 95% were included in the study (Illumina, 2008). gPLINK v2.05.10 was used to further evaluate the completeness of participant and SNP data. Participants with missingness rate more than 5% (individual missingness test) were excluded from further analysis. SNPs with the following criteria in gPLINK were determined to have incomplete genotype data and were also excluded from further analysis: minor allele frequency (MAF) less than 1% (frequency test), genotype missingness rate more than 5% (genotypic missingness test), and significant Hardy-Weinberg disequilibrium among controls >0.001 (Hardy -Weinberg equilibrium test).

MERGING OF DATA (GGGT and Infinium iSelect)

Two different microarray protocols were used throughout the study period, Golden Gate genotyping (GGGT) and Illumina Infmium iSelect. This necessitated the use of GenomeStudio v2.0 and Stata/MP vl4.1 to consolidate a merged list of relevant SNPs. This strategy was done in accordance to the manufacturer’s manual of Illumina Infmium iSelect, using updated SNP rs IDs (Illumina, 2016). Three hundred fifty-one candidate SNPs were listed from merging and submitted to the quality control thresholds of the project.

DATA ANALYSES METHODS

The demographic and clinical data of case and control participants after matching were tabulated to present the comparability between cases and controls (expressed as no significant differences, p > 0.05). P values were calculated using Stata 14.0 (College Station, TX: StataCorp LP).

Two-sample Student’s t tests were done to calculate the p value for continuous data (i.e.: age, FBS, HbAlc, etc.), and conditional logistic regression was done to calculate the p values for categorical data (sex - percent female, hypertension - percent diagnosed, etc.) between cases and controls that reached the target sample size.

Determination of risk allele and risk allele frequency was performed through allelic association tests on gPLINK 2.050. Fisher-Irwin exact tests were performed on the different groups to initially assess for significant differences between alleles. Fisher-Irwin testing confirmed nominally significant allelic association at a p < 0.05. Correcting for multiple testing was done via Holm-Bonferroni adjustments, when possible (Giacalone et al, 2018). The crude odds ratios (OR) were used to infer the impact of an allele on phenotypic outcome. An OR > 1.0 denoted susceptibility (risk) and an OR < 1.0 denoted protection. If the OR was > 1.0, the minor allele was reported as the risk allele, and the minor allele frequency (MAF) was reported as risk allele frequency (RAF). If the OR was < 1.0, the major allele was reported as the risk allele. The reciprocal of the OR (1 OR) was reported to be the risk ROR, and the percent difference of the MAF was reported as the RAF.

GENOTYPIC ASSOCIATION TESTING & DETERMINATION OF GENETIC EFFECT

Complex, multi -factorial traits and diseases often follow an additive trend, where the presence of more copies of the risk allele confers higher risk of the complication or associated disease. Other models include the recessive model (where the presence of two of risk alleles are required to take full gene action) and the dominant model (where the presence of only one risk allele is require to take full gene action) (Talluri et al., 2014). These models are inferred based on the distribution of the case and control genotypes among participants. Fisher-Irwin exact tests of association were performed to initially determine the best possible mode of genetic effect at a nominally significant p value < 0.05. Correcting for multiple testing was done in the same fashion as in allelic association testing, via Holm- Bonferroni adjustments, if possible, or via p values based on PCER, EWER or FDR to minimize type 1 errors where Holm-Bonferroni adjustments proved too strict (Gardner, 2017a, 2017b; Giacalone et al., 2018).

LOGISTIC REGRESSION ANALYSIS & DETERMINATION OF THE SNPs OF INTEREST

Stata/MP vl4.0 was used to do conditional logistic regression analysis. Statistically significant SNPs on conditional logistic regression based on their p values were discussed in detail.

ETHICAL CONSIDERATIONS

The University of the Philippines Manila Research Ethics Board (UPMREB) approved the study protocol, case report forms and the informed consent forms prior to the start of the study (2012-0185-NIH).

RESULTS

Of the two hundred ten (210) participants enrolled in the subgroup, only 184 were included after data quality control of which 66 were cases with T2DM and 118 controls (without T2DM) (Table 1). The cases have had T2DM for an average of 16 years, with mean FBS level of 161.82 mg/dL and mean HbAlc of 8.77%. The group of T2DM cases has significantly higher prevalence of hypertension and metabolic syndrome than controls (p<0.001), which is expected as the prevalence of hypertension in T2DM cases has been previously reported to be between 50% and 75% (Colosia et al., 2012). While the mean triglyceride and HDL-C levels were as expected, the LDL-C level is lower among cases than controls. This may be due to the use of anti-hyperlipidemic medications among diabetic participants; a larger percentage of cases were on statins (51.52%) compared with controls (5%) (p <0.001).

Table 1. Clinical profile of participants in the type 2 diabetes mellitus sub-study

With T2DM Without T2DM

Clinical Features ( .n = 66) ( ,n = „ 1„18„) n-value

Age (mean years ± SD) 55.64 (10.61) 54.30 (11.41) ns

Sex (% males) 36.36 37.29 ns

Hypertension (%) 74.24 25.86 < 0.001 Obese (%) 26.15 22.34 ns

Metabolic syndrome (%) 70.77 21.74 < 0.001

Anthropometries

BMI (mean kg/m ² ± SD) 25.62 (4.47) 25.83 (4.80) ns

Waist-hip ratio (mean ± SD) 0.95 (0.06) 0.93 (0.06) ns

Laboratory tests

Triglycerides (mean mg/dL ± SD) 139.71 (103.93) 121.21 (92.62) ns

LDL-c (mean mg/dL ± SD) 120.11 (43.44) 136.01 (39.36) 0.02

HDL-c (mean mg/dL ± SD) 47.20 (10.80) 51.80 (14.05) 0.007

After correcting for multiple testing (Bonferroni-adjusted p-value = 2.16e-04), there are eight variants found to have significant association with type 2 diabetes mellitus (Table 2). The genotype distribution of the variants in the control group follows Hardy -Weinberg equilibrium. Results of the allelic association tests of the eight variants and other information are summarized in Table 2.

Table 2. Variants significantly associated with T2DM in the study

, , . , Implicated _ Crude OR

Vanant Function Genotypes p-value

Gene (95% CI) ^r rs7119 HMG20A 3’ UTR / GG vs AG and AA 31.06 0.001 noncoding transcript (G- recessive) (4.18, 230.52) exon / downstream gene / regulatory region variant rs7766070 CDKAL1 Intron / regulatory AA and AC vs CC (A- 20.03 < 0.001 region variant dominant) (6.15, 65.26) rs708272 CETP Intron / regulator, CC vs TC and TT (C- 12.80 < 0.001 region variant recessive) (3.87, 42.34) rsl2150053 SERPINF1 Upstream gene CC and TC vs TT 9.94 < 0.001 variant (C- dominant) (3.43, 28.82) rs2383208 CDKN2B- Downstream gene GG and AG vs AA (G- 9.18 < 0.001

AS1 variant dominant) (3.21, 26.29) rs391300 SRR Intron / upstream AG vs AA 2.00 ns gene variant (0.42, 9.56)

GG vs AA 8.69 0.007

(G- additive) (1.83, 41.38) rs659366 UCP2 Upstream gene / CC vs TC and TT (C- 7.92 < 0.001 non-coding recessive) (2.78, 22.61) transcript exon / regulatory region / TF binding site variant rsl0497721 TMEFF2 Intron variant AA and AC vs CC (A- 3.33 0.001 dominant) (L59, 6.95)

Abbrev: HMG20A, high mobility group 20 A; CDKAL1, CDK5 regulatory subunit associated protein 1 like 1; CETP, cholesteryl ester transfer protein; SERPINF1, serpin family F member 1; CDKN2B-AS1, cyclin dependent kinase inhibitor 2B- antisense RNA 1; SRR, serine racemase; UCP2, uncoupling protein 2; TMEFF2, transmembrane protein with EGF-like and two follistatin-like domains 2. The p-value is significant at p<0.05 after conditional logistic regression for matched samples.

SUMMARY AND CONCLUSION

The study indicates genomic variants associated with the presence of T2DM in human, particularly in Filipinos. As variants are static from birth, it preempts the eventual occurrence of mature onset T2DM in the early part of life. Thus, the markers can be used as markers for susceptibility that may identify individuals who can mainly benefit from preemptive measures to prevent T2DM and its complications.

CITATION LIST

Non-Patent Literature

Andersen G, Dalgaard LT, Justesen JM, Anthonsen S, Nielsen T, Thorner LW, Witte D, Jorgensen T, Clausen JO, Lauritzen T, Holmkvist J, Hansen T, Pedersen O. The frequent UCP2 -866G>A polymorphism protects against insulin resistance and is associated with obesity: a study of obesity and related metabolic traits among 17636 Danes. Int JObes (Lond). 2013 Feb;37(2):175-81. doi: 10.1038/ijo.2012.22.

Bhatta, P., Bermano, G., Williams, H. C., & Knott, R. M. (2020). Meta-analysis demonstrates Gly482Ser variant of PPARGC1A is associated with components of metabolic syndrome within Asian populations. Genomics, 112(2), 1795-1803. https://doi.Org/10.1016/j.ygeno.2019.10.011

Bulotta A, Ludovico O, Coco A, Di Paola R, Quattrone A, Carella M, Pellegrini F, Prudente S, Trischitta V. The common -866G/A polymorphism in the promoter region of the UCP-2 gene is associated with reduced risk of type 2 diabetes in Caucasians from Italy. J Clin Endocrinol Metab. 2005 Feb;90(2): 1176-80. doi: 10.1210/jc.2004-1072.

Christodoulou MI, Avgeris M, Kokkinopoulou I, Maratou E, Mitrou P, Kontos CK, Pappas E, Boutati E, Scorilas A, Fragoulis EG. Blood-based analysis of type-2 diabetes mellitus susceptibility genes identifies specific transcript variants with deregulated expression and association with disease risk. Sci Rep. 2019 Feb 6;9(1): 1512. doi: 10.1038/s41598-018- 37856-1.

Cook JP, Morris AP. Multi-ethnic genome-wide association study identifies novel locus for type 2 diabetes susceptibility. Eur J Hum Genet. 2016 Aug;24(8): 1175-80. doi: 10.1038/ejhg.2016.17.

El-Lebedy D. Interaction between endothelial nitric oxide synthase rsl799983, cholesteryl ester-transfer protein rs708272 and angiopoietin-like protein 8 rs2278426 gene variants highly elevates the risk of type 2 diabetes mellitus and cardiovascular disease. Cardiovasc DiabetoL 2018 Jul 4;17(1):97. doi: 10.1186/sl2933-018-0742-8.

Ghassibe-Sabbagh M, Haber M, Salloum AK, Al-Sarraj Y, Akle Y, Hirbli K, Romanos J, Mouzaya F, Gauguier D, Platt DE, El-Shanti H, Zalloua PA. T2DM GWAS in the Lebanese population confirms the role of TCF7L2 and CDKAL1 in disease susceptibility. Sci Rep. 2014 Dec 8;4:7351. doi: 10.1038/srep07351.

Gozel N, Dalkilic S. UCP2 866 G/A gene (rs659366) polymorphism associated with diabetes type 2 in Turkish population. ProgrNutr [Internet], 2018 Feb. 1 [cited 2022 May l];19(4):430- 5.

Haiman CA, Fesinmeyer MD, Spencer KL, Buzkova P, Voruganti VS, Wan P, Haessler J, Franceschini N, Monroe KR, Howard BV, Jackson RD, Florez JC, Kolonel LN, Buyske S, Goodloe RJ, Liu S, Manson JE, Meigs JB, Waters K, Mukamal KJ, Pendergrass SA, Shrader P, Wilkens LR, Hindorff LA, Ambite JL, North KE, Peters U, Crawford DC, Le Marchand L, Pankow JS. Consistent directions of effect for established type 2 diabetes risk variants across populations: the population architecture using Genomics and Epidemiology (PAGE) Consortium. Diabetes. 2012 Jun; 61(6): 1642-7. doi: 10.2337/dbl 1-1296.

Jiang T, Song XX, Zhang M, Qi WH, Zhang XW. [Association between insulin resistance and cholesteryl ester transfer protein gene polymorphism in type 2 diabetes mellitus], Zhonghua Yi Xue Yi Chuan Xue Za Zhi. 2005 Jun;22(3):298-301.

McCarthy MI, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009 Apr;9(2): 164-71. doi: 10.1007/sl 1892-009-0027-4.

Rahimi Z, Nourozi-Rad R, Vaisi-Raygani A, Saidi MR, Rahimi Z, Ahmadi R, Yarani R, Hamzehee K, Parsian A. Association between cholesteryl ester transfer protein TaqIB variants and risk of coronary artery disease and diabetes mellitus in the population of western Iran. Genet Test Mol Biomarkers. 2011 Nov;15(l l):813-9. doi: 10.1089/gtmb.2011.0037.

Sim X, Ong RT, Suo C, Tay WT, Liu J, Ng DP, Boehnke M, Chia KS, Wong TY, Seielstad M, Teo YY, Tai ES. Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia. PLoS Genet. 2011 Apr;7(4):el001363. doi:

10.1371/journal.pgen.l001363.

Takeuchi F, Serizawa M, Yamamoto K, Fujisawa T, Nakashima E, Ohnaka K, Ikegami H, Sugiyama T, Katsuya T, Miyagishi M, Nakashima N, Nawata H, Nakamura J, Kono S, Takayanagi R, Kato N. Confirmation of multiple risk Loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes. 2009 Jul;58(7): 1690-9. doi: 10.2337/db08-1494.

Tsai FJ, Yang CF, Chen CC, Chuang LM, Lu CH, Chang CT, Wang TY, Chen RH, Shin CF, Liu YM, Chang CC, Chen P, Chen CH, Fann CS, Chen YT, Wu JY. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet. 2010 Feb 19;6(2):el000847. doi: 10.1371/journal.pgen.1000847.

Vimaleswaran KS, Radha V, Ghosh S, Majumder PP, Sathyanarayana Rao MR, Mohan V. Uncoupling protein 2 and 3 gene polymorphisms and their association with type 2 diabetes in asian Indians. Diabetes Technol Ther. 2011 Jan; 13(1): 19-25. doi: 10.1089/dia.2010.0091.

Xu K, Zhang M, Cui D, Fu Y, Qian L, Gu R, Wang M, Shen C, Yu R, Yang T. UCP2 - 866G/A and Ala55Val, and UCP3 -55C/T polymorphisms in association with type 2 diabetes susceptibility: a meta-analysis study. Diabetologia. 2011 Sep;54(9):2315-24. doi: 10.1007/s00125-011-2245-y. Xu L, Chen S, Zhan L. Association of uncoupling protein-2 -866G/A and Ala55Val polymorphisms with susceptibility to type 2 diabetes mellitus: A meta-analysis of case-control studies. Medicine (Baltimore). 2021 Feb 12;100(6):e24464. doi: 10.1097/MD.0000000000024464. Patent Literature

KR1 020090035068. Lee IK. SNP used as a marker, capable of diagnosing susceptibility of a second type diabetes mellitus. 09.04.2009.

KR1 020140102933. Lee IK. Marker composition for diagnosing diabetes. 25.08.2014.

W02006063703A2 (A3,A8). La Roche H. Single nucleotide polymorphism (SNP) associated to type II diabetes. 2006-06-22.

WO2007128884. Salonen JT, Hyppbnen J, Kaikkonen J, Pirskanen M, Uimari P, Aalto JM. Novel genes and markers in type 2 diabetes and obesity. 15.11.2007.

W02008058332. Collier GR, Walder KR. Jowett JBM, Shields KA, curran JE, Blangero J, Moses EK. Diagnostic protocols for diabetes. 22.05.2008. W02020232034. Yang WC, Yang MT. LAMA2, PLXDC2 r MLL4 as novel biomarkers for prediabetes and diabetes.19.11.2020.