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Current Pharmacogenomics and Personalized Medicine (Formerly Current Pharmacogenomics) - Current Issue
Volume 21, Issue 2, 2024
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Genomic Medicine: Perspective of the Challenges for the Implementation of Preventive, Predictive, and Personalized Medicine in Latin America
Genomic information plays an essential role in personalized medicine, with the main objective of determining risk and predisposition to disease, as well as guiding diagnosis, selection, and prioritization of therapeutic options, and even predicting prognosis. Research in the second half of the 20th century allowed genomics to move from the laboratory to clinical practice. The Human Genome Project showed the structure of the genome, the genes, and several of their regulatory pathways, which allowed obtaining exact knowledge about the molecular origin of a growing number of diseases and the development of next-generation sequencing technologies. In the second decade of the 21st century, the decrease in testing costs has allowed genomic medicine to begin to be applied in hospital institutions and outpatient services with a positive impact on public health. However, it has been evidenced that these potential benefits have not been experienced equitably throughout the world. This commentary explores the main challenges and obstacles to the implementation of genomic medicine services in order to expand their use as part of clinical practice in the Latin American context. Finally, six main barriers have been identified: i) high costs and poor access, ii) lack of trained personnel in the genomic field, iii) negative personal and social beliefs, iv) lack of representation of Latin American populations in genomic databases, v) scarce evidence of impact on clinical practice, and vi) lack of understanding of genomic test results by patients and clinicians.
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Navigating the Future of PCOS Treatment: The Precision Medicine Paradigm
Polycystic Ovary Syndrome (PCOS) is a condition affecting women of reproductive age, characterized by a heterogeneous array of symptoms. This study aims to examine the role of Precision and Personalized Medicine (PPM) in managing PCOS, given the diverse manifestations of the disease and any genetic factors involved. In this review, we have analyzed the existing literature on the heterogeneity in PCOS symptoms, efforts to acquire PPM data for the characterization of molecular changes in PCOS, and the impact of advances in artificial intelligence on precision medicine. PCOS symptoms present differently in each individual, making traditional therapies ineffective. By tailoring treatment to each individual's genetic and molecular profile, PPM offers a promising approach to address the complex nature of PCOS. Understanding PCOS molecular underpinnings requires continuous acquisition of PPM data. Advances in artificial intelligence have greatly enhanced precision medicine's potential applications. Precision medicine could become a standard component of PCOS care, similar to its application in treating serious conditions like cancer and heart disease, due to its ability to address the condition's complexity through individualized treatment approaches.
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Epidemiology and Genetic Architecture of Type 2 Diabetes Mellitus in Geographically Different Indian Populations: A Review
Authors: Jyotsna Singh, Vijay Tripathi, Rajiv Kant and Jonathan A. LalType 2 Diabetes Mellitus (T2DM) has been a severe public health issue worldwide for many years. The primary cause and risk factor of T2DM is hereditary and complicated interaction between epigenetics. Identification and understanding of genetic markers may help to detect, prevent, and manage T2DM. This review examined the effect of single-gene and gene-gene interactions for predicting diabetes mellitus. Based on the literature survey, common and unique Single Nucleotide Polymorphisms (SNPs) and genes were explored in the Indian Populations, including PPARG, TCF7L2, KCNJ11, CDKN2A, IGF2BP2, SLC30A8, HHEX and CDKAL1. Identifying common and specific markers may help in risk prediction and early detection of T2DM. Future research and Genome-wide association studies are also required to predict the gene-gene interaction, generate large data sets for removing non-representative groups, and focus only on specific marker-associated traits.
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Association of Single Nucleotide Polymorphism in OCT1 and OCT3 Genes with the Efficacy of Metformin Response in North Indian Type 2 Diabetes Mellitus Patients
Authors: Saliha Rizvi, Syed Tasleem Raza and Farzana MahdiIntroductionVariability in the effectiveness of metformin treatment among individuals with type 2 diabetes mellitus (T2DM) has been linked to various genetic factors. Understanding the genetic mechanisms underlying the action of metformin can greatly aid the personalized management of T2DM. Our investigation aimed to explore the impact of genetic variations in the organic cation transporters (OCT1 and OCT3) genes on the efficacy of metformin therapy in T2DM individuals from North India.
MethodsThis observational cross-sectional study assessed the influence of OCT1 (rs628031) and OCT3 (rs2292334) polymorphisms on metformin response in T2DM patients. Metformin response was determined based on HbA1c levels, dividing patients (n = 177) into two categories: responders (HbA1C<7%; n = 127) and non-responders (HbA1C≥7%; n = 50). Responders were further classified as T2DM patients receiving either monotherapy (n = 55) or combination therapy (n = 72). Genotyping was conducted using the PCR-RFLP method.
ResultsNo significant association was observed between OCT1 (rs628031) polymorphism and metformin response in T2DM patients. However, a notable association was found between OCT3 (rs2292334) polymorphism and metformin response. Carriers of the AA genotype exhibited enhanced efficacy of metformin in both monotherapy (OR (CI)= 0.29(0.11-0.72), p=0.007) and combination therapy (OR (CI)= 0.41(0.16-1.0), p=0.047). Additionally, the A allele was more prevalent in responders (OR (CI)= 0.48(0.28-0.84), p=0.010), while the G allele was associated with reduced efficacy of metformin in T2DM patients (OR (CI)= 2.07(1.19-3.61), p=0.010).
ConclusionGenotyping of OCT3 (rs2292334) may serve as a valuable tool in predicting the response to metformin in T2DM patients.
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Pharmacogenetics of Metformin Monotherapy: GSTM1/T1 Polymorphisms and T2DM Risk
Authors: Ashwin Kumar Shukla, Komal Awasthi, Kauser Usman and Monisha BanerjeeIntroductionMetformin is a key treatment for type 2 diabetes, often linked to oxidative stress and genetic factors like GSTM1 and GSTT1 variations.
MethodsWe studied 150 subjects, examining how their deletion polymorphisms in these genes correlate with Met treatment response. Those with GSTM1/T1 deletions (-/-) had a higher T2DM risk (2.71-fold, P=0.005).
ResultsMet responders with GSTM1(16bp) deletions had lower glucose levels compared to non-responders (P<0.0001), and similar trends were observed with GSTT1(54bp) deletions. Responders with both deletions also managed lipids better (P=0.0256; P=0.0151). Non-responders with GSTM1/T1 null genotypes had better HDL management (P=0.007).
ConclusionThese findings suggested that GSTM1 deletion could predict T2DM susceptibility and Met response.
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Estrogen Receptor-αrs9340799 Polymorphism Influences Bone Mineral Density in Women Over 60 Years of Age and Women Who are Postmenopausal for More than 10 Years
BackgroundOsteoporosis is a multifactorial disorder where genetic and environmental factors contribute to changes in bone mineral density. Several genetic polymorphisms are associated with low bone mineral density and osteoporosis risk, including estrogen receptor-α rs2234693 and rs9340799 single nucleotide polymorphisms.
ObjectiveTo determine the allele frequencies of these polymorphisms among postmenopausal Jordanian women and to assess their association with low bone mineral density and osteoporosis among studied subjects.
MethodsThis cross-sectional study enrolled 450 postmenopausal Jordanian women having dual-energy X-ray absorptiometry scans at the National Center for Diabetes, Endocrinology, and Genetics. The study protocol was approved by this center “Institutional Review Board.” The estrogen receptor-α gene sequence containing rs2234693 and rs9340799 polymorphisms was identified by polymerase chain reaction, followed by restriction fragment length polymorphism.
ResultsThe wild-type allele frequencies of rs2234693 (T) and rs9340799 (A) were 54% and 59%, respectively. The rs9340799 GG genotype was significantly associated with lower femoral neck T-scores in women who were postmenopausal for more than 10 years (p = 0.023) and was significantly associated with lower lumbar spine (p = 0.033) and femoral neck (p = 0.002) T-scores in women older than 60 years of age. However, there was no association between rs2234693, rs9340799, or their haplotypes with osteoporosis or bone mineral density T-score values. The two polymorphisms were in Hardy-Weinberg equilibrium and exhibited strong but incomplete linkage disequilibrium.
ConclusionThe data suggest that rs9340799 polymorphism may render some women more susceptible to osteoporosis than others.
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