Next Article in Journal
Population Characteristics of the Spectrum and Frequencies of CFTR Gene Mutations in Patients with Cystic Fibrosis from the Republic of Bashkortostan (Russia)
Next Article in Special Issue
Effects of Gene–Lifestyle Interaction on Obesity Among Students
Previous Article in Journal
Combinatorial Anti-Cancer Effect of Polypurine Reverse Hoogsteen Hairpins against KRAS and MYC Targeting in Prostate and Pancreatic Cancer Cell Lines
Previous Article in Special Issue
Effects of ACLY Inhibition on Body Weight Distribution: A Drug Target Mendelian Randomization Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 15
Issue 10
10.3390/genes15101334
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Genetic Variants of Obesity in Malaysia: A Scoping Review

by
Siti Sarah Hamzah
*,
Liyana Ahmad Zamri
,
Norhashimah Abu Seman
and
Nur Azlin Zainal Abidin
Endocrine and Metabolic Unit, Nutrition, Metabolic & Cardiovascular Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Setia Alam 40170, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Genes 2024, 15(10), 1334; https://doi.org/10.3390/genes15101334
Submission received: 4 July 2024 / Revised: 11 September 2024 / Accepted: 18 September 2024 / Published: 17 October 2024
(This article belongs to the Special Issue Genetics and Epigenetics of Metabolic Diseases)
Browse Figures
Versions Notes

Abstract

:
Background: Obesity is a pressing public health issue in Malaysia, involving not only excess weight but also complex metabolic and physiological changes. Addressing these complexities requires comprehensive strategies, including understanding the population-level differences in obesity susceptibility. This review aims to compile the genetic variants studied among Malaysians and emphasize their implications for obesity risk. Methods: Relevant articles published up to March 2024 were extracted from the Scopus, PubMed, and ScienceDirect databases. The review process was conducted in accordance with the PRISMA-ScR guidelines. From an initial pool of 579 articles, 35 of these were selected for the final review. Results: The identified gene variants, including LEPR (K656N), LEP (G2548A—Indian only), ADIPOQ (rs17366568), UCP2 (45bp-I/D), ADRB3 (rs4994), MC3R (rs3827103), PPARγ (pro12Ala—Malay only), IL1RA (intron 2 VNTR), NFKB1 (rs28362491), and FADS1 (rs174547—Indian only), showed significant associations with obesity as measured by the respective studies. Conclusions: Overall, more intensive genetic research is needed, starting with population-based profiling of genetic data on obesity, including among children. Sociocultural contexts and environmental factors influence variations in genetic elements, highlighting the need for targeted interventions to mitigate the impacts of obesity in the population.

1. Introduction

The World Health Organization (WHO) identifies overweight and obesity as the fifth leading risk for global mortality, with elevated BMI contributing to diseases like cardiovascular diseases, diabetes, and certain types of cancer [1,2]. Obesity, once considered a problem predominantly in high-income countries, has now become an increasing public health issue worldwide.
In Malaysia, the trends in overweight, obesity, and abdominal obesity have continued to rise, as evidenced by the National Health and Morbidity Survey (NHMS) in 2011 (29.4%, 15.1%, and 45.4%) and 2015 (30%, 17.7%, and 48.6%) [3,4]. Currently, obesity remains a major health challenge in the country, with no sign of abating. The latest NHMS report reveals that over half of the adult population is affected by weight issues, with 54.4% being overweight or obese and 54.5% having abdominal obesity [5].
Recently, the Health Research Priorities for the 12th Malaysia Plan (12MP-HRP) 2021–2025 have identified 15 priority research areas related to overweight and obesity, with one of them being the need to investigate the role of genetic factors in the development of overweight and obesity [6]. This shows that studies in this area and scientific data on this topic are still scarce in Malaysia.
Obesity is widely recognized as a polygenetic disease influenced by interactions between genes and the environment, including factors such as diet, lack of physical exercise, microbiome, and chemical contaminants, all of which can alter gene expression [7]. In addition to environmental, social, and economic factors, phenotypic heterogeneity exists among overweight and obese individuals due to interactions at the molecular, genetic, and cellular levels [8]. Another form of obesity also exists, known as monogenic obesity, in which mutations in specific genes cause early-onset of obesity, often from infancy or early childhood. Recently, gene discovery studies have found common fundamental biology between polygenic and monogenic obesity, with central nervous system (CNS) and neuronal pathways that control food intake being major drivers of body weight in both forms [9]. Early evidence also shows that individuals’ polygenic susceptibility to obesity may partly influence the expression of mutations that cause monogenic obesity [10]. Therefore, it is crucial to identify population-based genetic variants associated with obesity to enhance understanding of their predisposition for improved diagnosis and management.
Given these considerations, this scoping review aims to compile and present genetic research on obesity conducted in Malaysia up to March 2024. To date, no review has specifically focused on this aspect within Malaysia. This review consolidates the studied genetic variants and their associations with obesity and offers recommendations for future research based on the limitations of current studies. Ultimately, this review seeks to effectively guide future obesity research in Malaysia and address the gaps highlighted in the 12th Malaysia Plan.

2. Methods

2.1. Protocol

The framework for analyzing this scoping review was conducted following the methodology outlined by Arksey and O’Malley [11]. The scoping review protocol was prospectively registered with the Open Science Framework (https://osf.io/qyzka, accessed on 11 July 2024). Guidelines for conducting a scoping review were adhered to [12], along with the PRISMA-ScR (Preferred Reporting Items for Systematic Review and Meta-analyses extension for Scoping Reviews) guidelines for reporting [13].

2.2. Identification of Relevant Studies

A systematic literature search was performed using a predefined search strategy across Scopus, PubMed, and ScienceDirect databases. This search was conducted by at least two independent researchers. The search included articles published from the earliest available date to March 2024, as research on this topic was limited in Malaysia. English language terms were used and review articles including scoping, systematic, narrative, meta-synthesis, and meta-analysis were omitted. Boolean operators (AND, OR) were utilized to combine words. The identified records were exported to EndNote and independently screened for inclusion by four reviewers. Disagreements were addressed through discussion until a consensus was achieved.

2.3. Inclusion Criteria

The inclusion criteria were as follows:
  • Peer-reviewed articles, including original research and clinical studies;
  • Human-based research;
  • Study conducted among Malaysian (Malay, Chinese, Indian, indigenous people, bumiputra Sabah and Sarawak);
  • English and Malay language;
  • Any articles published until March 2024.
The exclusion criteria were as follows:
  • Books, book chapters, and book reviews;
  • Review articles (systematic, meta-analysis, meta-synthesis, scoping, narrative);
  • Animal studies;
  • Non-Malaysian population;
  • Perspective, opinion, and commentary in peer-reviewed journal;
  • Non-genetics or obesity studies.

2.4. Data Extraction

Data extraction from the included studies was conducted using a standardized data extraction sheet in Microsoft Excel 2021 (Microsoft Corporation, Redmond, WA, USA). The extraction process included recording the year of publication, scientific study title, authors, objectives, sex distribution, sample size, ethnicity, comparison group details, and findings.

3. Results

3.1. Study Characteristics

Based on the keyword searches, 579 potentially eligible records were identified, out of which 153 articles were excluded due to redundancy. Following abstract screening, 378 articles were further excluded, leaving 47 articles for full-text screening. Subsequently, 13 studies were excluded during the full-text review, resulting in the inclusion of 34 studies for data extraction and analysis. In total, 391 articles were removed during screening for following reasons: 9 studies involved animal subjects, 306 were unrelated to genetics or obesity, 60 articles were conducted among non-Malaysians and 16 were review papers (Figure 1).
The characteristics of the included studies are summarized in Table 1. The first genetic study on obesity in Malaysia was published in 2009 by Liew et al. followed by an increasing trend in research on gene polymorphisms and their association with obesity starting from 2018 until the present. Most studies focused on university students and local community members attending health clinics, with objectives primarily centered around the relationship between specific gene variants and obesity or health parameters associated with obesity. Only three studies were conducted among children. The majority of studies involved three or more ethnic groups (n = 19), while others focused exclusively on Malay participants or had more than 75% Malay participants (n = 11), Chinese participants (n = 2), or combined Chinese and Indian participants (n = 2). However, 99% of the studies did not stratify outcomes by ethnic group (except for the study by Kok et al.) but rather compared obese and non-obese individuals. It is noteworthy that all included studies had relatively small sample sizes, ranging from 150 to 1200 participants, which may impact the validity and generalizability of their research findings.

3.2. Genetic Variants of Interest and Risk of Obesity

Forty-two variants of genes directly or indirectly involved in susceptibility to obesity were identified (Table 2). Each study examined the association between these gene variants and obesity, typically reporting results in terms of Minor Allele Frequency (MAF) and/or Odds Ratio (OR). Among these, the genetic variants of LEPR (K656N), LEP (G2548A- Indian only), ADIPOQ (rs17366568), UCP2 (45bp-I/D), ADRB3 (rs4994), MC3R (Rs3827103), PPARγ (pro12Ala—Malay only), IL1RA (intron 2 VNTR), NFKB1 (rs28362491), and FADS1 (rs174547—Indian only) have been significantly associated with obesity risk in Malaysians.
However, no significant association with obesity was reported for the genetic variants of LEP (A19G), LEPR (K109R, Q223R), PYY (R72T), NPY (rs16147T, rs161139C), PPAR (L162V), PPAR2 (C161T), PPARδ (T294C), UCP1 (-3826 A/G), UCP3 (-55C/T), CARTPT (rs2239670), POMC (Rsal), MC4R (V1031), FTO (rs9930506, rs9939609, rs17817288, rs9930501, rs9932754), ADIPOQ (rs3774261), INSIG2 (rs7566605), RETN, DRD2 (Taq1A, Taq1B, Taq1C), VDR (bsml), IRX (rs3751723), FASN (rs4246445, rs2229425, rs2228305,rs2229422), and ADRB2 (rs1042713).

3.3. Protein–Protein Interaction (PPI) Network

Out of the thirty-three genes for which selected genetic variants were reported as significantly associated with obesity, only four genes (LEP, LEPR, POMC, and MC4R) are known to be linked with monogenic obesity. Mutations in these genes can cause early-onset obesity which is not easily influenced by environmental factors alone, unlike polygenic obesity. Since the majority of the reviewed articles are highly heterogenous, biased in terms of selecting candidate genes, and the studies were mostly underpowered, only the genes associated with monogenic obesity were selected for protein—protein interaction analysis. The STRING database shows that interactions between LEP, LEPR, POMC, and MC4R have been reported from various experimental data, curated databases, and co-expression studies (Figure 2). Nodes in these networks represent proteins, and the edges denote their interactions. The roles of these genes in many biological processes and important molecular pathways are also well established. Examples of Gene Ontology (GO) biological processes involving some or all of the genes include response to a melanocyte-stimulating hormone, leptin-mediated signaling pathway, regulation of appetite, and regulation of feeding behavior. In terms of KEGG pathways, examples include adipocytokine signaling pathway and AMPK signaling pathway (Table 3).

4. Discussion

Review findings suggest that there was a growing focus on genetic variants associated with obesity in Malaysians. This trend may be attributed to substantial evidence indicating that specific genetic variants may affect populations differently, influenced by factors such as sociocultural contexts, dietary habits, and patterns of body fat distribution [48]. It was noted that the majority of the included studies (94%) were conducted among adults in universities and local health clinics. However, defining early predictors of obesity is crucial, as the National Health Morbidity Survey 2022 (NHMS 2022) reported that childhood obesity is currently a major health problem in Malaysia, with one in three teens aged 13–17 being overweight or obese [49]. Therefore, while it is more convenient to recruit adults, there is also a need for more biomarker studies involving children and adolescents. This would simultaneously address the scarcity of scientific data on the genetics of childhood obesity, as identified in the Health Research Priorities for the 12th Malaysia Plan (12MP-HRP) [6].
The present review also found that a considerable number of genetic variants have been explored. Out of 42 gene polymorphisms investigated, fewer than half were reported to have a significant association with obesity by the authors, and several studies investigated overlapping targets. For example, variants like LEPR Q223R [14,23,25,46] LEP Q2548A [23,25,37,46], LEP A19G [14,37], PPAR L162V and PPAR2 C161T [24,38], FTO rs9930506 [38,45], UCP2 45-bp I/D [15,27], DRD2 Taq1A, Taq1B and Taq1D [39,44]. Notably, the findings across these studies were consistent, showing that none of the genetic variants were linked to obesity, except for LEP G2548A [23], which may be associated with overweight/obesity among Indian males; UCP2 45 bp I/D [27] with overall adiposity among Malaysian women; and DRD2 polymorphisms with eating behavior but not with obesity [39,44]. It is worth noting that these findings may, in part, reflect the small sample sizes used in the studies and the fact that the claimed significance level may not be entirely reliable due to the insufficient statistical power. To address this issue more effectively, studies with larger sample sizes and adequate power are needed to produce more reliable and valid findings that contribute more effectively to scientific knowledge.
Furthermore, this review has identified contradictory results regarding the association of well-established FTO gene variants with obesity risk among Malays, Chinese, and Indians. A research group in Singapore found that FTO variants, especially rs9939609, which are common in European populations, were significantly associated with obesity in Chinese and Malays but not in Asian Indians [50]. However, there was no evidence for this SNP or other FTO regions in obesity and obesity-related parameters in either Chinese, Malays, or Indians in Malaysia [21]. Nevertheless, these results further emphasize the variation in genetic elements among individuals that influence susceptibility to obesity. This is supported by Karra et al., who showed that people with two high-obesity-risk FTO variants have a 70% increased risk of becoming obese compared to those with low-obesity-risk variants [51]. Therefore, given that the targets examined in the included studies were based on evidence from various populations, it is essential for researchers in Malaysia to first conduct gene profiling among obese individuals. This preliminary step will help to identify specific biomarker targets for obesity, paving the way for more focused genetic investigations.
Due to the lack of homogeneity in the samples from most of the reviewed articles, only the protein interactions between studied genes related to monogenic obesity are analyzed using STRING. Monogenic obesity is relatively rare, follows a Mendelian pattern of inheritance, and is usually characterized by severe obesity, unlike the polygenic form of obesity, which often arises from the cumulative effect of multiple genetic variants, each contributing a small effect on obesity risk, as well as environmental factors. Genes such as LEP, LEPR, POMC, and MC4R are known to play crucial roles in various important biological processes and pathways. The most significant shared pathway relates to the adipocytokine signaling pathway. This pathway involves cytokines and adipokines produced by adipocytes and is interconnected with other pathways such as the AMPK signaling pathway, regulating processes such as the metabolism, inflammation, and energy balance. Some components of adipokines, including leptin, adiponectin, resistin, Interleukin-6 (IL-6), and Tumor Necrosis Factor-α (TNF-α), have been associated with the development of insulin resistance, obesity, and related health conditions [52,53,54,55]. Understanding the characteristics of genetic variants that cause each form of obesity is crucial for more precise disease management.
This scoping review has several limitations. Firstly, most of the included studies had small sample sizes and suboptimal study designs. Secondly, the majority of study participants were drawn from institutional and healthcare settings, which may limit the generalizability of the results. Thirdly, the studies encompassed a wide range of ages, and these variations should be considered when interpreting the results. Finally, the bibliographic search was conducted using only three databases, which might have led to the omission of some relevant articles.
For future obesity genetic research, it is crucial for researchers to adopt appropriate study designs and ensure the inclusion of larger sample sizes to strengthen the validity of the findings. Additionally, it is important to include participants from community settings to ensure the findings are applicable to a broader population. Given the significant role of gene–environment interactions in the onset of obesity, large-scale epigenetic studies are needed to identify novel genetic variants specific to the Malaysian population. Leveraging advanced technologies, such as long-read sequencing, can help overcome the limitations of genome-wide association studies (GWAS), which primarily capture common genetic variants.

5. Conclusions

In summary, genetic biomarker research on obesity among Malaysians remains limited in scope, primarily focusing on well-known genes or gene variants. While targeting established gene variants can be one research strategy, it is more compelling to establish comprehensive biomarker genetic profiles related to obesity across different age groups—from children to adults—in the local population.

Author Contributions

Conceptualization, S.S.H. and L.A.Z.; methodology, S.S.H., L.A.Z., N.A.S. and N.A.Z.A.; software, S.S.H.; formal analysis, S.S.H., L.A.Z., N.A.S. and N.A.Z.A.; data curation, S.S.H.; writing—original draft preparation, S.S.H.; writing—review and editing, S.S.H. and L.A.Z.; visualization, S.S.H. and L.A.Z.; supervision, S.S.H.; project administration, S.S.H. All authors have read and agreed to the published version of the manuscript.

Funding

The research presented in this publication was supported by the National Institutes of Health (NIH) Malaysia, under registration number NMRR ID-23-01291-2ZC.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to thank the Director General of Health Malaysia for the permission to publish this paperThe content is the sole responsibility of the authors and does not necessarily reflect the official views of NIH Malaysia.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Obesity and Overweight. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 13 July 2023).
  2. Statistics. Available online: https://easo.org/media-portal/statistics/ (accessed on 13 July 2023).
  3. Institute for Public Health. NHMS 2011. Available online: https://iku.gov.my/nhms-2011 (accessed on 4 July 2024).
  4. Institute for Public Health. NHMS 2015. Available online: https://iku.gov.my/nhms-2015 (accessed on 4 July 2024).
  5. NHMS 2023. Available online: https://iku.gov.my/nhms-2023 (accessed on 4 July 2024).
  6. Muhamad, N.A.; Lemna, F.N.; Mohd Dali, N.S.; Too, L.C.; Johari, M.Z.; Bakhtiar, F.; Ab. Ghani, R.M.; Mahjom, M.; Sapian, R.A.; Musa, N.S.E.; et al. Health Research Priorities for 12th Malaysia Plan (12MP-HRP) 2021–2025; National Institutes of Health, Ministry of Health Malaysia: Shah Alam, Malaysia, 2021.
  7. Hinney, A.; Vogel, C.I.G.; Hebebrand, J. From Monogenic to Polygenic Obesity: Recent Advances. Eur. Child Adolesc. Psychiatry 2010, 19, 297–310. [Google Scholar] [CrossRef] [PubMed]
  8. Mayoral, L.P.-C.; Andrade, G.M.; Mayoral, E.P.-C.; Huerta, T.H.; Canseco, S.P.; Rodal Canales, F.J.; Cabrera-Fuentes, H.A.; Cruz, M.M.; Pérez Santiago, A.D.; Alpuche, J.J.; et al. Obesity Subtypes, Related Biomarkers & Heterogeneity. Indian J. Med. Res 2020, 151, 11–21. [Google Scholar] [CrossRef]
  9. Loos, R.J.F.; Yeo, G.S.H. The Genetics of Obesity: From Discovery to Biology. Nat. Rev. Genet. 2022, 23, 120–133. [Google Scholar] [CrossRef] [PubMed]
  10. Chami, N.; Preuss, M.; Walker, R.W.; Moscati, A.; Loos, R.J.F. The Role of Polygenic Susceptibility to Obesity among Carriers of Pathogenic Mutations in MC4R in the UK Biobank Population. PLoS Med. 2020, 17, e1003196. [Google Scholar] [CrossRef] [PubMed]
  11. Arksey, H.; O’Malley, L. Scoping Studies: Towards a Methodological Framework. Int. J. Soc. Res. Methodol. 2005, 8, 19–32. [Google Scholar] [CrossRef]
  12. Peters, M.D.J.; Marnie, C.; Tricco, A.C.; Pollock, D.; Munn, Z.; Alexander, L.; McInerney, P.; Godfrey, C.M.; Khalil, H. Updated Methodological Guidance for the Conduct of Scoping Reviews. JBI Evid. Synth. 2020, 18, 2119–2126. [Google Scholar] [CrossRef]
  13. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Med. 2018, 169, 7. Available online: https://www.acpjournals.org/doi/10.7326/M18-0850 (accessed on 14 July 2023). [CrossRef]
  14. Liew, S.F.; Chuah, H.S.; Lau, C.H.; Lee, C.H.; Say, Y.H. Prevalence of the Leptin and Leptin Receptor Gene Variants and Obesity Risk Factors among Malaysian University Students of Setapak, Kuala Lumpur. Asian J. Epidemiol. 2009, 3, 49–58. [Google Scholar] [CrossRef]
  15. Yiew, S.-K.; Khor, L.-Y.; Tan, M.-L.; Pang, C.-L.; Chai, V.-Y.; Kanachamy, S.S.; Say, Y.-H. No Association between Peroxisome Proliferator-Activated Receptor and Uncoupling Protein Gene Polymorphisms and Obesity in Malaysian University Students. Obes. Res. Clin. Pract. 2010, 4, e325–e331. [Google Scholar] [CrossRef]
  16. Chan, P.M.; Fan, S.H.; Say, Y.H. No Association of Peptide Tyrosine-Tyrosine (PYY) Gene R72T Variant with Obesity in the Kampar Health Clinic Cohort, Malaysia. Malays. J. Nutr. 2011, 17, 201–212. [Google Scholar]
  17. Lisa, Y.; Sook, H.F.; Yee, H.S. Association of the Cocaine- and Amphetamine-Regulated Transcript Prepropeptide Gene (CARTPT) Rs2239670 Variant with Obesity among Kampar Health Clinic Patrons, Malaysia. Malays. J. Med. Sci. 2012, 19, 43–51. [Google Scholar] [PubMed]
  18. Lee, H.M.; Fan, S.H.; Say, Y.H. Prevalence of RsaI Polymorphism in the 5′ Untranslated Region (UTR) of Proopiomelanocortin (POMC) Gene and Its Association with Obesity in the Kampar Health Clinic Cohort, Malaysia. Malays. J. Med. Health Sci. 2012, 8, 61–68. [Google Scholar]
  19. Chua, H.N.; Fan, S.H.; Say, Y.H. Prevalence of Melanocortin Receptor 4 (MC4R) V103I Gene Variant and Its Association with Obesity among the Kampar Health Clinic Cohort, Perak, Malaysia. Med. J. Malays. 2012, 67, 234–235. [Google Scholar]
  20. Apalasamy, Y.D.; Ming, M.F.; Rampal, S.; Bulgiba, A.; Mohamed, Z. Genetic Association of SNPs in the FTO Gene and Predisposition to Obesity in Malaysian Malays. Braz. J. Med. Biol. Res. 2012, 45, 1119–1126. [Google Scholar] [CrossRef]
  21. WeiWei, C.; SookHa, F.; YeeHow, S. Association of Fat Mass and Obesity-Associated (FTO) Gene Rs9939609 Variant with Obesity among Multi-Ethnic Malaysians in Kampar, Perak. Sains Malays. 2013, 42, 365–371. [Google Scholar]
  22. Apalasamy, Y.D.; Ming, M.F.; Rampal, S.; Bulgiba, A.; Mohamed, Z. Association of Melanocortin-4 Receptor Gene Polymorphisms with Obesity-Related Parameters in Malaysian Malays. Ann. Hum. Biol. 2013, 40, 102–106. [Google Scholar] [CrossRef] [PubMed]
  23. Ng, Z.Y.; Veerapen, M.K.; Hon, W.M.; Lim, R.L.H. Association of Leptin/Receptor and TNF-α Gene Variants with Adolescent Obesity in Malaysia. Pediatr. Int. 2014, 56, 689–697. [Google Scholar] [CrossRef]
  24. Apalasamy, Y.D.; Rampal, S.; Salim, A.; Moy, F.M.; Bulgiba, A.; Mohamed, Z. Association of ADIPOQ Gene with Obesity and Adiponectin Levels in Malaysian Malays. Mol. Biol. Rep. 2014, 41, 2917–2921. [Google Scholar] [CrossRef]
  25. Fan, S.-H.; Say, Y.-H. Leptin and Leptin Receptor Gene Polymorphisms and Their Association with Plasma Leptin Levels and Obesity in a Multi-Ethnic Malaysian Suburban Population. J. Physiol. Anthropol. 2014, 33, 15. [Google Scholar] [CrossRef]
  26. Apalasamy, Y.D.; Moy, F.M.; Rampal, S.; Bulgiba, A.; Mohamed, Z. Genetic Associations of the INSIG2 Rs7566605 Polymorphism with Obesity-Related Metabolic Traits in Malaysian Malays. Genet. Mol. Res. 2014, 13, 4904–4910. [Google Scholar] [CrossRef]
  27. Say, Y.-H.; Ban, Z.-L.; Arumugam, Y.; Kaur, T.; Tan, M.-L.; Chia, P.-P.; Fan, S.-H. Uncoupling Protein 2 Gene (UCP2) 45-Bp I/D Polymorphism Is Associated with Adiposity among Malaysian Women. J. Biosci. 2014, 39, 867–875. [Google Scholar] [CrossRef]
  28. Apalasamy, Y.D.; Rampal, S.; Salim, A.; Moy, F.M.; Su, T.T.; Majid, H.A.; Bulgiba, A.; Mohamed, Z. Polymorphisms of the Resistin Gene and Their Association with Obesity and Resistin Levels in Malaysian Malays. Biochem Genet 2015, 53, 120–131. [Google Scholar] [CrossRef] [PubMed]
  29. Apalasamy, Y.D.; Ming, M.F.; Rampal, S.; Bulgiba, A.; Mohamed, Z. Gender-Dependent Association of a β(2)-Adrenergic Gene Variant with Obesity Parameters in Malaysian Malays. Asia Pac. J. Public Health 2015, 27, NP154–NP165. [Google Scholar] [CrossRef] [PubMed]
  30. Chia, P.-P.; Fan, S.-H.; Say, Y.-H. Screening of Peroxisome Proliferator-Activated Receptors (PPARs) α, γ and α Gene Polymorphisms for Obesity and Metabolic Syndrome Association in the Multi-Ethnic Malaysian Population. Ethn. Dis. 2015, 25, 383–390. [Google Scholar] [CrossRef]
  31. Zain, S.M.; Mohamed, Z.; Jalaludin, M.Y.; Fauzi, F.; Hamidi, A.; Zaharan, N.L. Comprehensive Evaluation of the Neuropeptide-Y Gene Variants in the Risk of Obesity: A Case–Control Study and Meta-Analysis. Pharmacogenetics Genom. 2015, 25, 501. [Google Scholar] [CrossRef]
  32. Zaharan, N.L.; Muhamad, N.H.; Jalaludin, M.Y.; Su, T.T.; Mohamed, Z.; Mohamed, M.N.A.; A Majid, H. Non-Synonymous Single-Nucleotide Polymorphisms and Physical Activity Interactions on Adiposity Parameters in Malaysian Adolescents. Front. Endocrinol. 2018, 9, 209. [Google Scholar] [CrossRef] [PubMed]
  33. Zahri, M.K.; Emilia, A.; Rawi, R.I.M.; Taib, W.R.W.; Sani, A.I.; Baig, A.A. Contribution of the Pro12Ala Polymorphism of Peroxisome Proliferator-Activated Receptor Ɣ2 Gene in Relation to Obesity. Meta Gene 2016, 10, 39–44. [Google Scholar] [CrossRef]
  34. Shunmugam, V.; Say, Y.-H. Evaluation of Association of ADRA2A Rs553668 and ACE I/D Gene Polymorphisms with Obesity Traits in the Setapak Population, Malaysia. Iran Red. Crescent. Med. J. 2016, 18, e22452. [Google Scholar] [CrossRef]
  35. Kok, Y.-Y.; Ong, H.-H.; Say, Y.-H. Interleukin-1 Receptor Antagonist and Interleukin-4 Genes Variable Number Tandem Repeats Are Associated with Adiposity in Malaysian Subjects. J. Obes. 2017, 2017, 4104137. [Google Scholar] [CrossRef]
  36. Rahmadhani, R.; Zaharan, N.L.; Mohamed, Z.; Moy, F.M.; Jalaludin, M.Y. The Associations between VDR BsmI Polymorphisms and Risk of Vitamin D Deficiency, Obesity and Insulin Resistance in Adolescents Residing in a Tropical Country. PLoS ONE 2017, 12, e0178695. [Google Scholar] [CrossRef]
  37. Rohani, W.T.W.; Aryati, A.; Shamsuddin, A.A. Haplotype Analysis of Leptin Gene Polymorphisms in Obesity among Malays in Terengganu, Malaysia Population. Med. J. Malays. 2018, 73, 281–285. [Google Scholar]
  38. Mitra, S.R.; Tan, P.Y.; Amini, F. Effect of FTO Rs9930506 on Obesity and Interaction of the Gene Variants with Dietary Protein and Vitamin E on C-Reactive Protein Levels in Multi-Ethnic Malaysian Adults. J. Hum. Nutr. Diet. 2018, 31, 758–772. [Google Scholar] [CrossRef] [PubMed]
  39. Lek, F.-Y.; Ong, H.-H.; Say, Y.-H. Association of Dopamine Receptor D2 Gene (DRD2) Taq1 Polymorphisms with Eating Behaviors and Obesity among Chinese and Indian Malaysian University Students. Asia Pac. J. Clin. Nutr. 2018, 27, 707–717. [Google Scholar] [CrossRef] [PubMed]
  40. Association of IRX3 Rs3751723 Polymorphism with the Risk of Overweight and Obesity: Case-Control Study and Meta-Analysis. Available online: https://colab.ws/articles/10.1016%2Fj.mgene.2018.01.007 (accessed on 19 September 2024).
  41. Chong, E.T.J.; Kuok, S.S.E.; Lee, P.-C. Risk Association, Linkage Disequilibrium and Haplotype Analyses of FASN Rs4246445, Rs2229425, Rs2228305 and Rs2229422 Polymorphisms in Overweight and Obesity. Bioimpacts 2018, 8, 159–165. [Google Scholar] [CrossRef] [PubMed]
  42. Mitra, S.R.; Tan, P.Y.; Amini, F. Association of ADRB2 Rs1042713 with Obesity and Obesity-Related Phenotypes and Its Interaction with Dietary Fat in Modulating Glycaemic Indices in Malaysian Adults. J. Nutr. Metab. 2019, 2019, 8718795. [Google Scholar] [CrossRef]
  43. Alshajrawi, O.; Basit, E.; Baig, A. HIF1 (Rs11549465) and NFKB1 (Rs28362491) Variants Association with Obesity in Malaysia. Meta Gene 2020, 25, 100753. [Google Scholar] [CrossRef]
  44. Lim, Z.-M.; Chie, Q.-T.; Teh, L.-K. Influence of Dopamine Receptor Gene on Eating Behaviour and Obesity in Malaysia. Meta Gene 2020, 25, 100736. [Google Scholar] [CrossRef]
  45. Tan, P.Y.; Mitra, S.R. The Combined Effect of Polygenic Risk from FTO and ADRB2 Gene Variants, Odds of Obesity, and Post-Hipcref Diet Differences. Lifestyle Genom 2020, 13, 84–98. [Google Scholar] [CrossRef]
  46. Mohanraj, J.; D’Souza, U.J.A.; Fong, S.Y.; Karkada, I.R.; Jaiprakash, H. Association between Leptin (G2548A) and Leptin Receptor (Q223R) Polymorphisms with Plasma Leptin, BMI, Stress, Sleep and Eating Patterns among the Multiethnic Young Malaysian Adult Population from a Healthcare University. Int. J. Environ. Res. Public Health 2022, 19, 8862. [Google Scholar] [CrossRef]
  47. Ching, Y.K.; Chin, Y.S.; Appukutty, M.; Chan, Y.M.; Lim, P.Y.; Nasir, K.H. Interactions of Genetic and Macronutrient Intake with Abdominal Obesity among Middle-Aged Vegetarians in Malaysia. Asia Pac. J. Clin. Nutr. 2023, 32, 168–182. [Google Scholar] [CrossRef]
  48. Caprio, S.; Daniels, S.R.; Drewnowski, A.; Kaufman, F.R.; Palinkas, L.A.; Rosenbloom, A.L.; Schwimmer, J.B. Influence of Race, Ethnicity, and Culture on Childhood Obesity: Implications for Prevention and Treatment. Diabetes Care 2008, 31, 2211–2221. [Google Scholar] [CrossRef] [PubMed]
  49. Ministry of Health Malaysia. NHMS AHS 2022 States Report. Available online: https://iku.gov.my/nhms-ahs-2022-state-report (accessed on 12 June 2024).
  50. Tan, J.T.; Dorajoo, R.; Seielstad, M.; Sim, X.L.; Ong, R.T.-H.; Chia, K.S.; Wong, T.Y.; Saw, S.M.; Chew, S.K.; Aung, T.; et al. FTO Variants Are Associated with Obesity in the Chinese and Malay Populations in Singapore. Diabetes 2008, 57, 2851–2857. [Google Scholar] [CrossRef] [PubMed]
  51. Karra, E.; O’Daly, O.G.; Choudhury, A.I.; Yousseif, A.; Millership, S.; Neary, M.T.; Scott, W.R.; Chandarana, K.; Manning, S.; Hess, M.E.; et al. A Link between FTO, Ghrelin, and Impaired Brain Food-Cue Responsivity. J. Clin. Investig. 2013, 123, 3539–3551. [Google Scholar] [CrossRef] [PubMed]
  52. Achari, A.E.; Jain, S.K. Adiponectin, a Therapeutic Target for Obesity, Diabetes, and Endothelial Dysfunction. Int. J. Mol. Sci. 2017, 18, 1321. [Google Scholar] [CrossRef]
  53. Rangareddy, H.; Venkatapathappa, P.; Mandalaneni, K.; Srinivasaiah, A.; Bourne-Yearwood, K.; Rangareddy, H.; Venkatapathappa, P.; Mandalaneni, K.; Srinivasaiah, A.; Bourne-Yearwood, K. Leptin and Obesity: Understanding the Impact on Dyslipidemia. In Body Mass Index—Overweight, Normal Weight, Underweight; IntechOpen: London, UK, 2023; ISBN 978-1-83768-335-2. [Google Scholar]
  54. Rachwalik, M.; Hurkacz, M.; Sienkiewicz-Oleszkiewicz, B.; Jasinski, M. Role of Resistin in Cardiovascular Diseases: Implications for Prevention and Treatment. Adv. Clin. Exp. Med. 2021, 30, 865–874. [Google Scholar] [CrossRef]
  55. Sethi, J.K.; Hotamisligil, G.S. Metabolic Messengers: Tumour Necrosis Factor. Nat. Metab. 2021, 3, 1302–1312. [Google Scholar] [CrossRef]
Genes 15 01334 g001
Figure 1. PRISMA flowchart outlining selection process for including studies in the review.
Figure 1. PRISMA flowchart outlining selection process for including studies in the review.
Genes 15 01334 g001
Genes 15 01334 g002
Figure 2. STRING protein networks of genes associated with monogenic obesity.
Figure 2. STRING protein networks of genes associated with monogenic obesity.
Genes 15 01334 g002
Table 1. Participants characteristics.
Table 1. Participants characteristics.
NoStudy (Author/Year)ObjectivesSample SizeParticipants characteristics (Gender/Ethnicity/Obesity Status)
1Liew et al., 2009 [14](i) To investigate the prevalence of the leptin gene (LEP) A19G and leptin receptor gene (LEPR) K109R, Q223R, and K656N variants and their possible association with obesity. (ii) To investigate the prevalence of associated obesity risk factors in Malaysian university students of Setapak, Kuala Lumpur.200Males: 85, Females: 115/mean age: 21.22 ± 2.85 years/Malay: 3 (2.1%); Chinese: 96 (67.1%); Indian: 36 (25.2%); Others: 3 (2.1%)/Non-obese: 143; Obese: 57
2Yiew et al., 2010 [15]To examine the prevalence of these gene polymorphisms and their possible potential link to obesity among university students. 256Males: 140, Females: 116/mean age 21.7 ± 1.7 years/Chinese: 240 (93.8%)/Non-obese: 170; Overweight: 67; Obese: 19
3Chan et al., 2011 [16]To examine the association between the R72T variant of the Peptide Tyrosine-Tyrosine (PYY) gene and obesity, along with related anthropometric measurements, among the cohort from Kampar Health Clinic in Malaysia.197Males: 78, Females: 119/mean age: 55.1 ± 11.0
years/Malay: 75 (38.1%); Chinese: 77 (39.1%); Indian: 40 (20.3%); Others: 5 (2.5%)/Non-obese: 98; Obese: 99
4Lisa et al.,2011 [17]To examine the association between the CART prepropeptide gene (CARTPT) rs2239670 variant and obesity, along with related anthropometric indicators, among patients at a health clinic in Kampar, Perak, Malaysia.300Males: 115, Females: 185/Malay/Peninsular Bumiputera: 98 (32.7%); Chinese: 141 (47.0%); Indian: 61 (20.3%)/Non-obese: 163 (mean age: 53.18 ± 15.85 years); Obese: 137 (51.46 ± 12.40)
5Lee et al., 2012 [18]To explore the prevalence of the RsaI SNP in the 5′-untranslated region (UTR) of the POMC gene and its potential association with obesity.302Males: 120, Females: 182/Malay: 92; Chinese: 141; Indian: 64; Others: 5/Non-obese: 160; Obese: 142
6Chua et al., 2012 [19]To assess the prevalence of the Melanocortin receptor 4 (MC4R) V103I gene variant and its relationship with obesity among a group of patients attending the Kampar Health Clinic.254Males: 101; Females: 153/mean age: 52.27 ± 14.2 years/Malay: 74 (29.1%); Chinese: 124 (20.1%); Indian: 51 (20.1%); Others: 5 (2.0%)/Non-obese: 136; Obese: 118
7Apalasamy et al., 2012 [20]To examine whether SNPs and linkage disequilibrium (LD) blocks in different regions of the FTO gene are associated with obesity susceptibility in Malaysian Malays.587Malay: 587 (100%)/mean age: 48.29 ± 9.89 years/Non-obese: 429; Obese: 158
8Chey et al., 2013 [21]To examine the association between the FTO rs9939609 variant and obesity in a multi-ethnic Malaysian population.324Males: 126; Females: 198/age: 21 to 80 years old/Malay: 98; Chinese: 158; Indian: 68/Non-obese: 178; Obese: 146
9Apalasamy et al., 2013 [22]To genotype MC4R gene variants and evaluate the genetic link between MC4R SNPs and obesity-associated parameters.652Males: 293; Females: 359/Malay: 483 (100%)/Non-obese: 483; Obese: 169
10Ng et al., 2014 [23]To assess the prevalence of overweight and obesity among Malaysian adolescents and investigate the association of specific polymorphisms with overweight, obesity, or excess body fat in this group.613Males: 248 (40.5%); Females: 365 (59.5%)/mean age: 14.8 ± 1.3 years; Malay: 241; Chinese: 219; Indian: 153/Non-obese: 470; Overweight/Obese: 143
11Apalasamy et al., 2014 [24]To examine the association between the ADIPOQ rs17366568 and rs3774261 SNPs with obesity, as well as their association with adiponectin levels, in Malaysian Malays.574Malay: 574 (100%)/Non-obese: 424 (mean age: 46.17 ± 5.32 years); Obese: 150 (mean age: 45.61 ± 7.37 years)
12Fan and Say, 2014 [25]To examine the prevalence of SNPs in the leptin gene (LEP) [A19G and G2548A] and the leptin receptor gene (LEPR) [K109R and Q223R], and their associations with fasting plasma leptin levels and obesity in a suburban population in Kampar, Perak.408Male: 169; Female: 239/mean age: 52.4 ± 13.7 years/Malay: 148; Chinese: 177; Indian: 83/Non-obese: 218; Obese: 190
13Apalasamy et al., 2014 [26]To investigate the association between the rs7566605 SNP and obesity, as well as other metabolic parameters, in Malaysian Malays.672Malay: 672 (100%)/Non-obese: 500 (mean age: 46.47 ± 7.06 years); Obese: 172 (mean age: 47.97 ± 6.05 years)
14Say et al., 2014 [27]To assess the prevalence of the UCP2 45-bp I/D polymorphism and its potential association with obesity (measured by BMI), overall adiposity (measured by total body fat percentage), and central adiposity (measured by waist-to-hip ratio) in a representative sample of the multi-ethnic Malaysian population.926Males: 416, Females: 510/Malay: 102; Chinese: 672; Indian: 152/Non-obese: 661; Obese: 265
15Apalasamy et al., 2015 [28](i) To examine the association between polymorphisms in the resistin gene and obesity in a homogeneous Malaysian Malay population. (ii) To explore the association between resistin levels and specific SNPs and haplotypes of the RETN gene.631Malay: 631 (100%)/Non-obese: 469 (mean age: 48.33 years); Obese: 162 (mean age: 48.43 years)
16Apalasamy et al., 2015 [29]To examine the association between the rs1042714 SNP and obesity-related parameters. 672Male: 300; Female: 372/mean age: 48.22 ± 10.05 years/Malay: 672 (100%)
17Chia et al., 2015 [30]To investigate the association of peroxisome proliferator-activated receptor (PPAR) genes PPARα L162V, PPARγ2 C161T, and PPARδ T294C single nucleotide polymorphisms (SNPs) with obesity and metabolic syndrome (MetS) in a multi-ethnic population in Kampar, Malaysia.307Males: 124; Females: 183/mean age 53.3 ± 14.2 years/Malay: 97; Chinese: 85; Indian: 55/Non-obese: 127, Obese: 180
18Zain et al., 2015 [31](i) To assess the impact of NPY rs5574 and rs16147 variants on the risk of obesity in Asians. (ii) To perform a meta-analysis summarizing the effects of these variants, including the extensively researched rs16139.942Males: 264 (28%); Females: 678 (72%)/age: 13 years/Malay: 74%; Chinese: 13%; Indian: 10%; Others: 3%/Non-obese: 680; Overweight/obese: 262
19Zaharan et al., 2018 [32]To investigate potential associations between adiposity parameters and selected SNPs among the Malaysian Health and Adolescents Longitudinal Research Team study (MyHeARTs).1179Males: 39%; Females: 61%/age: 15 years old/Malay: 79%; Chinese: 7%; Indian: 9%; Others: 5%/Non-obese: 76%; Overweight/Obese: 24%
20Zahri et al., 2016 [33]To determine the genotypic and allelic frequencies of the PPARƔ2 gene and assess its association with lipid profiles, anthropometric measurements, and obesity susceptibility in Malay individuals.217Malay: 217 (100%)/Non-obese: 123 (mean age: 33.59 ± 10.54 years); Obese: 94 (mean age: 39.18 ± 9.97 years)
21Shunmugame et al., 2016 [34]To investigate the association between the adrenergic receptor α2A (ADRA2A) rs553668 and angiotensin-converting enzyme (ACE) I/D SNPs with obesity traits (body mass index—BMI; waist-hip ratio—WHR; total body fat percentage—TBF) in a Malaysian population.214Males: 99; Females: 115/mean age: 26.27 ± 11.93 years/Malay: 45; Chinese: 116; Indians: 53/Non-obese: 142; Obese: 72
22Kok et al., 2017 [35]To examine the association of IL1RA and IL4 VNTRs with obesity and adiposity in 315 Malaysian individuals.315Males: 128; Females: 187/Malay: 23; Chinese: 251; Indian: 41/Non-obese: 261; Obese: 54
23Rahmadhani et al., 2017 [36]To investigate the association between BsmI polymorphism and risk of vitamin D deficiency, obesity, and insulin resistance in adolescents residing in a tropical country.941Males: 261 (28%); Females: 680 (72%); age: 13 years old/Malay: 702 (75%); Chinese: 121 (13%); Indian: 94 (10%); Others: 24 (2%)/Non-obese: 629 (67%); Overweight: 104 (11%); Obese: 208 (22%)
24Shamsuddin et al., 2018 [37]To investigate the association of SNPs and haplotype of the Leptin gene, specifically G2548A, A19G, and H1328080 with obesity in Malays from Terengganu.249Malay: 249 (100%)/Non-obese: 101; Overweight: 148; Obese: 54
25Mitra et al., 2018 [38]To assess (i) the impact of FTO rs9930506 on obesity and related anthropometric and biochemical parameters, and (ii) how diet influences the relationship between FTO rs9930506 and obesity phenotypes.178Males: 24; Females: 154/Non-obese: 99; Obese: 79
26Lek et al., 2018 [39]To examine the association between DRD2 Taq1A, Taq1B, and Taq1D gene polymorphisms with eating behavior (i.e., the preference, intake frequency, craving of high-fat foods) and obesity.394Males: 161 (mean age: 20.9 ± 0.13 years); Females: 233 (mean age: 20.9±0.11 years)/Chinese: 308; Indian: 86/ Non-obese: 327; Obese: 67
27Chong et al., 2018 [40]To examine the association between the Iroquois homeobox 3 (IRX3) rs3751723 polymorphism and increased risk of obesity in the Malaysian population through a case-control study and a meta-analysis.1030Non-obese: 694 (mean age: 25.91 ± 9.75 years); Overweight: 223 (mean age: 26.91 ± 12.98 years); Obese: 113 (mean age: 26.45 ± 11.97 years)
28Chong et al., 2018 [41]To explore the association between fatty acid synthase (FASN) rs4246445, rs2229425, rs2228305, and rs2229422 SNPs with the risk of overweight and obesity in the Malaysian population.1030Males: 620; Females: 410/Non-obese: 694 (mean age: 23.41 ± 9.81 years); Overweight: 223 (mean age: 23.34 ± 9.78 years); Obese: 113 (mean age: 23.45 ± 9.88)
29Mitra et al., 2019 [42]To assess (i) the relationship between ADRB2 rs1042713 and obesity as well as related metabolic parameters, and (ii) the impact of dietary nutrients on these associations in Malaysian adults.178Males: 24; Females: 154/Non-obese: 99; Obese: 79
30Al-Shajrawi et al., 2020 [43]To investigate the role of variants in NFKB1 (rs28362491) and HIF1 (rs11549465) in relation to obesity in Malay individuals.188Males: 70; Females: 118/Malay: 188 (100%)/Obese: 93 (37.9 ± 2 9.2 years); Non-obese: 95 (mean age: 32.24 ± 12.1 years)
31Lim et al., 2020 [44]To examine how dopamine type 2 receptors (DRD2) gene variants (ANKK1/DRD2 Taq1A, DRD2 Taq1B, and DRD2 Taq1D) influence eating behaviors (i.e., cognitive restraint eating (CR), emotional eating (EE), and uncontrolled eating (UE)) and their association with obesity. 394Males: 125; Females: 269/Malay: 32, Chinese: 329, Indian: 32; Aborigine: 1
32Tan et al., 2020 [45]To examine the combined effect of the FTO rs9930501, rs9930506, and rs9932754 variants and ADRB2 rs1042713 and rs1042714 using polygenic risk scores (PRSs) on (1) the odds of obesity and (2) changes in dietary, anthropometric, and cardiometabolic parameters following a high-protein, calorie-restricted, high-vitamin E, high-fiber (Hipcref) diet intervention in Malaysian adults.Cross sectional:178, RCT 128Males: 24; Females/Age: ≥18 years: 154/Malay: 86; Chinese: 42; Indians: 50/Non-obese: 99; Obese: 79
33Mohanraj et al., 2022 [46]To investigate the association between sleeping habits, eating behavior, stress indicators, and plasma leptin levels, as well as its genomic polymorphisms, among different racial groups within a young adult healthcare student population in Malaysia.185Males: 89; Females: 96/Malay: 61; Chinese: 45; Indian: 56; Others: 23/Non-obese: 129; Overweight/Obese: 56
34Ching et al., 2023 [47]To assess how the rs174547 variant in the fatty acid desaturase 1 (FADS1) gene interacts with macronutrient intakes such as carbohydrates (especially fiber), protein, and fat and its impact on abdominal obesity among middle-aged Malaysian vegetarians of Chinese and Indian ethnicity.163Males: 50 (30.7%); Females: 113 (69.3%)/mean age: 50 ± 5 years/Chinese: 95; Indian: 68
Table 2. List of studied genetic variants associated with monogenic and polygenic obesity. The Minor Allele Frequency (MAF), Odds Ratio (OR), and findings on the association with obesity for each variant presented were taken directly from the respective studies.
Table 2. List of studied genetic variants associated with monogenic and polygenic obesity. The Minor Allele Frequency (MAF), Odds Ratio (OR), and findings on the association with obesity for each variant presented were taken directly from the respective studies.
(a) Monogenic Obesity
NoAuthorGene VariantsOutcomes (Minor Allele Frequency (MAF)/Odd Ratio (OR)Estimated Power of Study
1Liew et al., 2009 [14]
  • Leptin (LEP) gene A19G
  • Leptin Receptor (LEPR) gene K656N, Q223R, and K109R
MAF: LEP A19G (0.52) and LEPR K109R (0.43), LEPR Q223R (0.36), 656N allele (0.31). OR: N/A.
Finding of the study: Only LEPR K656N was associated with obesity.		0.545
2Chua et al., 2012 [19]
  • Melanocortin receptor 4 (MC4R) V103I gene variant
MAF: N/A. OR: N/A.
Finding of the study: MC4R V103I variant does not show a direct correlation with obesity in the studied cohort.		0.58
3Lee et al., 2012 [18]
  • RsaI SNP site in the 5′-untranslated region (UTR)of POMC
MAF: 0.31. OR: N/A
Finding of the study: RsaI alleles and genotypes were not identified as risk factors for obesity.		0.05
4Apalasamy et al., 2013 [22]
  • MC4R gene (rs571312, rs2229616 and rs7227255)
MAF: N/A. OR: N/A
Finding of the study: MC4R rs571312 and rs2229616 are associated with obesity-related traits in Malaysian Malays, but rs7227255 is not. The MC4R gene shows low linkage disequilibrium in this population, and its haplotypes do not increase obesity risk.		0.58
5Ng et al., 2014 [23]
  • LEP G-2548A (rs7799039)
  • LEPR Q223R (rs1137101)
  • TNFα G-308A (rs1800629)
* Except TNFα G-308A
MAF: LEP G-2548A, G = 0.247 (Chinese), 0.336 (Malay); LEPR Q223R, A = 0.139 (Chinese), 0.232 (Malay), 0.438 (Indian); and TNFα G-308A, A = 0.096 (Chinese), 0.058 (Malay) and 0.062 (Indian). OR: LEP G-2548A male adolescents with AA genotype: 3.64 (95%CI: 1.15–11.54; p = 0.025), LEP G-2548A 2.10 (overweight/obese): OR, 2.10 (95%CI: 0.98–4.48; p = 0.053), (over-fat) Indian subjects: 2.63 (95%CI: 1.14–6.03; p = 0.020).
Finding of the study: LEP G-2548A risk allele may be associated with overweight/obese Indian male adolescents in Malaysia.		1.00
6Fan and Say 2014 [25]
  • Leptin (LEP) gene (G2548A and A19G)
  • Leptin Receptor (LEPR) gene (Q223R and K109R)
MAF: The LEP A19G (0.74), G2548A (0.67) and LEPR K109R (061), Q223R (0.79). OR: N/A.
Finding of the study: The LEP and LEPR SNPs examined in this study may not serve as reliable markers for obesity in this Malaysian population. 		0.64
7Shamsuddin et al., 2018 [37]
  • G2548A
  • H1328080
  • A19G
MAF: (Case vs. Control): G22548A:(0.32 vs. 0.33), H1328080: (0.25 vs. 0.23), A19G: (0.26 vs. 0.29). OR: AAG haplotype of G2548A, H1328080, and A19G: 8.897 (95%CI: 1.59–49.78, p = 0.002).
Finding of the study: Haplotype AAG of G2548A, H1328080, and A19G conferred the significant association with obesity among Malay population in Terengganu.		0.05
8Mohanraj et al., 2022 [46]
  • Leptin (LEP) gene G2548A
  • Leptin Receptor (LEPR) gene Q223R
MAF: N/A OR: The association of the LEP G2548A and LEPR Q223R gene variants with BMI (overweight to morbidly obese) were not significant.
Finding of the study: While leptin (G2548A) and leptin receptor (Q223R) polymorphisms do not have a direct association with BMI or related factors in the population examined, other factors like gender, ethnicity, and psychological state significantly influence plasma leptin levels.		0.37
(b) Polygenic obesity
NoAuthorGene VariantsOutcomes (Minor Allele Frequency (MAF)/Odd Ratio (OR)Estimated Power of Study
1Yiew et al., 2010 [15]
  • PPAR L162V; PPAR2 C161T; UCP1
−3826A/G; UCP2 45 bp Ins/Del and −866G/A
  • UCP3 −55C/T SNPs
MAF: PPAR L162V (0.006); PPAR2 C161T (0.36); UCP1 −3826A/G (0.58); UCP2 −866G/A (0.12), 45 bp I/D (0.56) and UCP3 −55C/T (0.34). OR: N/A.
Finding of the study: None were associated with obesity.		0.07
2Chan et al., 2011 [16]
  • PYY gene (rs1058046) (T)
MAF: 0.45 N/A. OR: The mutated TT genotype and T allele were both not associated with obesity and the OR for obesity was 0.946 for those with T allele.
Finding of the study: R72T variant in PYY gene was not associated with obesity
and most of its related anthropometric measurements.		0.05
3Lisa et al., 2011 [17]
  • CARTPT rs2239670 (A)
MAF: 0.17. Unadjusted OR: 0.977 (95%CI: 0.639, 1.492, p = 0.913); Adjusted OR: 0.809 (95%CI: 0.511, 1.280, p = 0.365); Adjusted for age, gender, and ethnicity.
Finding of the study: CARTPT rs2239670 was not a predictor of obesity in the Malaysian subjects of this study.		0.05
4Apalasamy et al., 2012 [20]
  • Regions of the FTO gene
MAF: FTO gene polymorphisms ranged from 0.126 to 0.355. OR: N/A.
Finding of the study: No specific haplotype was significantly associated with an increased risk of obesity in Malaysian Malays.		0.54
5Chey et al., 2013 [21]
  • FTO rs9939609 (T)
MAF: 0.199. Unadjusted OR: 1.680 (95%CI: 1.036, 2.72, p = 0.035). Adjusted OR: 1.455 (95%CI: 0.874, 2.42, p = 0.149); Adjusted for age, gender, and ethnicity.
Finding of the study: No link found between this SNP and obesity or related traits, even though the MAF was highest among Malays.		0.12
6Appalasamy et al., 2014 [24]
  • ADIPOQ (rs3774261 and rs17366568)
MAF: rs3774261 (0.46), rs17366568 (0.04). OR: 2.15 (95%CI: 1.13–4.09, p = 0.026) and 0.87 (95%CI: 0.67–1.13, p = 0312), respectively.
Finding of the study: Only ADIPOQ rs17366568 polymorphism was associated with obesity.		0.75
7Apalasamy et al., 2014 [26]
  • Insulin-induced gene 2 (INSIG2) (rs7566605)
MAF: N/A. OR: N/A
Finding of the study: INSIG2 rs7566605 SNP is not an important variant in predisposing Malaysian Malays to obesity.		0.58
8Say et al., 2014 [27]
  • Uncoupling Protein 2 gene (UCP2) 45-bp I/D polymorphism
MAF: Overall (0.14), Malay (0.17), Chinese (0.12), Indian (0.21). OR: I/D genotype (2.02 (95%CI: 1.18, 3.45; p = 0.01), I allele (1.81 (95%CI: 1.15, 2.84, p = 0.01).
Finding of the study: UCP2 45-bp I/D polymorphism was associated with obesity and overall adiposity (total body fat percentage) among women in this cohort.		0.99
9Appalasamy et al., 2015 [28]
  • RETN (rs34861192, rs1862513, and rs3219175)
MAF: rs1862513 (0.46), rs3219175 (0.14), rs34861192 (0.15). OR: 0.86, 1.03, and 0.8, respectively (All OR values were not significant).
Finding of the study: The haplotypes of the RETN gene were not associated with obesity.		0.05
10Apalasamy et al., 2015 [29]
  • rs1042714 (Gln27Glu)
MAF: N/A. OR: N/A.
Finding of the study: rs1042714 polymorphism may play a key role in the development of obesity-related traits in Malaysian Malays, with gender influencing its impact on these traits.		N/A
11Chia, et al., 2015 [30]
  • PPAR genes
  • PPARδ T294C SNPs
  • PPARγ2 C161T
  • PPARα L162V
MAF: Overall: PPAR α L162V (0.08), PPARγ2 C161T (0.22) and PPARδ T294C (0.30), respectively. OR: No association was found between obesity and PPARα L162V, PPARγ2 C161T, and PPARδ T294C SNPs.
Finding of the study: None of the PPAR SNPs were associated with obesity or Metabolic syndrome in the suburban population of Kampar, Malaysia.		0.36
12Zain et al., 2015 [31]
  • NPY (rs16147 and rs5574)
MAF: NPY rs16147 T allele: (0.44 vs. 0.38, obese vs. control respectively); NPY rs5574 T allele: (0.28 vs. 0.33, obese vs. control respectively). OR: rs16147 T allele: 1.46 (95%CI: 1.02–2.07; p = 0.036), rs5574 T allele: 0.63 (95%CI: 0.46–0.86; p = 0.02).
Finding of the study: The rs16147 T allele contributed towards an increased risk of obesity, whereas the rs5574 T-allele conferred reduced risk.		0.34
13Zaharan et al., 2018 [32]
  • FABP2 rs1799883
  • β-3 adrenergic receptor gene ADRB3 (rs4994)
  • MC3R (rs3827103)
  • GHRL (rs696217)
  • vit D receptor (rs2228570)
MAF: rs1799883 (0.25), rs4994 (0.01), rs3827103 (0.25), rs696217 (0.08) and rs2228570 (0.33) OR: N/A.
Finding of the study: ADRB3 rs4994 and MC3R rs3827103 were associated with % body fat (BF).		0.55
14Zahri et al., 2016 [33]
  • Peroxisome proliferator-activated receptor g2 (PPARγ2) gene; Pro12Ala polymorphism
MAF: Pro12—obese (0.941), Non-obese (0.989). Ala12—obese (0.059), Non-obese (0.011). Unadjusted OR: Pro12Ala −5.30 (95%CI: 1.44–19.59, p = 0.012). Adjusted OR: 5.46 (95%CI: 0.27.0–23.40, p = 0.022) (adjusted for age, TG and LDL-C).
Finding of the study: The Pro12Ala polymorphism in the PPARγ2 gene predisposes Malay individuals to obesity, and the Ala12 allele may predict changes in lipid metabolism and adipocyte in this population.		1.00
15Shunmugam et al., 2016 [34]
  • Angiotensin-converting enzyme (ACE) genes
  • α-adrenergic receptor 2A (ADRA2A)
MAF: Overall = ADRA2A rs553668 (0.55), ACE I/D (0.56). Unadjusted OR: ACE II genotype and I allele: 2.15 (95%CI: 1.02–4.52, p =0.04) and 1.55 (95%CI: 1.05, 2.28), p = 0.03) respectively. Adjusted OR: 2.02 (95%CI: 0.87, 4.70, p = 0.10) and 1.46 (95%CI: 0.95, 2.26, p = 0.09) (adjusted for gender, age, and ethnicity).
Finding of the study: Subjects with both ADRA2A rs553668 GG and ACE I/D II genotypes had notably lower WHR compared to other genotype combinations which suggests ACE II genotype may serve as a protective factor against central adiposity risk.		0.52
16Kok et al., 2017 [35]
  • Interleukin-4 (IL4) intron 3 70 bp
  • Interleukin-1 receptor antagonist (IL1RA) intron 2 86 bp repeat
MAF: IL1RA (0.02) and IL4 (0.25). OR: IL1RA (I/II Genotype): 12.21 (95%CI: (2.54–58.79, p = 0.002), II Allele: 5.78 (95%CI: 1.73–19.29, p = 0.004) (adjusted for ethnicity).
Finding of the study: IL1RA intron 2 VNTR appears to be a strong genetic marker for overall adiposity status in the studied population.		0.05
17Rahmadhani et al., 2017 [36]
  • BsmI (rs1544410) in the intronic region of the VDR gene
MAF: N/A. Adjusted OR: GA genotype: 1.44 (95%CI: 0.77–1.91, p = 0.42). AA genotype: 1.21 (95%CI: 0.60–3.46, p = 0.40). A allele of the VDR BsmI SNP: 1.21 (95%CI: 0.86–1.70, p = 0.28). Adjustment made for gender, ethnicity, maternal education and puberty stage.
Finding of the study: VDR BsmI polymorphism was not associated with obesity.		0.84
18Mitra et al., 2018 [38]
  • FTO (rs9930506)
MAF: 0.365. G allele is the minor allele among the studied ethnic groups, particularly in the Indian compared to Chinese and Malays population (78% vs 49.2%, p < 0.001). Unadjusted OR: GG vs. AA: 2.65 (95%CI: 1.09–6.45, p = 0.032). Adjusted OR: 2.87 (95%CI: 1.14–7.19, p = 0.025) adjusted for age, sex, physical activity, smoking, and alcohol use.
Finding of the study: The risk allele (G) of FTO rs9930506 was not associated with a higher risk of obesity.		N/A
19Lek et al., 2018 [39]
  • DRD2 Taq1A, Taq1B and Taq1D gene polymorphisms
MAF: Taq1A, Taq1B and Taq1D = Chinese (0.37, 0.39, 0.06), Indian (0.29, 0.28, 0.30). OR: The DRD2 Taq1A, Taq1B, and Taq1D genotypes and alleles showed no overall association with BMI, total body fat, or waist-hip ratio classes. However, the Taq1D D2 allele was linked to a 0.55 times lower risk of high central adiposity (WHR) compared to the D1 allele (OR: 0.55 (95%CI: 0.33–0.93, p = 0.03).
Finding of the study: DRD2 Taq1 gene polymorphisms influence eating behavior and preference, intake frequency, and craving for high-fat foods in Malaysian adults, but their impact on obesity and adiposity remains inconclusive.		0.05
20Chong et al., 2018 [40]
  • IRX3 (rs3751723)
MAF: N/A. OR: homozygous G/G vs. T/T: 1.72 (95%CI = 1.02–2.91, p < 0.05)
Finding of the study: The G/G genotype was linked to a higher obesity risk in non-fast-food consumers. The G allele increased overweight risk in Malaysian females but was protective in smokers. However, meta-analysis found no significant association between the IRX3 rs3751723 polymorphism and obesity.		0.97
21Chong et al., 2018 [41]
  • Fatty acid synthase (FASN) gene (rs2229422, rs2228305, rs2229425, and rs4246445)
MAF: rs4246445 (0.363), rs2229422 (0.186), rs2228305 (0.022) and rs2229425 (0.003). OR: N/A.
Finding of the study: The four SNPs were independent to each other, and not all of the haplotypes identified were significantly associated with overweight and obesity in this study.		0.08
22Mitra et al., 2019 [42]
  • ADRB2 (rs1042713)
MAF: 0.49 OR: No significant association between ADRB2 rs1042713 and obesity (obesity as defined by BMI ≥ 27.5 kg/m2) under codominant (AG: 1.26, (95%CI: 0.59–2.71, p = 0.548) and GG: 0.94, (95%CI: 0.40–2.23, p = 0.884), dominant: 1.14, (95%CI: 0.56–2.33, p = 0.725), and recessive: 0.80, (95%CI: 0.40–1.61, p = 0.538) models, after adjusting for covariates age, gender, physical activity status, smoking status, and alcohol consumption.
Finding of the study: No link was found between ADRB2 rs1042713 and obesity in Malaysian adults; however, it was associated with insulin resistance.		0.47
23Al-Shajrawi et al., 2020 [43]
  • NFKB1 (rs28362491)
  • HIF1 (rs11549465)
MAF: N/A. Genotypes: HIF-1 (rs11549465); CC: 84% and CT: 16% NFKB1 (rs28362491); Ins/Ins: 25%, Ins/Del: 44.7% and Del/Del: 30.3%. OR: N/A
Finding of the study: A significant association was found between NFKB1 rs28362491 and obesity (p = 0.002). Combination of CC for rs11549465 and Ins/Ins for rs28362491 were significant predictors for obesity, alongside waist circumference and LDL levels in the study population.		0.05
24Lim et al., 2020 [44]
  • Dopamine receptor gene variants (DRD2/ANKK1) Taq1A (rs1800497), DRD2 Taq1B (rs1079597) and DRD2 Taq1D (rs1800498))
MAF: Taq1A rs1800497 (0.38), Taq1B rs1079597 (0.39), Taq1D rs1800498 (0.08). OR: N/A
Finding of the study: The ANKK1/DRD2 Taq1A gene variant may significantly impact emotional eating in Malaysian adults. However, no association was found between DRD2/ANKK1 variants and obesity.		N/A
25Tan et al., 2020 [45]
  • FTO (rs9932754, rs9930501 and rs9930506)
  • ADRB2 (rs1042714 and rs1042713)
MAF: rs9930506 (0.37), rs993050l (0.37), rs9932754 (0.37), and rs1042713 (0.13). OR: 2.87 (95%CI: 1.14–7.19), 3.03 (95%CI: 1.23–7.49), 3.04 (95%CI: 1.22–7.59) and 1.38 (95%CI: 0.08–23.93), respectively.
Finding of the study: The highest tertile of polygenic risk score was significantly linked to increased odds of elevated C-reactive protein concentrations, indicating that individuals with a greater number of obesity-related risk alleles tend to have higher CRP levels.		0.85
26Ching et al., 2023 [47]
  • Fatty acid desaturase 1 (FADS1) gene (rs174547)
MAF: N/A OR: rs174547 and fiber intake was significant for vegetarians with the TT genotype at tertile 2 fiber intake after adjusting for age, sex, ethnicity, and food groups, OR: 4.71 (95%CI: 1.25–17.74, p = 0.022).
Finding of the study: s174547 SNP in the FADS1 gene significantly interacts with fiber intake in relation to abdominal obesity among middle-aged Malaysian vegetarians, specifically those with the TT genotype.		0.29
Table 3. GO Biological Process and KEGG pathways of genes associated with monogenic obesity (POMC, MC4R, LEP, and LEPR).
Table 3. GO Biological Process and KEGG pathways of genes associated with monogenic obesity (POMC, MC4R, LEP, and LEPR).
GO Biological ProcessStrengthFalse Discovery Rate (FDR)
Response to melanocyte-stimulating hormone3.520.0013
Leptin-mediated signaling pathway2.950.0056
Regulation of appetite2.650.0130
Regulation of feeding behavior2.580.0114
Bone growth2.640.0133
Regulation of bone remodeling2.60.00029
Response to dietary excess2.590.0144
Regulation of endocrine processes2.40.019
Energy reserve metabolic process2.470.00032
Insulin secretion2.440.0247
Regulation of gluconeogenesis2.410.0257
KEGG Pathways
Adipocytokine signaling pathway2.343.01 × 10 −5
AMPK signaling pathway2.040.0253
Non-alcoholic fatty liver disease1.950.0280
JAK-STAT signaling pathway1.920.0280
Neuroactive ligand-receptor interaction1.782.69 × 10−5
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hamzah, S.S.; Ahmad Zamri, L.; Abu Seman, N.; Zainal Abidin, N.A. Genetic Variants of Obesity in Malaysia: A Scoping Review. Genes 2024, 15, 1334. https://doi.org/10.3390/genes15101334

AMA Style

Hamzah SS, Ahmad Zamri L, Abu Seman N, Zainal Abidin NA. Genetic Variants of Obesity in Malaysia: A Scoping Review. Genes. 2024; 15(10):1334. https://doi.org/10.3390/genes15101334

Chicago/Turabian Style

Hamzah, Siti Sarah, Liyana Ahmad Zamri, Norhashimah Abu Seman, and Nur Azlin Zainal Abidin. 2024. "Genetic Variants of Obesity in Malaysia: A Scoping Review" Genes 15, no. 10: 1334. https://doi.org/10.3390/genes15101334

APA Style

Hamzah, S. S., Ahmad Zamri, L., Abu Seman, N., & Zainal Abidin, N. A. (2024). Genetic Variants of Obesity in Malaysia: A Scoping Review. Genes, 15(10), 1334. https://doi.org/10.3390/genes15101334

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop