Association Between Angiotensin Converting Enzyme Insertion / Deletion Genotypes and Diabetic Nephropathy Defined by Urinary Albumin-to-Creatinine Ratio: A Systematic Review and Meta-Analysis

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Obuku" }, { "@type": "Person", "name": "Gertrude N. Kiwanuka" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Diabetic nephropathy (DN) is a major complication of type 2 diabetes mellitus (T2DM) and a leading cause of kidney failure. Evidence on the influence of ACE I/D polymorphisms in DN risk is inconsistent across populations. Methods A systematic review and meta-analysis was conducted following the PRISMA 2020 guidelines. Studies published between January 1990 to February 2025 were retrieved from PubMed, EMBASE and Web of Science. Eligible observational studies reported the frequency of ACE genotypes with DN in T2DM. Independent reviewers screened studies using Rayyan software, extracted data, and assessed risk of bias using the ROBINS-E tool. Reporting on the quality of studies was determined using the STREGA guidelines. Pooled odds ratio (OR) with 95% confidence intervals (CI) were calculated using random-effects models in R version 4.4.2. Subgroup, meta-regression, and sensitivity analyses addressed heterogeneity; Egger’s test assessed publication bias. Registered in PROSPERO (CRD42024577680). Funding from Fogarty International Center of the National Institutes of Health (D43TWO11632). Results Of the 46 studies included in this review, the combined sample size was 16,322 participants. The majority of studies (29 out of 46) were conducted in Asia. Only 5 studies reported DN–related comorbidities by ACE genotypes and one assessed mortality. Twenty-five of the included 46 studies contributed data to the meta-analysis. The ACE II genotype was protective against DN; II vs. ID [OR= 0.70 (CI: 0.63–0.77)] and II vs. DD [OR= 0.68 (CI: 0.55–0.84)]; Heterogeneity was (I2 = 71.7%, τ2 = 0.1776, p < 0.0001). Stronger associations were observed in studies using urinary Albumin-Creatinine-Ratio over Albumin-Excretion-Rate. Egger’s test showed no publication bias (p = 0.55). Conclusion The ACE II genotype is significantly protective against DN risk in T2DM. Standardization of urinary albumin measurement and further genotype-phenotype studies are needed to strengthen clinical utility of the ACE I/D polymorphisms. 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F1000Research 2025, 14 :697 ( https://doi.org/10.12688/f1000research.166660.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Association Between Angiotensin Converting Enzyme Insertion / Deletion Genotypes and Diabetic Nephropathy Defined by Urinary Albumin-to-Creatinine Ratio: A Systematic Review and Meta-Analysis [version 1; peer review: awaiting peer review] Ritah Kiconco https://orcid.org/0000-0003-0194-2828 1,2 , Robert Kalyesubula 3 , Jazira Tumusiime https://orcid.org/0009-0006-0015-4327 1 , [...] Raymond Atwine 4 , Charles Nkubi Bagenda https://orcid.org/0009-0006-4235-1003 1 , Simon Peter Rugera 1 , Bosco Bekiita Agaba 1,5 , Erick Nyakundi Ondari https://orcid.org/0000-0002-1498-6486 6 , Deusdedit Tusubira https://orcid.org/0000-0002-4698-424X 7 , Martha Lyaka https://orcid.org/0000-0002-7993-613X 8 , Moses Ocan https://orcid.org/0000-0002-8852-820X 9 , Alison Annet Kinengyere 8,9 , Ekwaro A. Obuku https://orcid.org/0000-0001-5311-2346 9,10 , Gertrude N. Kiwanuka 7 Ritah Kiconco https://orcid.org/0000-0003-0194-2828 1,2 , Robert Kalyesubula 3 , [...] Jazira Tumusiime https://orcid.org/0009-0006-0015-4327 1 , Raymond Atwine 4 , Charles Nkubi Bagenda https://orcid.org/0009-0006-4235-1003 1 , Simon Peter Rugera 1 , Bosco Bekiita Agaba 1,5 , Erick Nyakundi Ondari https://orcid.org/0000-0002-1498-6486 6 , Deusdedit Tusubira https://orcid.org/0000-0002-4698-424X 7 , Martha Lyaka https://orcid.org/0000-0002-7993-613X 8 , Moses Ocan https://orcid.org/0000-0002-8852-820X 9 , Alison Annet Kinengyere 8,9 , Ekwaro A. Obuku https://orcid.org/0000-0001-5311-2346 9,10 , Gertrude N. Kiwanuka 7 PUBLISHED 17 Jul 2025 Author details Author details 1 Department of Medical Laboratory Science, Mbarara University of Science and Technology, Mbarara, Western Region, Uganda 2 Department of Biochemistry, Soroti University, Soroti, Eastern Region, Uganda 3 Departments of Physiology and Internal Medicine, Makerere University, Kampala, Central Region, Uganda 4 Department of Pathology, Mbarara University of Science and Technology, Mbarara, Western Region, Uganda 5 Malaria Division, Ministry of Health, Kampala, Uganda 6 Department of Biological Sciences, Kisii University, Kisii, Kisii County, Kenya 7 Department of Biochemistry, Mbarara University of Science and Technology, Mbarara, Western Region, Uganda 8 Sir Albert Cook Memorial Library, Makerere University, Kampala, Central Region, Uganda 9 Africa Center for Systematic Reviews and Knowledge Translation, College of Health Sciences, Makerere University, Kampala, Central Region, Uganda 10 Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, University of London, London, England, UK Ritah Kiconco Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Robert Kalyesubula Roles: Conceptualization, Formal Analysis, Funding Acquisition, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing Jazira Tumusiime Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Visualization, Writing – Review & Editing Raymond Atwine Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing – Review & Editing Charles Nkubi Bagenda Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Visualization, Writing – Review & Editing Simon Peter Rugera Roles: Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Resources, Software, Validation, Writing – Review & Editing Bosco Bekiita Agaba Roles: Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing – Review & Editing Erick Nyakundi Ondari Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing – Review & Editing Deusdedit Tusubira Roles: Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – Review & Editing Martha Lyaka Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing – Review & Editing Moses Ocan Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Review & Editing Alison Annet Kinengyere Roles: Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Review & Editing Ekwaro A. Obuku Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Review & Editing Gertrude N. Kiwanuka Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW This article is included in the Genomics and Genetics gateway. Abstract Background Diabetic nephropathy (DN) is a major complication of type 2 diabetes mellitus (T2DM) and a leading cause of kidney failure. Evidence on the influence of ACE I/D polymorphisms in DN risk is inconsistent across populations. Methods A systematic review and meta-analysis was conducted following the PRISMA 2020 guidelines. Studies published between January 1990 to February 2025 were retrieved from PubMed, EMBASE and Web of Science. Eligible observational studies reported the frequency of ACE genotypes with DN in T2DM. Independent reviewers screened studies using Rayyan software, extracted data, and assessed risk of bias using the ROBINS-E tool. Reporting on the quality of studies was determined using the STREGA guidelines. Pooled odds ratio (OR) with 95% confidence intervals (CI) were calculated using random-effects models in R version 4.4.2. Subgroup, meta-regression, and sensitivity analyses addressed heterogeneity; Egger’s test assessed publication bias. Registered in PROSPERO (CRD42024577680). Funding from Fogarty International Center of the National Institutes of Health (D43TWO11632). Results Of the 46 studies included in this review, the combined sample size was 16,322 participants. The majority of studies (29 out of 46) were conducted in Asia. Only 5 studies reported DN–related comorbidities by ACE genotypes and one assessed mortality. Twenty-five of the included 46 studies contributed data to the meta-analysis. The ACE II genotype was protective against DN; II vs. ID [OR= 0.70 (CI: 0.63–0.77)] and II vs. DD [OR= 0.68 (CI: 0.55–0.84)]; Heterogeneity was (I 2 = 71.7%, τ 2 = 0.1776, p < 0.0001). Stronger associations were observed in studies using urinary Albumin-Creatinine-Ratio over Albumin-Excretion-Rate. Egger’s test showed no publication bias (p = 0.55). Conclusion The ACE II genotype is significantly protective against DN risk in T2DM. Standardization of urinary albumin measurement and further genotype-phenotype studies are needed to strengthen clinical utility of the ACE I/D polymorphisms. READ ALL READ LESS Keywords Diabetic Nephropathy, Mutations, Angiotensin Converting Enzyme Gene, Type 2 Diabetes Mellitus. Corresponding Author(s) Ritah Kiconco ( [email protected] ) Close Corresponding author: Ritah Kiconco Competing interests: No competing interests were disclosed. Grant information: The research reported in this publication was supported by the Fogarty International Center of the National Institutes of Health under Award Number D43TWO11632. The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Kiconco R et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Kiconco R, Kalyesubula R, Tumusiime J et al. Association Between Angiotensin Converting Enzyme Insertion / Deletion Genotypes and Diabetic Nephropathy Defined by Urinary Albumin-to-Creatinine Ratio: A Systematic Review and Meta-Analysis [version 1; peer review: awaiting peer review] . F1000Research 2025, 14 :697 ( https://doi.org/10.12688/f1000research.166660.1 ) First published: 17 Jul 2025, 14 :697 ( https://doi.org/10.12688/f1000research.166660.1 ) Latest published: 17 Jul 2025, 14 :697 ( https://doi.org/10.12688/f1000research.166660.1 ) Introduction Approximately 20–50% of people with diabetes worldwide suffer from diabetic nephropathy (DN), which is among the most prevalent and costly microvascular consequences of long-term type 2 diabetes mellitus (T2DM). 1 – 3 Prolonged albuminuria, decreased glomerular filtration rate, and progressive kidney damage are hallmarks of DN, which eventually results in kidney failure. 4 While microalbuminuria, commonly assessed via the urine albumin-to-creatinine ratio (UACR), remains a widely accepted early biomarker of diabetic nephropathy (DN), 5 several other biomarkers have demonstrated potential for early detection. These include neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and cystatin C, which are often elevated before albumin appears in urine and may reflect early tubular injury or impaired glomerular filtration. 6 – 8 Despite their clinical promise, these alternative biomarkers are not yet widely adopted in routine practice because of limitations such as high assay costs, limited availability of standardized kits, and the need for a specialized laboratory infrastructure. 9 , 10 In contrast, microalbuminuria remains a practical and accessible option in most clinical settings, especially in low- and middle-income countries, owing to the affordability and availability of UACR testing. 11 – 15 The pathophysiology of DN is complex, with hyperglycemia-induced reactive oxygen species, inflammation, and genetic predispositions playing pivotal roles. 16 , 17 Angiotensin-converting enzyme (ACE), a key component of the renin-angiotensin-aldosterone system (RAAS), has been implicated in the development of DN because it is involved in the regulation of blood pressure and fluid balance. ACE gene insertion/deletion (I/D) polymorphism, which results in different genotypes insertion/insertion (II), insertion/deletion (ID) and deletion/deletion (DD), has been extensively studied as a potential genetic determinant of high blood pressure and DN risk. 18 – 20 Evidence suggests that the ACE DD genotype is associated with increased ACE activity, leading to higher susceptibility to DN and its complications, whereas the II genotype may have a protective effect. 21 , 22 Despite substantial research on diabetic nephropathy (DN), critical gaps remain in our understanding of the interplay between angiotensin-converting enzyme (ACE) insertion/deletion (I/D) genotypes and DN progression in type 2 diabetes mellitus (T2DM). While numerous studies have reported associations between ACE I/D polymorphisms and DN risk, the findings are inconsistent owing to variations in study design, population, and methods of microalbuminuria (UACR) measurement. 23 – 26 The heterogeneity of these results could hinder the development of standardized clinical guidelines for the genetic risk assessment of DN. Therefore, we conducted this systematic review and meta-analysis primarily to determine the association between Angiotensin Converting Enzyme (ACE) genotypes and Diabetic Nephropathy (DN) defined by Urine Albumin-to-Creatinine Ratio (UACR) in adults with Type 2 Diabetes Mellitus (T2DM). Secondary outcomes included DN-related mortality and comorbidities. Methods Registration and protocol This systematic review and meta-analysis study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 27 The protocol was registered with PROSPERO 28 under registration number CRD42024577680 and is published as a Preprint in Open science Framework (OSF) preprints. 29 Eligibility criteria Studies were eligible for inclusion if they enrolled male and female participants aged 18 years or older with a confirmed diagnosis of type 2 diabetes mellitus (T2DM), and investigated angiotensin-converting enzyme (ACE) insertion/deletion (I/D) genotypes (II, ID, DD) as the primary exposure. Diabetic nephropathy (DN) was the primary outcome, commonly defined using microalbuminuria markers such as the albumin excretion rate (AER), urine albumin-to-creatinine ratio (UACR), or albumin-to-creatinine ratio (ACR). Diagnostic thresholds for microalbuminuria were standardized across studies at 30–300 mg/day for AER and 30–300 mg/g for UACR/ACR. One study that defined DN using traditional renal function tests, including estimated glomerular filtration rate (eGFR) and serum creatinine, was also included to capture broader clinical definitions of nephropathy. This deviation from the protocol was noted but deemed valuable for completeness. The comparator across studies was typically T2DM patients with normal UACR levels, enabling evaluation of the association between ACE genotypes and DN risk. Secondary outcomes included nephropathy-related mortality and comorbidities such as cardiovascular disease, hypertension, neuropathy, and retinopathy. Eligible studies employed observational designs (cohort, case-control, or cross-sectional) and were published between January 1990 and February 2025. Articles with missing data on key study variables or those that did not explore the ACE I/D-DN relationship were excluded. Non-English articles whose abstracts were available in English were included in the initial screening; however, those whose full texts were not available for retrieval were eventually excluded. Abstracts, unpublished studies, and grey literature were not included in this review, and no direct contact was made with study authors for clarification or additional data. Discrepancies were resolved through discussion, ensuring consistency and minimizing bias, although formal blinding and interrater reliability statistics were not applied. Information sources We conducted a comprehensive search on across multiple electronic databases (PubMed, Web of Science, and EMBASE) to identify studies relevant to the research objectives. Additional studies were retrieved from Google Scholar and reference lists of the included articles to ensure broad coverage. Search strategy The search strategy was designed to capture studies assessing the relationship between angiotensin-converting enzyme (ACE) insertion/deletion (I/D) genotypes and diabetic nephropathy (DN). The search included studies published from 1 st January 1, 1990, to 28 th February 28, 2025, without language restrictions. Keywords and medical subject headings (MeSH) related to “ACE I/D genotypes,” “diabetic nephropathy,” and “microalbuminuria” were combined with Boolean Operators “OR” and “AND” “AND” and truncated where applicable to optimize retrieval. The search strings used are listed in Table 1 . Table 1. Search strategy and database hits for the systematic review and meta-analysis on ACE I/D genotypes and diabetic nephropathy. Database Search date Search string #Hits PubMed 28 th February, 2025 ((“Type 2 Diabetes Mellitus” OR “T2DM” OR “Diabetes Mellitus, Type 2”[tiab]) AND (“Diabetic Nephropathies” OR “Diabetic Nephropathy” OR “DN” OR “Nephropathy” OR “Kidney Disease") AND (“Angiotensin-Converting Enzyme” OR “ACE” OR “ACE I/D” OR “ACE Insertion/Deletion Polymorphism” OR “ACE Genotypes”) AND (“Urine Albumin-to-Creatinine Ratio” OR “UACR” OR “Microalbuminuria” OR “Albuminuria”) AND (“Mortality” OR “Comorbidities” OR “Complications") AND (“Genetic Markers” OR “Polymorphism, Genetic” OR “Biomarkers”)) 48 EMBASE 28 th February, 2025 ((“Type 2 Diabetes Mellitus” or T2DM or “Diabetes Mellitus Type 2”) and (“Diabetic Nephropathies” or “Diabetic Nephropathy” or DN or Nephropathy or “Kidney Disease”) and (“Angiotensin-Converting Enzyme” or ACE or “ACE I/D” or “ACE Insertion/Deletion Polymorphism” or “ACE Genotypes”) and (“Urine Albumin-to-Creatinine Ratio” or UACR or Microalbuminuria or Albuminuria) and (Mortality or Comorbidities or Complications) and (“Genetic Markers” or Polymorphism or Genetic or Biomarkers)).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword heading word, floating subheading word, candidate term word] 15 Web of Science 28 th February, 2025 TS = ((“Type 2 Diabetes Mellitus” OR T2DM OR “Diabetes Mellitus Type 2”) AND (“Diabetic Nephropathy” OR “Diabetic Nephropathies” OR DN OR Nephropathy OR “Kidney Disease”) AND (“Angiotensin-Converting Enzyme” OR ACE OR “ACE I/D” OR “ACE Insertion/Deletion Polymorphism” OR “ACE Genotypes”) AND (“Urine Albumin-to-Creatinine Ratio” OR UACR OR Microalbuminuria OR Albuminuria) AND (Mortality OR Comorbidities OR “Hypertension” OR “Cardiovascular Disease”) AND (“Genetic Markers” OR Polymorphism OR Genetic OR Biomarkers) AND TS = (Cohort OR “Case-Control” OR “Cross-Sectional” OR “Observational Study” OR “Randomized Controlled Trial” OR RCT) AND TS = (Adult OR “18 years and above”) NOT TS = (“Type 1 Diabetes” OR “Gestational Diabetes” OR Prediabetes OR “Case Report” OR Review OR Editorial OR “Animal Study”)) 13 Two independent experienced information retrieval specialists (AAK and ML), in consultations with the lead reviewer (RK), independently conducted the database searches and verified the inclusion of additional studies from alternative sources such as Google Scholar and reference lists. Discrepancies in the search results were resolved through consensus discussion. The systematic review process was organized using Rayyan, 30 a web-based platform that facilitates the filtering and assessment of papers based on predetermined requirements for inclusion and exclusion. Selection process Two reviewers, RK and JT, independently assessed the titles and abstracts of the studies according to the inclusion criteria using Rayyan software. Full-text articles were retrieved from studies that met the eligibility requirements. Any disagreements regarding inclusion were resolved through a consensus between the two reviewers. Full-text articles were retrieved from eligible studies, and the selection process is shown in Figure 1 . Figure 1. Schematic diagram of article selection process for inclusion in the systematic review. This flowchart illustrates the stepwise criteria used to screen and select articles for inclusion in the systematic review. The selection process involved four major steps: (1) assessing general relevance based on population, study design, and setting; (2) evaluating the presence of key exposures and biomarkers (i.e., ACE I/D polymorphisms and UACR); (3) confirming the reporting of diabetic nephropathy and related outcomes (e.g., mortality, comorbidities); and (4) applying exclusion criteria such as type 1 diabetes (T1DM), gestational diabetes (GDM), prediabetes, purely genetic/laboratory studies lacking clinical outcomes, and non-original articles (e.g., reviews or editorials). Articles were classified as “Include,” “Maybe,” or “Exclude” depending on how fully they met the criteria. Abbreviations : T1DM – Type 1 diabetes mellitus; T2DM – Type 2 diabetes mellitus; GDM – Gestational diabetes mellitus; ACE – Angiotensin-converting enzyme; I/D – Insertion/deletion; RCT – Randomized Controlled Trial; UACR – Urine albumin-to-creatinine ratio. Data collection process Data extraction was performed independently by two authors, RK and JT, using a standardized data extraction form in Microsoft excel. The extracted data included study characteristics (author, year, population, and design, age, sex and clinical characteristics such as glycated hemoglobin levels, blood pressure, T2DM duration, body mass index), exposure details (ACE I/D genotypes), comparators (UACR measurement methods), outcomes (DN, mortality, and comorbidities), and statistical measures such as the frequency counts were also retrieved and computed in to Odds ratio with confidence intervals. Discrepancies were resolved through discussion with GNK and RA. Data items The data extraction captured a broad range of information including details on study design such as case control, cross-sectional or cohort, sample size and distribution between males and females, age, genotyping methods such as PCR, UACR or other DN diagnostic approaches, and outcome reporting. Genotype frequencies were entered in Excel, and odds ratio (OR) with 95% confidence intervals (CI) were calculated using R software version 4.4.2. Secondary outcomes such as comorbidities and mortality were recorded as binary variables (Yes/No) based on whether a particular study explicitly reported on them. No missing data were encountered during the meta-analysis, as only studies with complete and extractable data were included in the quantitative synthesis. Studies lacking sufficient data, as identified during visual inspection of the compiled Excel sheet, were excluded from meta-analysis and instead included in the narrative synthesis. Risk of bias assessment The risk of bias in individual studies was assessed using the ROBINS-E tool, 31 which evaluates bias across key domains: confounding, selection bias, classification of interventions, and outcome assessment. Confounding was assessed as part of the ROBINS-E risk of bias evaluation, where considerations were made on whether studies appropriately controlled for potential confounders such as age, sex, duration of diabetes, and comorbidities. Where studies reported such adjustments or stratifications, these were documented and factored into the overall bias assessment. Studies were assigned an overall remark of either a low, moderate or some concerns, or high risk of bias. The risk of the bias assessments as depicted by the robvis tool 32 are shown in Figure 2 . The results of the bias assessment are available as Extended data. The ROBINS-E risk of bias assessment was conducted independently by CNB and SPR. Figure 2. Bar plot summary of domain-level risk of bias judgements using the ROBINS-E tool. This bar plot illustrates the proportion of studies judged at each level of risk of bias across the seven ROBINS-E domains and overall, using the robvis tool. Most domains had a high proportion of low-risk ratings, particularly for confounding, exposure measurement, and participant selection. However, bias due to missing data and selective outcome reporting were the most frequent high-risk areas. In the “Overall risk of bias” domain, the bar plot shows approximately 60% of studies as low risk, which aligns with the actual count of 25 out of 46 studies (54%) that were included in the meta-analysis based on their low-risk rating. Effect measures We included 25 studies in the meta-analysis to assess the association between ACE I/D genotypes and diabetic nephropathy risk. For the primary outcome, diabetic nephropathy (DN) assessed using urine albumin to creatinine ratio or renal function tests, odds ratio (OR) with 95% confidence intervals (CI) were used as the effect measure to compare the risk of DN across ACE I/D genotypes (II vs. ID and II vs. DD). This binary outcome metric was selected due to the categorical nature of the genotype data and the case or non-case definition of DN. Odds ratio OR were calculated using genotype frequency data. Secondary outcomes such as comorbidities (hypertension, cardiovascular disease) and mortality were synthesized narratively, and their presence (Yes) or absence (No) was recorded per study. Synthesis methods Study selection and eligibility for synthesis Studies were deemed eligible for synthesis if they included adult patients with T2DM, assessed ACE I/D polymorphisms and reported DN outcomes using laboratory-based criteria. The review included studies that assessed DN using albumin excretion rate (AER) or microalbuminuria, albumin-to-creatinine ratio (ACR), or renal function markers such as serum creatinine and estimated glomerular filtration rate (eGFR). These criteria allowed for flexible yet clinically relevant definitions of nephropathy. Study characteristics and outcome definitions were tabulated to determine synthesis eligibility. Studies without sufficient genotype data or outcome definitions were excluded. Data preparation and conversion Data preparation involved standardizing genotype frequency data across included studies. Frequencies for ACE I/D genotypes were extracted and used to calculate odds ratio (OR) with corresponding 95% confidence intervals (CI). For meta-analysis, all effect sizes were transformed into log odds ratios and back-transformed for interpretation. Secondary outcomes such as comorbidities and mortality were recorded as binary variables (reported vs. not reported) in Excel spreadsheets. Reporting study outcomes For the primary outcome, diabetic nephropathy (DN), the standardized metric used was the odds ratio (OR) with corresponding 95% confidence intervals (CI), comparing DN prevalence across ACE I/D genotypes (II, ID, DD). OR were chosen because they are appropriate for summarizing associations in observational studies. Genotype frequencies from individual studies were extracted into Microsoft Excel, and OR were calculated using R software (version 4.4.2). These OR were log-transformed for meta-analysis to stabilize variance and ensure normality assumptions. The pooled estimates were then back-transformed to the original OR scale. For secondary outcomes, such as mortality, cardiovascular disease, hypertension, neuropathy, and retinopathy, data were extracted into Excel as binary indicators (Yes/No) depending on whether each study reported on the specific outcome. Statistical models The primary effect measure used was the odds ratio (OR) for the association between ACE I/D genotypes and DN. Random-effects models were used for all meta-analyses to account for between-study heterogeneity in population, study design, and UACR diagnostic methods. Statistical significance was determined at p < 0.05. Heterogeneity was assessed using the I 2 and τ 2 statistics. Meta-regression was performed to explore heterogeneity sources, particularly differences in urinary albumin measurement methods (ACR vs. AER). All statistical estimates were generated using R software (version 4.4.2). Presentation of results Forest plots were generated to visually present pooled OR and 95% CI. Individual study estimates and weights were shown. Funnel plots and Egger’s regression tests assessed publication bias. All statistical visualizations and analyses were produced in R software. Reporting bias was examined via funnel plots and Egger’s test; no significant asymmetry was detected. Subgroup and sensitivity analyses To explore sources of heterogeneity, subgroup analyses were conducted based on urinary albumin measurement methods (ACR vs. AER) of the included studies. Meta-regression was used to assess the potential influence of these study-level characteristics on effect size estimates. Sensitivity analyses were conducted using a leave-one-out approach, in which each study was sequentially removed from the meta-analysis to evaluate the robustness and stability of the pooled effect estimates. Reporting bias assessment Selective outcome reporting and qualitative assessments were done through quality appraisal of the studies conducted using the STREGA reporting guidelines. KR and DT assessed adherence, with discrepancies resolved by ENO and BBA. Studies were rated as fair, good, very good, or excellent. Excellent studies provided thorough methodological and genotyping details; very good ones had minor gaps; good studies had moderate reporting deficiencies; and fair studies lacked core STREGA elements. The full results of STREGA appraisal are provided in the Extended data. Certainty assessment We did not apply the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework, 33 which is best suited for intervention studies. Instead, we appraised the certainty of evidence by triangulating risk of bias (via ROBINS-E), reporting quality of studies (via STREGA), consistency, precision of pooled estimates, and the direction of effects across studies. Results Study selection The initial search across PubMed, EMBASE, Web of Science, Google Scholar, and reference lists yielded 968 records. After 189 duplicates were removed, 779 unique records were screened for eligibility. During the screening process, 721 records were excluded based on the title and abstract review. A total of 58 full-text articles were retrieved, all of which were successfully accessed. After full-text assessment, 12 studies were excluded due to incorrect population (n = 2), wrong outcome (n = 9), or wrong publication type (n = 1). Ultimately, 46 studies met the inclusion criteria and were included in this systematic review and meta-analysis. The complete screening and selection process is summarized in the PRISMA 2020 flowchart 34 as shown in Figure 3 . Figure 3. PRISMA 2020 flow diagram for study selection. This flow diagram outlines the process of identifying, screening, and including studies in the review. A total of 968 records were identified through database searches and reference lists: PubMed (n = 48), Embase (n = 15), Web of Science (n = 13), Google Scholar (n = 108), and reference lists (n = 784). After removing 189 duplicates, 779 records were screened. Of these, 721 were excluded based on title and abstract screening, and 58 full-text reports were assessed for eligibility. Twelve reports were excluded due to; wrong population (n = 2), wrong outcome (n = 9), or wrong publication type (n = 1). Ultimately, 46 studies were included in the final review. Exclusion of ineligible studies After full-text review, 12 studies that initially appeared to meet inclusion criteria were excluded for the following reasons: a) Wrong publication type (n = 1): Panagiotopoulos et al., 35 was a narrative review rather than a primary research study. b) Wrong outcome (n = 9): These studies, including Wong et al., 36 investigated ACE polymorphisms however, interest was with macroangiopathy and not type 2 diabetes mellitus associated nephropathy; Ahluwalia et al., 37 and Pai et al., 38 assessed ACE polymorphisms not as insertion/deletion SNPs. Fujisawa et al., 39 presented ACE I/D polymorphisms data in relation to retinopathy and myocardial infarction but not diabetic nephropathy (DN). Taha et al., 40 assessed ACE polymorphisms in type 2 diabetes mellitus patients but did not stratify them into nephropathy cases and controls. Araz et al., 41 ACE ID, DD, II were not distributed across UACR/albuminuric groups. Canani et al., 42 ACE DD and ID genotypes were not reported independently. Wong et al., 43 distribution of ACE I/D across different DN categories determined by UACR/Microalbuminuria/AER was not done. Draman et al., 44 microalbuminuria/UACR was not measured. c) Wrong Population (n = 2): Pontremoli et al., 45 focused on essential Hypertension and Al-Harbi et al., 46 in the methods section of the full text did not specify the population as type 2 diabetes mellitus (T2DM) but unrelated adult patients. These studies were excluded to ensure consistency with our predefined Population Exposure Comparator Outcome (PECO) criteria and focus on clinically relevant DN outcomes in adults with T2DM. Study characteristics We followed the Centre for Reviews and Dissemination (CRD) approach 47 and the Synthesis Without Meta-analysis (SWiM) guidelines 48 due to their detailed and structured framework. Forty-six (46) observational studies investigating the association between ACE I/D polymorphisms and DN in patients with T2DM were examined. The total sample size across all studies was 16,322 participants, with individual study populations ranging from 50 to 3744 participants. The combined mean of mean age in the studies was 58.11 (±4.30). The combined median of mean numbers of males was 90.5 (IQR: 62.75-171.5) and that of females was 105.5 (IQR: 57.0-183.75). Most studies were conducted in Asia (29/46), Europe (8/46), Middle East (6/46) and Africa (5/46) reflecting a diverse geographical representation. Studies were grouped based on outcome assessment methods (ACR vs. AER), study design (case-control, cross-sectional, or cohort), and genotype comparisons (II vs. DD, II vs. ID), to account for methodological and clinical heterogeneity and to enable meaningful synthesis of effect estimates across similar contexts. Most studies assessed the primary outcome of DN using either ACR 23 , 25 , 49 – 70 or AER, 71 – 91 whereas only one study used traditional renal function tests 92 (serum creatinine, eGFR, and urea levels). Genotyping for ACE I/D polymorphism was primarily conducted using polymerase chain reaction (PCR) methods, including conventional PCR, PCR-RFLP, high-resolution melting, and sequencing approaches. While all studies reported ACE I/D genotype distributions and DN outcomes, only five studies (10.7% of 46) explored associations with comorbidities, and one (2.2% of 46) reported on mortality. The majority identified either the D allele or DD genotype as being associated with increased DN risk, although several (40/46) reported null or population-specific findings. The geographical distributions of the studies included in this review are shown in Figure 4 . Figure 4. Geographical distribution of studies included in the review. This world map illustrates the countries where the 46 studies included in the systematic review were conducted. The studies are distributed across several regions, including Asia (China, Japan, South Korea, India, Pakistan, Taiwan, Bangladesh, Malaysia), the Middle East (Iran, Iraq), Europe (Finland, France, Germany, Italy, Poland, Turkey), and Africa (Egypt, Morocco, Tunisia, Uganda). Countries are color-coded to reflect regional groupings. The map was generated using MapChart.net . Examining ACE I/D polymorphism in relation to diabetic nephropathy and comorbidities Due to the limited number of studies addressing ACE I/D polymorphism in relation to diabetic nephropathy and associated comorbidities, the included studies were synthesized narratively rather than statistically. The summary of findings examining ACE I/D polymorphism in relation to diabetic nephropathy and comorbidities is presented in Table 2 . Table 2. Summary of included studies examining ACE I/D polymorphism in relation to diabetic nephropathy and comorbidities. First author, year Study design Method of ACE I/D genotyping Diabetic nephropathy assessment Results/conclusion Reported ACE I/D polymorphism and mortality/comorbidities in DN Araz et al., 2001 Cross-sectional Polymerase chain reaction Albumin-creatinine ratio No link found between ACE I/D polymorphism and DN or DR in Turkish T2DM patients. Yes Arfa et al., 2008 Case-control Polymerase chain reaction Albumin Excretion rate Preliminary findings show no association between ACE I/D polymorphism, T2DM, or DN in Tunisians. No Grzeszcszak et al., 1998 Case-control Polymerase chain reaction Albumin-creatinine ratio No association found between ACE I/D, PstI polymorphisms and nephropathy in NIDDM. No Gupta et al., 2023 Cross-sectional Polymerase chain reaction Albumin-creatinine ratio VEGF D allele linked to DR in DN patients, highlighting VEGF’s role in retinopathy. Yes Kiconco et al., 2024 Cross-sectional PCR Microalbuminuria ACE I/D affects metabolic markers, not nephropathy. No Luo et al., 2019 Case-control PCR with sequencing Albumin Excretion rate ACE gene at the rs4646994 locus may increase the risk of developing DN. No Parchwani et al., 2013 Case-control Polymerase chain reaction Albumin Excretion rate ACE DD variant linked to higher DN risk, not severity, in T2DM. No Uddin et al., 2007 Case-control PCR Albumin Excretion rate Percentage of DD genotype and D allele was significantly higher in DN compared to controls. No Viswanathan et al., 2001 Case-control PCR Albumin-creatinine ratio D allele linked to nephropathy; no ID/DD distribution difference. No Yahya et al., 2024 Cross-sectional PCR Albumin-creatinine ratio DD genotype linked to 2× DN risk; AGT1R A1166C not associated with DN in Iraqi T2DM. No Yahya et al., 2024 Cross-sectional HRM-RT and PCR-RFLP Albumin-creatinine ratio ID and AC genotypes linked to lower ACE1/2, lower ACR, better ACEI response. No Quantitative synthesis of ACE I/D polymorphism in diabetic nephropathy From the 46 studies, only those with a low overall score on the ROBINS E assessment were included in the quantitative synthesis. We conducted a meta-analysis, meta-regression, subgroup analysis, sensitivity analysis, and publication bias assessment on 25 studies to evaluate the association between ACE I/D polymorphisms and diabetic nephropathy (DN) in the study population. Meta-analysis This was conducted in accordance with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines, 93 and the corresponding checklist was completed to ensure comprehensive and transparent reporting. The included studies were deemed appropriate for assessing the hypothesis as they reported on ACE I/D genotypes and diabetic nephropathy outcomes in adult T2DM populations. Data were selected and coded based on sound clinical principles, prioritizing studies that clearly defined diabetic nephropathy using established albuminuria or renal function markers and reported ACE I/D genotype distributions relevant to the study objectives. Data classification and coding were performed independently by two reviewers using a standardized extraction template. Most included studies used standardized measures of albuminuria to define nephropathy, ensuring relevance to the primary outcome and alignment with the review objective. Data classification and coding were performed independently by two reviewers (RK and JT) using a standardized extraction template. Discrepancies were resolved through discussion with another reviewer; EAO, ensuring consistency and minimizing bias, although formal blinding and interrater reliability statistics were not applied. Random-effects models using the DerSimonian–Laird method 94 were used to estimate the pooled odds ratios (ORs) and 95% confidence intervals (CIs) across genotype comparisons to account for expected heterogeneity in study populations and methodologies across the included observational studies. Genotype II was associated with a lower risk of developing DN. The random-effects models showed a significant protective effect of the homozygous insertion (II) genotype, with pooled odds ratio (OR) of 0.70 (95% CI: 0.63–0.77) versus heterozygous insertion/deletion (ID) and 0.68 (95% CI: 0.55–0.84) versus homozygous deletion (DD). Heterogeneity across studies was moderate to substantial (I 2 = 71.7%, τ 2 = 0.1776, p < 0.0001), suggesting variability in the effect estimates, possibly due to methodological or population differences. A forest plot summarizing the pooled estimates and heterogeneity is shown in Figure 5 . Figure 5. Forest plot of pooled odds ratios from meta-analysis of ACE I/D polymorphism and diabetic nephropathy risk. This forest plot presents the individual and pooled odds ratio (OR) with corresponding 95% confidence intervals (CIs) for studies included in the meta-analysis assessing the association between ACE I/D polymorphism and the risk of diabetic nephropathy. Each horizontal line represents the confidence interval for a single study, with the square marker indicating the point estimate of the OR. The size of each square reflects the weight assigned to the study in the random-effects model. The diamond at the bottom of the plot represents the overall pooled OR, with a value of 0.68 (95% CI: 0.55–0.84). The heterogeneity among studies was moderate (I 2 = 71.7%, p < 0.0001). Meta-regression Meta-regression was performed to explore the sources of heterogeneity in the association between ACE I/D genotypes and diabetic nephropathy (DN) risk. In the crude model (k = 25; DerSimonian–Laird estimator), the residual heterogeneity was τ 2 = 0.1100 (SE = 0.0917; τ = 0.3317), with I 2 = 52.3% (p = 0.0141) and R 2 = 38.06%. The test for residual heterogeneity was significant (QE(12) = 25.16, p = 0.0141), while the test for moderators was not (QM(12) = 16.56, p = 0.17), indicating limited explanatory power overall. However, the form of UACR measurement was a significant individual predictor (β = 0.57, SE = 0.19, p = 0.0033; 95% CI: 0.19-0.95), suggesting that variations in measurement methods contributed meaningfully to heterogeneity in DN risk estimates. In the adjusted model, which included additional moderators, the residual heterogeneity increased (τ 2 = 0.1253, SE = 0.0692; τ = 0.3540), with I 2 = 63.0% (p < 0.0001) and R 2 = 29.45%. The overall test for residual heterogeneity remained significant (QE(23) = 62.21, p < 0.0001). The test of moderators was significant (QM(1) = 5.79, p = 0.016), confirming that the form of UACR measurement remained a key source of heterogeneity (β = 0.40, SE = 0.17, p = 0.016; 95% CI: 0.07-0.72). The results are summarized in Table 3 . Table 3. Meta-Regression of Study-Level covariates on the association between ACE I/D polymorphism and diabetic nephropathy risk. Covariate Estimate SE z-value p-value CI lower CI upper Interpretation Mean Age -0.0533 0.0347 -1.5335 0.1251 -0.1214 0.0148 Older populations may trend toward lower effect sizes; not significant Mean SBP (mmHg) -0.0287 0.0278 -1.0342 0.3011 -0.0832 0.0257 Higher SBP may be weakly associated with lower effect, but not significant Mean DBP (mmHg) 0.0348 0.0401 0.8673 0.3858 -0.0438 0.1134 Higher DBP shows a non-significant positive trend Mean HbA1c (%) -0.0775 0.1558 -0.4973 0.619 -0.3828 0.2279 Higher HbA1c not significantly associated with effect size BMI (Kg/m 2 ) 0.033 0.0465 0.7097 0.4779 -0.0581 0.1241 BMI does not significantly influence the association Mean T2D duration (years) 0.0267 0.0414 0.6441 0.5195 -0.0544 0.1077 Duration of diabetes not a significant moderator Hypertension (Yes/No) 0.4198 0.3045 1.3788 0.168 -0.1769 1.0165 Non-significant trend toward stronger effect in hypertensive populations Cardiovascular Disease (Yes/No) -0.7762 0.4844 -1.6022 0.1091 -1.7256 0.1733 Suggestive but non-significant inverse association in CVD populations Stroke (Yes/No) 1.0204 0.8991 1.135 0.2564 -0.7417 2.7825 No significant association with stroke prevalence Retinopathy (Yes/No) 0.0338 0.2426 0.1395 0.889 -0.4416 0.5093 Retinopathy status not associated with effect size Neuropathy (Yes/No) 0.3655 0.6829 0.5352 0.5925 -0.973 1.704 Neuropathy not significantly associated with pooled effect Form of UACR measured 0.57 0.194 2.938 0.0033* 0.1898 0.9503 Significant moderator - quantitative UACR methods associated with stronger genotype-DN associations Subgroup analysis Subgroup analyses were based on the methods used to assess the primary outcome which is DN and these included; ACR, AER and RFTs. They revealed differences in the strength of associations between ACE I/D genotypes and diabetic nephropathy (DN) risk. Studies using albumin-to-creatinine ratio (ACR) showed the strongest protective effect of the II genotype, with pooled odds ratio (OR) of 0.59 (95% CI: 0.47–0.75) under the random-effects model and 0.59 (95% CI: 0.51–0.67) under the common-effect model. For studies using the albumin excretion rate (AER), the association was weaker (random-effects OR = 0.71, 95% CI: 0.55–0.92; common-effect OR = 0.77, 95% CI: 0.66–0.90). The weakest association was observed in the study using renal function tests (RFTs), with a random-effects OR of 0.68 (95% CI: 0.55– 0.84) and a common-effect OR of 0.70 (95% CI: 0.63–0.77). Heterogeneity varied across subgroups: I 2 = 60.3% (p = 0.0019) for ACR and I 2 = 58.3% (p = 0.0102) for AER; no heterogeneity was detected in the RFT-based subgroup. These results suggested that the method used to assess DN may influence the estimated association between ACE I/D polymorphisms with DN risk. The full summary of subgroup results is presented in Table 4 . Table 4. Subgroup meta-analysis of ACE I/D polymorphism and diabetic nephropathy risk by biomarker type (ACR, AER, and RFT). Study/Sub-Group (Heterogeneity testing) logOR SE (logOR) OR 95% confidence Intervals Weight (Common) Weight (Random) ACR (Heterogeneity: I 2 = 60.3%, t 2 = 0.0931, p = 0.0019) Araz et al. 2001 -0.2249 0.3453 0.80 [0.41 – 1.57] 2.2 4.0 Doi et al. 1996 -0.6300 0.2477 0.53 [0.33 – 0.87] 4.3 5.0 Ezzidi et al. 2009 -1.1240 0.1555 0.32 [0.24 – 0.44] 11.0 5.9 Felehgari et al. 2011 -0.9140 0.5210 0.40 [0.14 – 1.11] 1.0 2.6 Gupta et al. 2023 -0.3703 0.3269 0.69 [0.36 – 1.31] 2.5 4.2 Ha et al. 2003 -0.4055 0.2890 0.67 [0.38 – 1.17] 3.2 4.5 Lee YJ and Tsai JC 2002 -0.4092 0.1542 0.66 [0.49 – 0.90] 11.2 5.9 Mansouri et al. 2001 0.29.19 0.5207 1.34 [0.48 – 3.72] 1.0 2.6 Oue et al. 1999 -0.4855 0.5790 0.62 [0.20 – 1.91] 0.8 2.3 Rahimi et al. 2012 -0.4300 0.3867 0.65 [0.30 – 1.39] 1.8 3.6 Teng et al. 2010 0.0219 0.1763 1.02 [0.72 – 1.44] 8.6 5.7 Viswanathan et al. 2001 -1.1385 0.5002 0.32 [0.12 – 0.85] 1.1 2.7 Wang et al. 2016 -1.1361 0.4539 0.32 [0.13 – 0.78] 1.3 3.1 Yahya et al. 2024 -0.5212 0.3717 0.59 [0.29 – 1.23] 1.9 3.7 Common effects model – – 0.59 [0.51 – 0.67] 51.8% – Random effects model – – 0.59 [0.47 – 0.75] – 55.9% AER (Heterogeneity: I 2 = 58.3%, t 2 = 0.0730, p = 0.0102) Arfa et al. 2008 -0.1823 0.5589 0.83 [0.28 – 2.49] 0.9 2.4 Fathi et al. 2015 -0.3879 0.2177 0.68 [0.44 – 1.04] 5.6 5.3 Hadjadji et al. 2003 -0.0952 0.1137 0.91 [0.73 – 1.14] 20.6 6.3 Hsieh et al. 2000 -0.4048 0.2198 0.67 [0.43 – 1.03] 5.5 5.3 Kuramoto et al. 1999 0.9318 0.5517 2.54 [0.86 – 7.49] 0.9 2.4 Nikzamir et al. 2009 -0.6526 0.2695 0.52 [0.31 – 0.88] 3.7 4.7 Ohno et al. 1996 -1.2130 0.3710 0.30 [0.14 – 0.62] 1.9 3.7 Okuno et al. 2003 -2.6092 1.0942 0.07 [0.01 – 0.63] 0.2 0.8 Parchwani et al. 2013 -0.0189 0.2868 0.98 [0.56 – 1.72] 3.2 4.6 Young et al. 1998 -0.2869 0.3847 0.75 [0.35 – 1.60] 1.8 3.6 Common effects model – – 0.77 [0.66 – 0.90] 44.3% – Random effects model – – 0.71 [0.55 – 0.92] – 39.2% RFTs (Heterogeneity: Not Computed) Shaihk et al. 2014 0.8803 0.2591 2.41 [1.45 – 4.01] 4.0 4.9 Common effects model – – 0.70 [0.63 – 0.77] 100% – Random effects model – – 0.68 [0.55 – 0.84] – 100% Sensitivity analyses Leave-one-out sensitivity analysis was conducted to assess the robustness of the findings and the influence of individual studies on the pooled effect estimates. One study was excluded at a time, and the overall odds ratio (OR) and 95% confidence interval (CI) were calculated. The pooled log OR ranged from -0.34 to -0.45, with minimal variation across iterations. The confidence intervals remained narrow and did not cross the null, suggesting consistent associations. The exclusion of Shaihk et al. 92 produced the most marked change (log OR = -0.4474; 95% CI: -0.6255 to -0.2692), followed by Kuramoto et al. 81 and Tseng et al., 66 though none altered the overall interpretation. These results indicated that no single study had a disproportionate impact on the association between ACE I/D genotypes and diabetic nephropathy in the meta-analysis. The full results are shown in Table 5 and visualized in Figure 6 . Table 5. Sensitivity analysis of individual studies included in the meta-analysis of ACE I/D genotypes and diabetic nephropathy. Study OR 95% CI lower 95% CI upper Araz et al. 2001 -0.3966089 -0.6166201 -0.1765978 Arfa et al. 2008 -0.3945578 -0.6110598 -0.1780557 Doi et al. 1996 -0.3771501 -0.5979375 -0.1563626 Ezzidi et al. 2009 -0.3353554 -0.5341380 -0.1365728 Fathi et al. 2015 -0.3901966 -0.6134694 -0.1669238 Felehgari et al. 2011 -0.3749063 -0.5898228 -0.1599899 Gupta et al. 2023 -0.3906916 -0.6114558 -0.1699273 Ha et al. 2003 -0.3891762 -0.6107695 -0.1675828 Hadjadj et al. 2003 -0.4090621 -0.6311435 -0.1869808 Hsieh et al. 2000 -0.3892575 -0.6124589 -0.1660562 Kuramoto et al. 1999 -0.4196006 -0.6267025 -0.2124986 Lee YJ and Tsai JC. 2002 -0.3890242 -0.6136330 -0.1644155 Mansouri et al. 2017 -0.4072607 -0.6214458 -0.1930757 Nikzamir et al. 2009 -0.3765290 -0.5966524 -0.1564056 Ohno et al. 1996 -0.3558761 -0.5648249 -0.1469272 Okuno et al. 2003 -0.3697799 -0.5791523 -0.1604074 Oue et al. 1999 -0.3871209 -0.6034409 -0.1708009 Parchwani et al. 2013 -0.4071222 -0.6259972 -0.1882471 Rahimi et al. 2012 -0.3881874 -0.6076244 -0.1687504 Shaihk et al. 2014 -0.4473571 -0.6255171 -0.2691971 Tseng et al. 2010 -0.4139499 -0.6325336 -0.1953663 Viswanathan et al. 2001 -0.3675264 -0.5801649 -0.1548878 Wang et al. 2016 -0.3649410 -0.5771711 -0.1527110 Yahya et al. 2024 -0.3845316 -0.6039635 -0.1650997 Young et al. 1998 -0.3936058 -0.6130589 -0.1741526 Figure 6. Leave-One-Out sensitivity analysis of the association between ACE I/D genotypes and diabetic nephropathy. This forest plot illustrates the results of a leave-one-out sensitivity analysis. Each line represents the pooled odds ratio (OR) and 95% confidence interval (CI) calculated by excluding one study at a time from the overall analysis. The consistency of effect estimates across iterations indicates that no single study unduly influenced the pooled results. The overall pooled OR remains significant and stable (OR = 0.64; 95% CI: 0.53–0.76) under the random-effects model. Heterogeneity was moderate (I 2 = 62.3%, p < 0.0001), and the form of UACR measurement was included as a subgroup moderator. Abbreviations : ACE – Angiotensin-Converting Enzyme; I/D – Insertion/Deletion; OR – Odds Ratio; CI – Confidence Interval; SE – Standard Error; UACR – Urine Albumin-to-Creatinine Ratio. Publication bias The funnel plot shown in Figure 7 and Egger’s regression test were used to assess the publication bias. The plot shows a relatively symmetrical distribution around the pooled effect size, with no substantial asymmetry. Egger’s test (t = -0.61, df = 23, p = 0.55; bias = -0.50, SE = 0.82) indicated no significant small study effects. The residual heterogeneity variance was τ 2 = 3.62, using standard error as the predictor and inverse-variance weights. Given the lack of asymmetry, a trim-and-fill analysis was not performed. These results suggest that the pooled estimates are unlikely to be affected by publication bias. Figure 7. Funnel plot for assessing publication bias in studies on ACE I/D genotypes and diabetic nephropathy. This funnel plot visualizes the distribution of studies included in the meta-analysis. The plot displays the standard error (y-axis) against the odds ratio (x-axis) for each study. Symmetry of the scatter around the pooled effect estimate suggests the absence of significant publication bias. The majority of studies fall within the pseudo 95% confidence limits (dashed lines). Abbreviations : ACE – Angiotensin-Converting Enzyme; I/D – Insertion/Deletion; OR – Odds Ratio; SE – Standard Error. Reporting bias assessment Assessment of reporting bias was informed by the “Selection of Reported Result” domain in ROBINS-E and several reporting items in STREGA. In ROBINS-E, most studies were rated as having a moderate risk for selective reporting, indicating that not all measured outcomes may have been reported. In the STREGA assessment, most studies did not report sample size justification or apply multiple testing correction, and Hardy-Weinberg Equilibrium (HWE) was tested in only a few. These findings suggest a moderate risk of bias due to missing or selectively reported results across the included studies. Certainty of evidence The certainty of the evidence in this review was appraised qualitatively, as the GRADE approach was not applied due to the observational nature of the included genetic association studies. Instead, confidence in the findings was supported by several methodological safeguards: risk of bias was assessed using the ROBINS-E tool, guiding the inclusion of only low-risk studies in the meta-analysis; reporting quality was evaluated using the STREGA checklist to ensure transparency and reproducibility; and consistency, direction, and precision of effect estimates were examined across studies. Out of 46 studies, 25 were rated as having low risk of bias, 15 as moderate or some concerns, and 6 as high according to ROBINS-E. STREGA compliance ratings showed that 19 studies were rated as very good, 13 as fair, 9 as good, and 5 as excellent. Based on this distribution, the overall confidence in the evidence is moderate, reflecting generally sound methodology and reporting, though limitations in transparency and bias remain in a subset of studies. Heterogeneity was assessed using I 2 and τ 2 statistics and explored further through subgroup and meta-regression analyses. Publication bias was evaluated through funnel plots and Egger’s regression test. Together, these assessments provide a moderate to high level of confidence in the observed association between ACE I/D genotypes and diabetic nephropathy, particularly the protective effect of the II genotype. However, the certainty of evidence for secondary outcomes, such as mortality and comorbidities, remains low due to limited data. Discussion We evaluated the association between ACE insertion/deletion (I/D) polymorphisms and DN in T2DM patients, with a focus on differences in UACR assessments, study populations, and clinical outcomes such as hypertension, cardiovascular disease, stroke and mortality from DN. Narrative synthesis demonstrated a generally consistent association between ACE I/D variants and DN risk, although the effect sizes varied across studies. The extent to which ACE I/D genotypes influence DN outcomes likely varies depending on several intersecting factors. Population characteristics, such as geographical location, age, sex, and prevalence of hypertension or other metabolic comorbidities, which modify the genetic effect of the ACE I/D polymorphism. For example, the distribution of I/D alleles and their association with DN may differ among Asian, African, and European populations due to underlying genetic diversity and environmental exposure. The meta-analysis revealed that individuals with the ACE II genotype had significantly lower odds of developing diabetic nephropathy compared to those with ID or DD genotypes, highlighting a protective genetic association as the primary outcome of this systematic review. Previous studies that encompassed ethnicity on this topic found that there was a reduced risk of diabetic nephropathy associated with genotype II among Europeans (Caucasians) with type 2 diabetes, although this observation was more applicable to Asians (Chinese, Japanese, Koreans). 22 , 95 Moreover, the meta-analysis confirmed a statistically significant protective association of the II genotype compared to both ID and DD genotypes, consistent with prior research implicating the renin–angiotensin–aldosterone system (RAAS) in DN pathogenesis. 22 , 56 , 95 These findings reinforce the hypothesis that genetic variation at the ACE locus contributes to an individual’s susceptibility to DN. However, the modest magnitude of the effect suggests that this polymorphism is likely one of multiple genetic contributors within a polygenic framework, as noted from the heterogeneity in the method used to assess diabetic nephropathy in primary studies. We observed that the identification of DN cases varied considerably across studies. Most studies assessed DN using either ACR or AER, while only one study relied on traditional renal function tests, such as serum creatinine, estimated glomerular filtration rate (eGFR), or urea levels. This inconsistency in diagnostic criteria significantly affects the comparability and generalizability of reported genotype-phenotype associations. Moreover, in our meta-regression analysis, the UACR measurement method emerged as a critical moderator of heterogeneity, underscoring the methodological influence on the pooled effect sizes. Although these biochemical markers are practical for large-scale research and clinical use, they are surrogates rather than definitive diagnostic tools. Notably, kidney histology remains the gold standard for diagnosing DN, offering detailed insight into glomerular and tubular changes. 96 However, renal biopsy is rarely performed due to its invasive nature, associated risks, and ethical limitations in asymptomatic populations. 97 Subgroup analyses revealed stronger genotype-phenotype associations in studies using urinary albumin-to-creatinine ratio (UACR) compared to albumin excretion rate (AER) or serum-based renal function tests. These findings align with prior recommendations advocating standardized albuminuria assessment in DN research to improve reproducibility and comparability across studies. 98 , 99 Methodological inconsistency in DN diagnosis remains a major source of heterogeneity in the genetic literature. 100 These methodological differences may obscure or exaggerate the true effects of ACE I/D polymorphism. 101 , 102 Sensitivity analyses confirmed the robustness of the pooled estimates, with no individual study unduly influencing the overall results. While studies by Shaikh et al., 92 Kuramoto et al., 81 and Tseng et al. 66 showed relatively greater influence when excluded, the overall direction and significance of the associations remained unchanged. This stability across leave-one-out analyses supports the internal validity of the meta-analysis. Assessment of publication bias using funnel plot symmetry and Egger’s regression test indicated no evidence of small-study effects. 103 The non-significant test and symmetrical funnel distribution suggest that selective reporting did not materially influence the meta-analysis findings. Despite the breadth of studies evaluating the genetic associations with DN, a major gap exists in the reporting of clinical outcomes. From the narrative synthesis of the included studies in this review, only one study addressed DN-related mortality, 83 and five reported comorbidities such as metabolic disease, 104 neuropathy 56 and retinopathy. 52 , 57 , 70 Most studies have been conducted in broader T2DM populations without disaggregating the outcomes for DN-specific cohorts. Consequently, the translational relevance of ACE I/D polymorphisms to clinical endpoints in DN remains poorly defined. Future studies should prioritize DN-specific outcomes and adopt standardized reporting frameworks to improve clinical interpretability. One limitation in this study is the presence of moderate between-study heterogeneity. This heterogeneity persisted even after subgroup and meta-regression analyses were conducted. A likely explanation for this is residual confounding. This may be due to variations in ethnicity, environmental exposure, and gene–environment interactions. Unfortunately, these variables were not captured in the data-extraction process. For instance, although the review predominantly included studies defining diabetic nephropathy based on urinary albuminuria markers (ACR or AER), one study using serum creatinine and eGFR was retained to incorporate relevant genetic data. This slight deviation was carefully considered and does not substantially compromise the consistency or validity of the overall findings, as the included outcome remains clinically relevant to nephropathy assessment. In addition, the primary diagnostic marker for DN in our systematic review was urinary albumin-to-creatinine ratio (UACR), as reported in the original studies. However, we acknowledge that there are differences between the quantitative and semi-quantitative methods for measuring urinary albumin levels. To address this, we used the ROBINS-E tool to assess the risk of bias related to outcome measurements. Studies that lacked clarity or likely used semi-quantitative methods were rated as moderate-to-high risk, and only those with low overall risk (25 studies) were included in the meta-analysis. This helped ensure a more accurate DN classification in our pooled estimates. Moreover, in our subgroup/meta-regression analyses, the form of urinary albumin measurement; ACR vs. AER was statistically significant, reinforcing the impact of methodology on DN diagnosis. This review did not include unpublished studies or abstracts, and no author contact was made for missing data, which may have limited access to additional insights; however, efforts were made to ensure comprehensive coverage through rigorous database and reference list searches. Therefore, we suggest that future studies prioritize standardized methods for assessing microalbuminuria, particularly the use of ACR over AER, to reduce heterogeneity in outcome reporting. There is also a clear need for well-designed, large-scale genetic association studies that specifically examine ACE I/D polymorphisms in relation to DN-related mortality and comorbidities, such as hypertension and cardiovascular disease, which are underrepresented in the current literature. Additionally, future research should explore gene-environment and gene-gene interactions, as well as the utility of ACE genotyping in clinical risk prediction models for DN progression within diverse populations, especially in regions such as sub-Saharan Africa, where such data are scarce. Conclusion This systematic review and meta-analysis provides evidence for a protective association between ACE insertion/deletion (I/D) polymorphism and diabetic nephropathy (DN), with genotype II consistently linked to reduced DN risk. Subgroup and meta-regression analyses identified the method of urinary albumin measurement; in particular, the albumin-to-creatinine ratio (ACR), which, according to our study, was a significant moderator of effect size variability, highlighting the need for methodological standardization in DN research and clinical practice. These findings were robust to sensitivity testing and showed no evidence of publication bias. However, the limited reporting of DN-specific clinical outcomes, including mortality and comorbidities, highlights persistent gaps in the literature. Moderate heterogeneity across studies suggests additional contributing factors; therefore, future research should prioritize harmonized diagnostic criteria, expand the evaluation of clinical outcomes, and incorporate polygenic and environmental risk models. Such efforts are essential for advancing precision medicine approaches for the prevention and management of DN in individuals with type 2 diabetes. Ethics approval and consent to participate This study is part of a doctoral research project approved by the Mbarara University of Science and Technology Research Ethics Committee (MUST-REC), reference number MUST-2024-1725. The review was conducted using publicly available data and therefore did not require additional ethical clearance. Author abbreviations RK - Ritah Kiconco, KR- Robert Kalyesubula, RA- Raymond Atwine, JT- Jazira Tumusiime, CNB- Charles Nkubi Bagenda, SPR- Simon Peter Rugera, BBA- Bosco Bekiita Agaba, ENO- Erick Nyakundi Ondari, DT- Deusdedit Tusubira, GNK- Gertrude N. Kiwanuka, ML - Martha Lyaka, MO - Moses Ocan, AAK- Alison Annet Kinyengere, EO - Ekwaro Obuku. Data availability Underlying data No data associated with this article. Extended data The materials used in this review are available in Zenodo. The project title is “ACE I/D gene mutations in diabetic nephropathy” https://doi.org/10.5281/zenodo.15765008 105 The project contains the following extended data; • Completed Table 2 • 2020 PRISMA checklist • MOOSE checklist • SWiM checklist • ROBINS-E assessment • STREGA assessment • Compiled meta-analysis dataset • Compiled systematic review dataset Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements The authors acknowledge the support of Mbarara University of Science and Technology (MUST) and the Africa Centre for Systematic Reviews and Knowledge Translation for providing technical and academic guidance throughout the development of this review. We thank the Fogarty International Center of the NIH for supporting the lead author through the D43 multimorbidity research training grant. The funder had no role in this review’s design or publication. Special thanks to all collaborators and institutional mentors who provided feedback and oversight throughout the reporting process. References 1. Gheith O, Farouk N, Nampoory N, et al. : Diabetic kidney disease: world wide difference of prevalence and risk factors. J. Nephropharmacol. 2015 Oct 9 [cited 2022 Oct 21]; 5 (1): 49–56. PubMed Abstract Reference Source 2. Selby NM, Taal MW: An updated overview of diabetic nephropathy: Diagnosis, prognosis, treatment goals and latest guidelines. Diabetes. Obes. Metab. 2020 [cited 2025 Apr 17]; 22 (S1): 3–15. PubMed Abstract | Publisher Full Text 3. Leese B: The costs of diabetes and its complications. Soc. Sci. Med. 1992 Nov 1 [cited 2025 Apr 22]; 35 (10): 1303–1310. Publisher Full Text Reference Source 4. Lim AK: Diabetic nephropathy – complications and treatment. Int. J. Nephrol. Renov. Dis. 2014 Oct 15 [cited 2025 Apr 17]; 7 : 361–381. PubMed Abstract | Publisher Full Text Reference Source 5. Gluhovschi C, Gluhovschi G, Petrica L, et al. : Urinary Biomarkers in the Assessment of Early Diabetic Nephropathy. J. Diabetes Res. 2016; 2016 : 4626125. 6. Moon SJ, Park HB, Yoon SY, et al. : Urinary Biomarkers for Early Detection of Recovery in Patients with Acute Kidney Injury. J. Korean Med. Sci. 2013 Aug 1 [cited 2025 Jun 1]; 28 (8): 1181–1186. PubMed Abstract | Publisher Full Text | Free Full Text 7. Ning M, Mao X, Niu Y, et al. : Usefulness and limitations of neutrophil gelatinase-associated lipocalin in the assessment of kidney diseases. J. Lab. Precis. Med. 2018 Jan 12 [cited 2025 Jun 1]; 3 (1). Publisher Full Text Reference Source 8. Devarajan P: Review: Neutrophil gelatinase-associated lipocalin: A troponin-like biomarker for human acute kidney injury. Nephrology. 2010 [cited 2025 Jun 1]; 15 (4): 419–428. PubMed Abstract | Publisher Full Text 9. Wajda J, Dumnicka P, Kolber W, et al. : The Marker of Tubular Injury, Kidney Injury Molecule-1 (KIM-1), in Acute Kidney Injury Complicating Acute Pancreatitis: A Preliminary Study. J. Clin. Med. 2020 May [cited 2025 Jun 2]; 9 (5): 1463. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source 10. Petrovic S, Bogavac-Stanojevic N, Peco-Antic A, et al. : Clinical Application Neutrophil Gelatinase-Associated Lipocalin and Kidney Injury Molecule-1 as Indicators of Inflammation Persistence and Acute Kidney Injury in Children with Urinary Tract Infection. Biomed. Res. Int. 2013 [cited 2025 Jun 2]; 2013 : 1–9. Publisher Full Text Reference Source 11. Tungsanga S, Nantanawijit R, Kiatamornrak P, et al. : Utility of a novel point-of-care test for albuminuria in communities at high risk for chronic kidney disease in Thailand. BMJ Public Health. 2025 Jan 16 [cited 2025 Jun 2]; 3 (1): e001412. PubMed Abstract | Publisher Full Text | Free Full Text 12. Stevens PE, Ahmed SB, Carrero JJ, et al. : KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024 Apr 1 [cited 2025 Jun 2]; 105 (4): S117–S314. PubMed Abstract | Publisher Full Text Reference Source 13. Tummalapalli SL, Shlipak MG, Damster S, et al. : Availability and Affordability of Kidney Health Laboratory Tests around the Globe. Am. J. Nephrol. 2021 Mar 2 [cited 2025 Jun 2]; 51 (12): 959–965. PubMed Abstract | Publisher Full Text | Free Full Text 14. Konta T, Asahi K, Tamura K, et al. : The health-economic impact of urine albumin-to-creatinine ratio testing for chronic kidney disease in Japanese non-diabetic patients. Clin. Exp. Nephrol. 2025 May 1 [cited 2025 Jun 2]; 29 (5): 583–595. PubMed Abstract | Publisher Full Text | Free Full Text 15. Thiengsusuk A, Youngvises N, Pochairach R, et al. : Urinary Albumin-to-Creatinine Ratio (uACR) Point-of-Care (POC) Device with Seamless Data Transmission for Monitoring the Progression of Chronic Kidney Disease. Biosensors. 2025 Mar [cited 2025 Jun 2]; 15 (3): 145. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source 16. Zhang X, Zhang J, Ren Y, et al. : Unveiling the pathogenesis and therapeutic approaches for diabetic nephropathy: insights from panvascular diseases. Front. Endocrinol. (Lausanne). 2024 Feb 22 [cited 2025 Apr 17]; 15 : 1368481. PubMed Abstract | Publisher Full Text | Free Full Text 17. Samsu N: Diabetic Nephropathy: Challenges in Pathogenesis, Diagnosis, and Treatment. Biomed. Res. Int. 2021 Jul 8 [cited 2025 Apr 17]; 2021 : 1497449. PubMed Abstract | Publisher Full Text | Free Full Text 18. Dhumad MM, Hamdan FB, Al-Mayah QS: Angiotensin-converting enzyme insertion/deletion (I/D) gene polymorphism in Iraqi type 2 diabetic patients: association with the risk of cardiac autonomic neuropathy. Egypt J. Med. Hum. Genet. 2020 Dec [cited 2023 Apr 8]; 21 (1): 1–7. Publisher Full Text 19. Solini A, Dalla Vestra M, Saller A, et al. : The Angiotensin-Converting Enzyme DD Genotype Is Associated With Glomerulopathy Lesions in Type 2 Diabetes. Diabetes. 2002 Jan 1 [cited 2025 Jun 1]; 51 (1): 251–255. PubMed Abstract | Publisher Full Text 20. Handani DAS, Ikawati Z, Hermawan A: The role of angiotensin I - converting enzyme (ACE) insertion/deletion gene polymorphism in hypertension and ACE inhibitor therapy: a narrative review. Indonesian Journal of Pharmacology and Therapy. 2023 Nov 14 [cited 2025 Jun 1]; 4 (3). Publisher Full Text Reference Source 21. Parving HH, Tarnow L, Rossing P: Genetics of diabetic nephropathy. J. Am. Soc. Nephrol. 1996 Dec; 7 (12): 2509–2517. Publisher Full Text 22. Ng DPK, Tai BC, Koh D, et al. : Angiotensin-I converting enzyme insertion/deletion polymorphism and its association with diabetic nephropathy: a meta-analysis of studies reported between 1994 and 2004 and comprising 14,727 subjects. Diabetologia. 2005 May; 48 (5): 1008–1016. PubMed Abstract | Publisher Full Text 23. Ezzidi I, Mtiraoui N, Kacem M, et al. : Identification of specific angiotensin-converting enzyme variants and haplotypes that confer risk and protection against type 2 diabetic nephropathy. Diabetes Metab. Res. Rev. 2009 [cited 2025 Mar 16]; 25 (8): 717–724. PubMed Abstract | Publisher Full Text 24. Rahimi Z: ACE insertion/deletion (I/D) polymorphism and diabetic nephropathy. J. Nephropathol. 2012 Oct [cited 2023 Apr 8]; 1 (3): 143–151. PubMed Abstract | Publisher Full Text | Free Full Text 25. Jayapalan JJ, Muniandy S, Chan SP: Null association between ACE gene I/D polymorphism and diabetic nephropathy among multiethnic Malaysian subjects. Indian J. Hum. Genet. 2010 [cited 2023 Oct 14]; 16 (2): 78–86. PubMed Abstract | Publisher Full Text Reference Source 26. Yu ZY, Chen LS, Zhang LC, et al. : Meta-analysis of the relationship between ACE I/D gene polymorphism and end-stage renal disease in patients with diabetic nephropathy. Nephrology (Carlton). 2012 Jul; 17 (5): 480–487. PubMed Abstract | Publisher Full Text 27. Moher D, Shamseer L, Clarke M, et al. : Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015 Jan 1; 4 (1): 1. PubMed Abstract | Publisher Full Text | Free Full Text 28. PROSPERO: 2025 [cited 2025 Apr 17]. Reference Source 29. Kiconco R, Kalyesubula R, Tumusiime J, et al. : Association Between Angiotensin Converting Enzyme Insertion/Deletion Genotypes and Diabetic Nephropathy Defined by Urinary Albumin-to-Creatinine Ratio: A Systematic Review and Meta-Analysis Protocol. OSF. 2025 [cited 2025 Jun 12]. Reference Source 30. Ouzzani M, Hammady H, Fedorowicz Z, et al. : Rayyan—a web and mobile app for systematic reviews. Syst. Rev. 2016 Dec 5 [cited 2025 Jun 1]; 5 (1): 210. PubMed Abstract | Publisher Full Text | Free Full Text 31. Higgins JPT, Morgan RL, Rooney AA, et al. : A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E). Environ. Int. 2024 Apr 1 [cited 2024 Aug 22]; 186 : 108602. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source 32. McGuinness LA, Higgins JPT: Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res. Synth. Methods. 2021 Jan; 12 (1): 55–61. Publisher Full Text 33. Guyatt GH, Oxman AD, Vist GE, et al. : GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008 Apr 26; 336 (7650): 924–926. PubMed Abstract | Publisher Full Text | Free Full Text 34. Haddaway NR, Page MJ, Pritchard CC, et al. : PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst. Rev. 2022 Jun 1 [cited 2022 May 6]; 18 (2): e1230. PubMed Abstract | Publisher Full Text | Free Full Text 35. Panagiotopoulos S, Smith TJ, Aldred GP, et al. : Angiotensin-converting enzyme (ACE) gene polymorphism in type II diabetic patients with increased albumin excretion rate. J. Diabetes Complicat. 1995 Dec; 9 (4): 272–276. PubMed Abstract | Publisher Full Text 36. Wong TY, Szeto CC, Chow KM, et al. : Contribution of gene polymorphisms in the renin-angiotensin system to macroangiopathy in patients with diabetic nephropathy. Am. J. Kidney Dis. 2001 [cited 6AD Dec 31]; 38 (1): 9–17. PubMed Abstract | Publisher Full Text 37. Ahluwalia TS, Ahuja M, Rai TS, et al. : ACE Variants Interact with the RAS Pathway to Confer Risk and Protection against Type 2 Diabetic Nephropathy. DNA Cell Biol. 2009 Mar [cited 2025 Mar 16]; 28 (3): 141–150. PubMed Abstract | Publisher Full Text 38. Pai D, Adiga S, Suresh G, et al. : The association of ACE gene polymorphism and serum ACE levels with diabetic nephropathy-a cross-sectional study. J. App. Pharm. Sci. 2024 Apr 5 [cited 2025 Mar 17]; 14 (4): 210–217. Publisher Full Text Reference Source 39. Fujisawa T, Ikegami H, Shen GQ, et al. : Angiotensin I-converting enzyme gene polymorphism is associated with myocardial infarction, but not with retinopathy or nephropathy, in NIDDM. Diabetes Care. 1995 Jul; 18 (7): 983–985. Publisher Full Text 40. Taha MM, Zakaria MAY, Eisa YH, et al. : Potential role of serum copeptin among smoker T2DM patients with emphasis to ACE I/D gene polymorphism predicting DN. Sci. Rep. 2024 Jun 6 [cited 2025 Mar 18]; 14 (1): 13068. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source 41. Araz M, Okan V, Celen Z, et al. : Angiotensin converting enzyme gene polymorphism and glomerular filtration rate changes in type 2 diabetic patients. Int. J. Clin. Pract. 2002; 56 (6): 416–418. PubMed Abstract | Publisher Full Text 42. Canani LH, Costa LA, Crispim D, et al. : The presence of allele D of angiotensin-converting enzyme polymorphism is associated with diabetic nephropathy in patients with less than 10 years duration of Type 2 diabetes. Diabet. Med. 2005 Sep; 22 (9): 1167–1172. PubMed Abstract | Publisher Full Text 43. Wong TY, Chan JC, Poon E, et al. : Lack of association of angiotensin-converting enzyme (DD/II) and angiotensinogen M235T gene polymorphism with renal function among Chinese patients with type II diabetes. Am. J. Kidney Dis. 1999 Jun; 33 (6): 1064–1070. PubMed Abstract | Publisher Full Text 44. Draman CR, Kong NC, Gafor AH, et al. : The effects of angiotensin-converting enzyme gene polymorphism on the progression of immunoglobulin A nephropathy in Malaysian patients. Singapore Med. J. 2008 Nov; 49 (11): 924–929. PubMed Abstract 45. Pontremoli R, Sofia A, Tirotta A, et al. : The deletion polymorphism of the angiotensin I-converting enzyme gene is associated with target organ damage in essential hypertension. J. Am. Soc. Nephrol. 1996 Dec; 7 (12): 2550–2558. PubMed Abstract | Publisher Full Text 46. Al-Harbi EM, Farid EM, Gumaa KA, et al. : Genotypes and allele frequencies of angiotensin-converting enzyme (ACE) insertion/deletion polymorphism among Bahraini population with type 2 diabetes mellitus and related diseases. Mol. Cell. Biochem. 2012 Mar; 362 (1–2): 219–223. PubMed Abstract | Publisher Full Text 47. Booth AM, Wright KE, Outhwaite H: Centre for Reviews and Dissemination databases: value, content, and developments. Int. J. Technol. Assess. Health Care. 2010 Oct; 26 (4): 470–472. PubMed Abstract | Publisher Full Text 48. Campbell M, McKenzie JE, Sowden A, et al. : Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline.2020 Jan 16 [cited 2025 Jun 8]. Reference Source 49. Viswanathan V, Zhu Y, Bala K, et al. : Association between ACE gene polymorphism and diabetic nephropathy in South Indian patients. J. Pancreas. 2001; 2 (2): 83 EP–87. Reference Source Reference Source Reference Source 50. Yahya AA, Kadhim DJ, Abdalhadi NA: The role of angiotensin converting enzyme (insertion)/(deletion) and angiotensin II type 1 receptor (A1166C) gene polymorphisms in antiproteinuric effect of ACE inhibitors in type 2 diabetic Iraqi patients. J. Appl. Pharm. Sci. 2024; 14 (7): 175 EP–183. Reference Source Reference Source Reference Source 51. Yahya AA, Kadhim DJ, Abdalhadi NA: Association of Angiotensin Converting Enzyme (insertion\deletion) and Angiotensin II Type 1 Receptor (A1166C) gene polymorphisms with diabetic nephropathy in Iraqi type 2 diabetic patients. Iraqi J. Pharm. Sci. 2024 Sep 15 [cited 2025 Mar 17]; 33 (3): 17–29. Publisher Full Text Reference Source 52. Gupta P, Madhu SV, Bubber P: Genetic Polymorphism in Patients with Diabetic Nephropathy and Retinopathy: A Cross-sectional Study. J. Clin. Diagn. Res. 2023; 17 (8). Reference Source Reference Source Reference Source 53. Grzeszczak W, Zychma MJ, Lacka B, et al. : Angiotensin I-converting enzyme gene polymorphisms: relationship to nephropathy in patients with non-insulin dependent diabetes mellitus. J. Am. Soc. Nephrol. 1998 Sep; 9 (9): 1664–1669. Publisher Full Text 54. Mansouri M, Zniber A, Boualla L, et al. : Associations between clinical characteristics and angiotensin-converting enzyme gene insertion/deletion polymorphism in Moroccan population with Type-2 diabetic nephropathy. Saudi J. Kidney Dis. Transpl. 2017; 28 (2): 261–267. PubMed Abstract | Publisher Full Text 55. Lee YJ, Tsai JCR: ACE Gene Insertion/Deletion Polymorphism Associated With 1998 World Health Organization Definition of Metabolic Syndrome in Chinese Type 2 Diabetic Patients. Diabetes Care. 2002 Jun 1 [cited 2025 Mar 16]; 25 (6): 1002–1008. PubMed Abstract | Publisher Full Text 56. Degirmenci I, Kebapci N, Basaran A, et al. : Frequency of angiotensin-converting enzyme gene polymorphism in Turkish type 2 diabetic patients. Int. J. Clin. Pract. 2005 Oct; 59 (10): 1137–1142. PubMed Abstract | Publisher Full Text 57. Doi Y, Yoshizumi H, Yoshinari M, et al. : Association between a polymorphism in the angiotensin-converting enzyme gene and microvascular complications in Japanese patients with NIDDM. Diabetologia. 1996 Jan; 39 (1): 97–102. PubMed Abstract | Publisher Full Text 58. Ismail M, Shaker O, Ashour E, et al. : The Combined Effect of ACE, TCF7L2, and PPARGC1A Gene Polymorphisms in Diabetic Nephropathy. Egypt. J. Chem. 2017; 60 (5): 869–881. 59. Ergen HA, Hatemi H, Agachan B, et al. : Angiotensin-I converting enzyme gene polymorphism in Turkish type 2 diabetic patients. Exp. Mol. Med. 2004 Aug [cited 2025 Mar 17]; 36 (4): 345–350. PubMed Abstract | Publisher Full Text Reference Source 60. Felehgari V, Rahimi Z, Mozafari H, et al. : ACE gene polymorphism and serum ACE activity in Iranians type II diabetic patients with macroalbuminuria. Mol. Cell. Biochem. 2011 Jan; 346 (1–2): 23–30. PubMed Abstract | Publisher Full Text 61. Ha S, Yong Lee S, Su Park H, et al. : ACE DD genotype is more susceptible than ACE II and ID genotypes to the antiproteinuric effect of ACE inhibitors in patients with proteinuric non-insulin-dependent diabetes mellitus. Nephrol. Dial. Transplant. 2000 Oct 1 [cited 2025 Mar 17]; 15 (10): 1617–1623. Publisher Full Text 62. Ha SK, Park HC, Park HS, et al. : ACE gene polymorphism and progression of diabetic nephropathy in Korean type 2 diabetic patients: effect of ACE gene DD on the progression of diabetic nephropathy. Am. J. Kidney Dis. 2003 May 1 [cited 2025 Mar 17]; 41 (5): 943–949. PubMed Abstract | Publisher Full Text Reference Source 63. Oue T, Namba M, Nakajima H, et al. : Risk factors for the progression of microalbuminuria in Japanese type 2 diabetic patients--a 10 year follow-up study. Diabetes Res. Clin. Pract. 1999 Oct; 46 (1): 47–55. PubMed Abstract | Publisher Full Text 64. Rahimi Z, Vaisi-Raygani A, Rahimi Z, et al. : Concomitant presence of endothelial nitric oxide 894T and angiotensin II-converting enzyme D alleles are associated with diabetic nephropathy in a Kurdish population from Western Iran. Nephrology (Carlton). 2012 Feb; 17 (2): 175–181. PubMed Abstract | Publisher Full Text 65. Pawar VS, Datkhile KD, Sontakke AV, et al. : Renin Angiotensin Aldosterone System Gene Polymorphisms among Type 2 Diabetic Patients with Retinopathy and Nephropathy. J. Pharm. Bioallied Sci. 2024 Dec [cited 2025 Mar 18]; 16 (Suppl 5): S4660–S4666. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source 66. Tseng CH, Tseng CP, Chong CK: Joint effects of hypertension, smoking, dyslipidemia and obesity and angiotensin-converting enzyme DD genotype on albuminuria in Taiwanese patients with type 2 diabetes mellitus. Clin. Biochem. 2010 May; 43 (7–8): 629–634. PubMed Abstract | Publisher Full Text 67. Wang Y, Peng W, Zhang X, et al. : The association of ACE gene polymorphism with diabetic kidney disease and renoprotective efficacy of valsartan. J. Renin-Angiotensin-Aldosterone Syst. 2016 Jul; 17 (3): 1470320316666749. PubMed Abstract | Publisher Full Text | Free Full Text 68. Aggarwal N, Kare PK, Varshney P, et al. : Role of angiotensin converting enzyme and angiotensinogen gene polymorphisms in angiotensin converting enzyme inhibitor-mediated antiproteinuric action in type 2 diabetic nephropathy patients. World J. Diabetes. 2017 Mar 15; 8 (3): 112–119. PubMed Abstract | Publisher Full Text | Free Full Text 69. Wyawahare M, Neelamegam R, Vilvanathan S, et al. : Association of Angiotensin-Converting Enzyme Gene Polymorphisms and Nephropathy in Diabetic Patients at a Tertiary Care Centre in South India. Clin. Med. Insights Endocrinol. Diabetes. 2017; 10 : 117955141772677. Publisher Full Text 70. Araz M, Yilmaz N, Güngör K, et al. : Angiotensin-converting enzyme gene polymorphism and microvascular complications in Turkish type 2 diabetic patients. Diabetes Res. Clin. Pract. 2001 Nov 1 [cited 2025 Mar 18]; 54 (2): 95–104. PubMed Abstract | Publisher Full Text Reference Source 71. Luo S, Shi C, Wang F, et al. : Association between the Angiotensin-Converting Enzyme (ACE) Genetic Polymorphism and Diabetic Retinopathy-A Meta-Analysis Comprising 10,168 Subjects. Int. J. Environ. Res. Public Health. 2016 Nov 15; 13 (11): 1142. PubMed Abstract | Publisher Full Text | Free Full Text 72. Uddin M, Azam M, Chowdhury N, et al. : Angiotensin I-Converting Enzyme Gene Polymorphism in Type 2 Diabetic Patients with Nephropathy. J. Med. Sci. 2007 May 1 [cited 2025 Mar 18]; 7 (4): 682–685. Publisher Full Text Reference Source 73. Parchwani DN, Palandurkar KM, Hema Chandan Kumar D, et al. : Genetic Predisposition to Diabetic Nephropathy: Evidence for a Role of ACE (I/D) Gene Polymorphism in Type 2 Diabetic Population from Kutch Region. Indian J. Clin. Biochem. 2015 Jan; 30 (1): 43–54. PubMed Abstract | Publisher Full Text | Free Full Text 74. Arfa I, Abid A, Nouira S, et al. : Lack of association between the angiotensin-converting enzyme gene (I/D) polymorphism and diabetic nephropathy in Tunisian type 2 diabetic patients. J. Renin-Angiotensin-Aldosterone Syst. 2008 Mar 1 [cited 2025 Mar 18]; 9 (1): 32–36. PubMed Abstract | Publisher Full Text 75. Mansoor Q, Bilal N, Qureshi S, et al. : Gender Based Disparities in ACE I/D Polymorphism Associated with Progression of Diabetic Nephropathy in Pakistani Patients with Type 2 Diabetes Mellitus. Int. J. Diabetes Metab. 2019 Mar 1 [cited 2023 Oct 14 Mar 1 [cited 2023 Oct 14]; 18 (2): 67–71. Publisher Full Text 76. Hadjadj S, Gallois Y, Alhenc-Gelas F, et al. : Angiotensin-I-converting enzyme insertion/deletion polymorphism and high urinary albumin concentration in French Type 2 diabetes patients. Diabet. Med. 2003 Aug; 20 (8): 677–682. PubMed Abstract | Publisher Full Text 77. Hsieh M, Lin S, Hsieh T, et al. : Increased frequency of angiotensin-converting enzyme DD genotype in patients with type 2 diabetes in Taiwan. Nephrol. Dial. Transplant. 2000 Jul 1 [cited 2025 Mar 16]; 15 (7): 1008–1013. PubMed Abstract | Publisher Full Text 78. Huang XH, Rantalaiho V, Wirta O, et al. : Angiotensin-converting enzyme insertion/deletion polymorphism and diabetic albuminuria in patients with NIDDM followed Up for 9 years. Nephron. 1998 Sep; 80 (1): 17–24. PubMed Abstract | Publisher Full Text 79. Mahwish UN, Ponnaluri KC, Heera B, et al. : Link between ACE I/D Gene Polymorphism and Dyslipidemia in Diabetic Nephropathy: A Case-control Study from Hyderabad, India. Indian J. Nephrol. 2020 [cited 2023 Oct 14]; 30 (2): 77–84. PubMed Abstract | Publisher Full Text Reference Source 80. Fathi M, Nikzamir AR, Esteghamati A, et al. : Combination of Angiotensin Converting Enzyme Insertion/Deletion (I/D) (rs4646994) and VEGF Polymorphism (+405G/C; rs2010963) Synergistically Associated With the Development, of Albuminuria in Iranian Patients With Type 2 Diabetes. Iran Red Crescent Med. J. 2015 Feb 21 [cited 2025 Mar 17]; 17 (2): e19469. PubMed Abstract | Publisher Full Text Reference Source 81. Kuramoto N, Iizuka T, Ito H, et al. : Effect of ACE gene on diabetic nephropathy in NIDDM patients with insulin resistance. Am. J. Kidney Dis. 1999 Feb; 33 (2): 276–281. PubMed Abstract | Publisher Full Text 82. Young RP, Chan JC, Critchley JA, et al. : Angiotensinogen T235 and ACE insertion/deletion polymorphisms associated with albuminuria in Chinese type 2 diabetic patients. Diabetes Care. 1998 Mar; 21 (3): 431–437. PubMed Abstract | Publisher Full Text 83. Yoshida H, Kuriyama S, Atsumi Y, et al. : Angiotensin I converting enzyme gene polymorphism in non-insulin dependent diabetes mellitus. Kidney Int. 1996 Aug 1 [cited 2023 Apr 8]; 50 (2): 657–664. Publisher Full Text Reference Source 84. Kimura H, Gejyo F, Suzuki Y, et al. : Polymorphisms of angiotensin converting enzyme and plasminogen activator inhibitor-1 genes in diabetes and macroangiopathy1. Kidney Int. 1998 Nov 1 [cited 2025 Mar 16]; 54 (5): 1659–1669. PubMed Abstract | Publisher Full Text Reference Source 85. Okuno S, Utsugi T, Ohno T, et al. : Angiotensin-Converting Enzyme Gene Polymorphism as a Potent Risk Factor for Developing Microalbuminuria in Japanese Patients with Type 2 Diabetes Mellitus: A 9-Year Follow-up Study. J. Int. Med. Res. 2003 Aug 1 [cited 2025 Mar 18]; 31 (4): 290–298. PubMed Abstract | Publisher Full Text 86. Ohno T, Kawazu S, Tomono S: Association analyses of the polymorphisms of angiotensin-converting enzyme and angiotensinogen genes with diabetic nephropathy in Japanese non-insulin-dependent diabetics. Metab. Clin. Exp. 1996 Feb 1 [cited 2025 Mar 16]; 45 (2): 218–222. PubMed Abstract | Publisher Full Text Reference Source 87. Nikzamir A, Esteghamati A, Feghhi M, et al. : The insertion/deletion polymorphism of the angiotensin-converting enzyme gene is associated with progression, but not development, of albuminuria in Iranian patients with type 2 diabetes. J. Renin-Angiotensin-Aldosterone Syst. 2009 Jun 1 [cited 2025 Mar 18]; 10 (2): 109–114. Publisher Full Text 88. Solini A, Giacchetti G, Sfriso A, et al. : Polymorphisms of angiotensin-converting enzyme and angiotensinogen genes in type 2 diabetic sibships in relation to albumin excretion rate. Am. J. Kidney Dis. 1999 Dec; 34 (6): 1002–1009. PubMed Abstract | Publisher Full Text 89. Movva S, Alluri RV, Komandur S, et al. : Relationship of angiotensin-converting enzyme gene polymorphism with nephropathy associated with Type 2 diabetes mellitus in Asian Indians. J. Diabetes Complicat. 2007 Jul 1 [cited 2025 Mar 18]; 21 (4): 237–241. PubMed Abstract | Publisher Full Text Reference Source 90. Schmidt S, Schöne N, Ritz E: Association of ACE gene polymorphism and diabetic nephropathy? The Diabetic Nephropathy Study Group. Kidney Int. 1995 Apr; 47 (4): 1176–1181. Publisher Full Text 91. Kiconco R, Kalyesubula R, Kiwanuka GN: Distribution of the ACE Gene Polymorphisms in Type 2 Diabetes Mellitus Patients, Their Associations with Nephropathy Biomarkers and Metabolic Indicators at a Tertiary Hospital in Uganda. Diabetes Metab. Syndr. Obes. 2024 Dec 31 [cited 2025 Mar 16]; 17 : 2211–2220. PubMed Abstract | Publisher Full Text | Free Full Text 92. Shaikh R, Shahid SM, Mansoor Q, et al. : Genetic variants of ACE (Insertion/Deletion) and AGT (M268T) genes in patients with diabetes and nephropathy. J. Renin-Angiotensin-Aldosterone Syst. 2014 Jun 1 [cited 2025 Mar 16]; 15 (2): 124–130. PubMed Abstract | Publisher Full Text 93. Brooke BS, Schwartz TA, Pawlik TM: MOOSE Reporting Guidelines for Meta-analyses of Observational Studies. JAMA Surg. 2021 Aug 1 [cited 2025 Jun 9]; 156 (8): 787–788. Publisher Full Text 94. Jackson D, Bowden J, Baker R: How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts? J. Stat. Plan. Inference. 2010 Apr 1 [cited 2025 Jun 13]; 140 (4): 961–970. Publisher Full Text Reference Source 95. Li ZW, Kun YS, Song N, et al. : The impact of angiotensin converting enzyme insertion/deletion gene polymorphism on diabetic kidney disease: A debatable issue. Nefrología (English Edition). 2022 Jul 1 [cited 2023 Apr 8]; 42 (4): 415–431. Publisher Full Text Reference Source 96. Schnuelle P: Renal Biopsy for Diagnosis in Kidney Disease: Indication, Technique, and Safety. J. Clin. Med. 2023 Oct 9 [cited 2025 Jun 2]; 12 (19): 6424. PubMed Abstract | Publisher Full Text | Free Full Text 97. Peces R, De Sousa E, Peces C: Renal biopsies in special situations. Nefrología (English Edition). 2011 Nov 1 [cited 2025 Jun 2]; 31 (6): 627–629. Reference Source 98. Mooyaart AL, Valk EJJ, van Es LA , et al. : Genetic associations in diabetic nephropathy: a meta-analysis. Diabetologia. 2011 Mar; 54 (3): 544–553. PubMed Abstract | Publisher Full Text | Free Full Text 99. Persson F, Rossing P: Diagnosis of diabetic kidney disease: state of the art and future perspective. Kidney Int. Suppl. (2011). 2018 Jan; 8 (1): 2–7. PubMed Abstract | Publisher Full Text | Free Full Text 100. Palmer ND, Freedman BI: Insights into the Genetic Architecture of Diabetic Nephropathy. Curr. Diab. Rep. 2012 Aug [cited 2025 Apr 19]; 12 (4): 423–431. PubMed Abstract | Publisher Full Text | Free Full Text 101. Gómez-Montañez E, Rojas-Salazar YL, Sánchez-Mendoza A, et al. : Cell death process associated with the renin-angiotensin-aldosterone system. Revista médica del Hospital General de México. 2024 Dec [cited 2025 Apr 19]; 87 (4): 175–183. Publisher Full Text Reference Source 102. Crisan D, Carr J: Angiotensin I-Converting Enzyme. J. Mol. Diagn. 2000 Aug [cited 2025 Apr 19]; 2 (3): 105–115. PubMed Abstract | Publisher Full Text | Free Full Text 103. Egger M, Davey Smith G, Schneider M, et al. : Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997 Sep 13; 315 (7109): 629–634. PubMed Abstract | Publisher Full Text | Free Full Text 104. Lee YJ, Tsai JCR: ACE Gene Insertion/Deletion Polymorphism Associated With 1998 World Health Organization Definition of Metabolic Syndrome in Chinese Type 2 Diabetic Patients. Diabetes Care. 2002 Jun 1 [cited 2023 Oct 14]; 25 (6): 1002–1008. PubMed Abstract | Publisher Full Text 105. Kiconco R: ACE I/D gene mutations in diabetic nephropathy.2025 Jun 30 [cited 2025 Jun 30]. Reference Source Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 17 Jul 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Medical Laboratory Science, Mbarara University of Science and Technology, Mbarara, Western Region, Uganda 2 Department of Biochemistry, Soroti University, Soroti, Eastern Region, Uganda 3 Departments of Physiology and Internal Medicine, Makerere University, Kampala, Central Region, Uganda 4 Department of Pathology, Mbarara University of Science and Technology, Mbarara, Western Region, Uganda 5 Malaria Division, Ministry of Health, Kampala, Uganda 6 Department of Biological Sciences, Kisii University, Kisii, Kisii County, Kenya 7 Department of Biochemistry, Mbarara University of Science and Technology, Mbarara, Western Region, Uganda 8 Sir Albert Cook Memorial Library, Makerere University, Kampala, Central Region, Uganda 9 Africa Center for Systematic Reviews and Knowledge Translation, College of Health Sciences, Makerere University, Kampala, Central Region, Uganda 10 Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, University of London, London, England, UK Ritah Kiconco Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Robert Kalyesubula Roles: Conceptualization, Formal Analysis, Funding Acquisition, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing Jazira Tumusiime Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Visualization, Writing – Review & Editing Raymond Atwine Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing – Review & Editing Charles Nkubi Bagenda Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Visualization, Writing – Review & Editing Simon Peter Rugera Roles: Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Resources, Software, Validation, Writing – Review & Editing Bosco Bekiita Agaba Roles: Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing – Review & Editing Erick Nyakundi Ondari Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing – Review & Editing Deusdedit Tusubira Roles: Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – Review & Editing Martha Lyaka Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Writing – Review & Editing Moses Ocan Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Review & Editing Alison Annet Kinengyere Roles: Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Review & Editing Ekwaro A. Obuku Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Review & Editing Gertrude N. Kiwanuka Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The research reported in this publication was supported by the Fogarty International Center of the National Institutes of Health under Award Number D43TWO11632. The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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