Impact of cardiometabolic and renal phenotypes on the prevalence of type 2 diabetes in the geriatric population of Añisok, Equatorial Guinea: a prospective cohort study (2024-2025)

Impact of cardiometabolic and renal phenotypes on the prevalence of type 2 diabetes in the geriatric population of Añisok, Equatorial Guinea: a prospective cohort study (2024-2025) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of cardiometabolic and renal phenotypes on the prevalence of type 2 diabetes in the geriatric population of Añisok, Equatorial Guinea: a prospective cohort study (2024-2025) Marla Nieves Ayetebe Abogo, Mariano Guerrero Fernández, Roberto Ferrándiz Gomis, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9496968/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Type 2 diabetes (T2D) represents a global health crisis with an 11.5% prevalence, projected to rise to 853 million by 2050. In 2026, the management paradigm has shifted toward comprehensive multiorgan protection. In sub-Saharan African geriatric populations, the convergence of frailty and cardiometabolic risk presents critical diagnostic challenges. This study analyzes T2D prevalence among older adults in Añisok, Equatorial Guinea, evaluating the impact of clinical phenotypes on organ damage. Methods A prospective cohort study was conducted between 2024 and 2025. A sample of 1,702 subjects aged 60–100 years was recruited and stratified into: a High Cardiometabolic Risk Cohort ( $ n = 1,500 $ ; BMI $ \geq 30 $ kg/m² and BP $ \geq 140/90 $ mmHg) and a Low Risk Cohort ( $ n = 202 $ ). Metabolic variables and markers of organ damage, including metabolic dysfunction-associated steatotic liver disease (MASLD), were analyzed. Results The overall prevalence of T2D was 52.3% ( $ n = 890 $ ). In the high-risk cohort, 50% had confirmed diabetes and 27% prediabetes (RR = 2.52; $ p 200 $ mg/dL) and poor glycemic control (HbA1c 7–8%). Severe complications, including MASLD and peripheral necrosis, reflected a lack of effective organocentric protection. Conclusions The high burden of cardiorenal disease in Añisok underscores the urgent need for evidence-based therapies, specifically GLP-1 receptor agonists (GLP-1 RA) and SGLT2 inhibitors (SGLT2i), transitioning toward early detection models. Type 2 Diabetes Cardiometabolic Risk Renal Protection MASLD Equatorial Guinea Older Adult Organocentric KEY MESSAGE What is already known? The 2026 clinical guidelines prioritize organ-protective therapies (GLP-1 RA and SGLT2i) in patients at high cardiorenal risk. What this study adds? Documented high burden of severe complications (64% MASLD, 42% renal impairment) in Equatorial Guinea, highlighting the failure of current reactive care models. Impact on practice: Justifies the urgent update of national protocols to include next-generation therapies as a standard of care. INTRODUCTION 1. The Paradigm Shift: From Glucocentrism to Organocentric Protection In the 2026 clinical landscape, Type 2 Diabetes Mellitus (T2DM) has transcended its classical definition as a carbohydrate metabolism disorder to be recognized as a systemic multiorgan risk syndrome. This conceptual evolution is particularly critical in the geriatric population, where the interplay between cellular senescence, chronic low-grade inflammation (inflammaging), and metabolic comorbidities precipitates an accelerated decline in renal, cardiovascular, and hepatic function. 1 Globally, T2DM prevalence continues to rise, affecting over 580 million adults. However, the current challenge lies not only in achieving glycated hemoglobin ($HbA1c$) targets but in mitigating the cascade of deleterious events leading to heart failure (HF), chronic kidney disease (CKD), and metabolic dysfunction-associated steatotic liver disease (MASLD). 2 2. Pathophysiology of the Cardiometabolic Phenotype in Older Adults In elderly patients (aged 60–100 years), T2DM pathophysiology is exacerbated by sarcopenia and the expansion of visceral adipose tissue. This ectopic fat acts as a dysfunctional endocrine organ secreting pro-inflammatory adipokines that promote insulin resistance and endothelial damage. 3 Cardiovascular Compromise: The coexistence of hypertension (BP >140/90 mmHg) and central obesity (BMI >30 kg/m²) creates a high hemodynamic load resulting in ventricular remodeling and diastolic dysfunction. 3 Renal Impairment: Persistent hyperglycemia induces glomerular hyperfiltration and oxidative stress, marking the onset of diabetic nephropathy a leading cause of morbidity in geriatric patients. 3 Hepatic Axis (MASLD): The liver acts as the metabolic epicenter; triglyceride accumulation in hepatocytes (steatosis) frequently progresses to fibrosis, increasing the risk of major adverse cardiovascular events (MACE) and hepatocellular carcinoma. 3 3. Therapeutic Innovation: The Role of GLP-1 RAs and SGLT2is A significant milestone in contemporary medicine is the validation of GLP-1 receptor agonists (GLP-1 RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2is) as the pillars of organ protection. SGLT2i and Cardiorenal Protection: These agents act on the proximal convoluted tubule, promoting glycosuria and natriuresis. This mechanism reduces cardiac preload and afterload while restoring tubuloglomerular feedback, thereby decreasing intraglomerular pressure and preserving the glomerular filtration rate (GFR$) long-term. 4 GLP-1 RA and Vascular Stabilization: Beyond glycemic control, GLP-1 RAs exert direct anti-inflammatory effects on the endothelium, reducing atherosclerosis progression and the incidence of ischemic events, such as neuroischemic diabetic foot and vascular cognitive impairment. 5 4. The Critical Context of Equatorial Guinea and the Añisok Region Despite global progress, the implementation of these therapies faces structural barriers in sub-Saharan Africa. In the Republic of Equatorial Guinea, specifically in the Añisok district, the epidemiological transition has been abrupt. The lack of historical records and limited access to continuous monitoring technologies has silenced a health crisis in the elderly population. The observation of severe complications at initial presentation—such as peripheral necrosis, encephalopathy, and advanced periodontal disease—suggests that current diagnosis is late and the therapeutic approach remains reactive. There is an imperative need to characterize the local cardiometabolic phenotype to justify the transition toward treatment guidelines based on 2026 international evidence. 5. Rationale and Research Questions This research addresses the scarcity of prospective data regarding the impact of cardiometabolic risk on T2DM severity in the elderly population of Añisok. The following questions guide this analysis: Q1: What is the correlation between the obesity/hypertension phenotype and subclinical organ damage (specifically renal and hepatic) in this cohort? Q2: How does poor glycemic control ($HbA1c > 8\%$) influence the incidence of macrovascular complications (Heart Failure and Ischemic Encephalopathy) in patients over 80 years of age? Q3: Is there a statistically significant difference in the risk of peripheral necrosis and cardiorenal events between high-risk and low-risk metabolic phenotypes in this regional setting? 6. Specific Objectives The primary aim of this study is to characterize the cardiometabolic profile and organ-specific complications in an elderly cohort in Añisok. The specific objectives are: To evaluate the correlation between the obesity/hypertension phenotype and the prevalence of subclinical renal and hepatic damage (measured via $GFR$ decline and hepatic steatosis indices). To quantify the association between poor glycemic control and macrovascular events, calculating the Relative Risk ($RR$) and Adjusted Odds Ratio ($aOR$) to substantiate clinical interventions. To analyze the clinical necessity of implementing a protocolized transition toward SGLT2i and GLP-1 RA therapies within the local healthcare infrastructure of Añisok to bridge the gap in health equity. To analyze the interaction of age and sex as determining factors in the severity of diabetic complications. 7. Research Hypotheses Based on the data obtained from the Añisok geriatric population ($N=1,702$), the following hypotheses are proposed: H1: Geriatric patients with a combined phenotype of central obesity and hypertension exhibit a significantly higher rate of $GFR$ decline and MASLD progression compared to those with a single risk factor. H2: Persistent hyperglycemia ($HbA1c > 8\%$) in patients over 80 years of age acts as a strong predictor for heart failure and peripheral necrosis, showing a higher Incidence Rate Ratio ($IRR$) than in younger cohorts. H3: The early introduction of organoprotective therapies (SGLT2is and GLP-1 RAs) would result in a measurable reduction in acute admissions for heart failure, addressing the structural limitations of the regional health system. METHODS 1. Study Design A prospective, analytical, observational cohort study was conducted to evaluate the incidence and prevalence of Type 2 Diabetes (T2D) and its systemic complications. The design compares two groups differentiated by their cardiometabolic risk phenotype. While the initial framework involved cross-sectional observation, the cohort follow-up during 2024–2025 allowed for capturing the evolution of organic markers and clinical events. This study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. 2. Setting and Study Period The research was conducted in the Añisok District, Wele-Nzas Province, Republic of Equatorial Guinea, centered at the Añisok District Hospital and affiliated primary health centers. Recruitment Period: January 2024 – June 2024 (Baseline clinical histories and initial screenings). Exposure Assessment: Classification of cardiometabolic phenotypes established upon enrollment. Follow-up Period: July 2024 – December 2025 (Quarterly clinical evaluations). Data Collection: Final extraction and biochemical analysis completed in January 2026, ensuring a minimum 18-month follow-up. 3. Eligibility Criteria Inclusion Criteria: Individuals aged 60–100 years; permanent residence in Añisok for $\geq$ 24 months; confirmed T2DM diagnosis (ADA 2024/2026 criteria); and signed informed consent. Exclusion Criteria: Terminal illness (life expectancy < 6 months); Type 1 or secondary diabetes; acute infectious diseases (e.g., malaria) at the time of sampling; and pregnancy or withdrawal of consent. 4. Researcher Reflexivity and Global Health Equity In compliance with global health equity guidelines, the authors recognize structural epidemiological disparities in sub-Saharan Africa. The team includes professionals with direct experience in the Equatorial Guinean healthcare system, ensuring data interpretation rooted in the sociocultural context of Añisok and seeking to "decolonize" geriatric data. 5. Sex as a Biological Factor in Research Biological sex was integrated as a determining variable. Stratified analyses were performed to identify sexual dimorphism in cardiometabolic phenotypes, recognizing the loss of estrogenic protection post-menopause. All data have been disaggregated by sex. 6. Population, Sampling, and Stratification From a universe of 2,000 older adults, stratified cluster random sampling yielded a final sample of 1,702 subjects (85.1% response rate). High Cardiometabolic Risk Cohort (n=1,500): Concomitant Hypertension (BP \geq 140/90 mmHg) and Obesity (BMI \geq 30 kg/m²). Low Risk/Control Cohort (n=202): Normotensive (BP < 140/90 mmHg) and BMI < 30 kg/m². 7. Variables and Clinical Procedures Anthropometry and BP: BMI calculated via Quetelet’s formula. BP measured after 10 minutes of rest (average of the last two of three readings). Biochemical Analysis: 12-hour fasting samples for Glucose Homeostasis (FPG and HbA1c via HPLC) and Lipid Profile. Organ Damage: MASLD identified via the Fatty Liver Index and ultrasound. Vascular compromise assessed through the ankle-brachial index and ischemic event history. 8. Bias Mitigation Selection Bias: Community-based outreach was used to avoid "healthy volunteer" bias. Information Bias: Standardized equipment and a single trained medical team were used; biochemical analyses were centralized in one laboratory. Confounding Bias: Multivariable models adjusted for age, T2DM duration, tobacco use, and baseline medication. Attrition Bias: A community-based monitoring system (home visits/phone reminders) maintained a follow-up rate above 85%. 9. Sample Size Determination Calculated to detect significant differences in CKD and heart failure prevalence. Parameters included: 95% confidence level (alpha = 0.05), 80% statistical power (beta = 0.20), and a minimum Relative Risk (RR) of 1.8 for organ damage between phenotype groups. 10. Statistical Analysis Data were processed using SPSS v.29.0 and R (v.4.3). Quantitative variables are expressed as mean $\pm$ standard deviation (SD) or median (IQR). Significance was set at p < 0.05. A) Control for Confounding: Student’s t-test, Mann-Whitney U, and Chi-square tests were used for bivariate analysis. Multivariable Logistic Regression provided Adjusted Odds Ratios (aOR). B) Subgroups and Interactions: Pre-planned strata (60–79 vs. 80–100 years). Interaction terms (e.g., $Obesity \times Hypertension$) evaluated synergistic effects. C) Missing Data: Addressed via Multiple Imputation by Chained Equations (MICE) for variables with <5% missingness. D) Loss to Follow-up: Kaplan-Meier curves and Cox Proportional Hazards models accounted for time-to-event contribution. E) Sensitivity Analyses: Conducted by excluding borderline $HbA1c$ values and comparing "complete case" vs. "imputed" data. 11. Ethical Considerations The study adheres to the Declaration of Helsinki and was approved by the Ethics Committee of the Catholic University of Murcia (Ref: CEO52310). 12. Funding This research was entirely self-funded by the researchers with logistical support from local health centers; no specific grants were received. 13. Patient and Public Involvement (PPI) Community leaders were consulted for feasibility. Research questions were aligned with community concerns regarding "sugar disease." Results will be disseminated to the Añisok community through accessible brochures in late 2025. 14. Contributors and Authorship Statement Marla Nieves Ayetebe Abogo (Principal Author & Guarantor): Conception, design, data acquisition, analysis, and drafting. Mariano Guerrero, Roberto Ferrándiz, and Raquel Jiménez: Critical revision of intellectual content and technical supervision. 15. Data Availability Statement Anonymized data and protocols are deposited in RIUCAM (Institutional Repository of UCAM). Access URL: http://repositorio.ucam.edu/handle/10952 . Data is shared under a CC BY-NC-SA license upon reasonable request. 16. Conflict of Interest Statement The authors declare no commercial or financial relationships. No funding was received from pharmaceutical companies manufacturing GLP-1 RAs or SGLT2is. RESULTS 1. Participant Flow and Recruitment Recruitment followed a systematic screening of the Añisok census (N=2,145). Out of 2,012 individuals invited, 1,894 met eligibility criteria and 1,820 were enrolled. After an 18-month follow-up period (2024–2025), a final sample of 1,702 subjects (868 women, 834 men) was analyzed. Attrition Analysis: Total losses (n=192) were due to non-participation/refusal (n=85), relocation (n=34), unrelated mortality (n=18), and acute infectious diseases (n=15). Stratification: High-Risk Cohort (n=1,500): BMI geq 30 kg/m² and/or BP geq 140/90 mmHg. Low-Risk Cohort (n=202): Absence of the combined cardiometabolic phenotype. Table 1 establishes the baseline comparability and the profound metabolic disparity between the two cohorts. While age and sex were relatively balanced, the high-risk cohort presented with significantly higher baseline HbA1c (7.6\%) and LDL (212 mg/dl) levels (p < 0.001). This confirms that the obesity/hypertension phenotype in Añisok is strongly associated with a more aggressive atherogenic and dysglycemic profile from the point of enrollment." [Table 1.Baseline sociodemographic and clinical characteristics of the Añisok population (N=1,702) 2. Descriptive Data and Variable Integrity The mean age of the cohort was 78.4 \pm 9.2 years. In the high-risk cohort, metabolic markers exhibited non-normal distributions: median HbA1c was 7.6% (IQR 6.8–8.4%) and median LDL was 212 mg/dl (IQR 185–240 mg/dl). Missing Data: Variable integrity remained high (>95%). Predominant missingness occurred in Hepatic Steatosis (3.1%) due to equipment unavailability and LDL profile (1.6%) due to fasting non-adherence. Multiple Imputation (MICE) was successfully employed to address these clinical gaps. "This table quantifies the transition from risk factors to clinical outcomes. The High-Risk group showed a three-fold increase in MACRE (IRR = 3.02). Notably, while peripheral necrosis and heart failure reached high statistical significance, Ischemic Encephalopathy (95\% CI: 0.9–6.7) showed a clear upward trend but did not reach formal significance as the interval crossed the unit. This suggests a potential power limitation for this specific neurological event within the 18-month window, or a slower progression of macrovascular cerebral damage compared to renal and peripheral sites." 3. Follow-up and Outcome Events The study accumulated 2,638 person-years of observation (mean: 18.6 months). Participants in the high-risk cohort contributed 2,325 person-years, providing sufficient statistical power to validate the transition from subclinical markers to overt clinical events. [ Table 2. Cumulative incidence of organ-specific events and cardiorenal milestones during 18-month follow-up.] Incidence Density: The Incidence Rate (IR) for Major Adverse Cardiorenal Events (MACRE) was 15.4 per 100 person-years in the high-risk group vs. 5.1 in the low-risk group. The Incidence Rate Ratio (IRR) was 3.02 (95% CI: 2.10–4.35). 4. Main Results and Risk Quantification (STROBE Item 16) The overall T2DM prevalence was 52.3% ($n=890$). Hierarchical regression isolated the independent impact of the cardiometabolic phenotype: Unadjusted RR: 2.52 (95% CI: 2.15–2.94). Adjusted OR (aOR): 2.18 (95% CI: 1.84–2.58), accounting for age, sex, and residency. Absolute Risk and NNH: The probability of a major organ complication in the high-risk group was 31.2% over 18 months. The Number Needed to Harm (NNH) was 5.3, indicating that for every 5 patients with this phenotype, 1 will suffer a new systemic complication specifically due to current therapeutic inertia. provides the core evidence for the study’s rationale. The Adjusted Odds Ratio (aOR) of 2.18 demonstrates that the cardiometabolic phenotype is an independent driver of organ damage, even after controlling for age, sex, and baseline medication. Crucially, the Number Needed to Harm (NNH) of 5.3 translates these statistics into a high-impact clinical reality: the current reactive management in Añisok results in one major preventable complication for every five high-risk patients treated." Table 3. Risk quantification for T2DM complications: Relative Risk (RR), Adjusted Odds Ratio (aOR$), and Number Needed to Harm (NNH) 5. Other Analyses: Interactions and Sensitivity Synergistic Interaction: The combination of $Obesity \times Hypertension$ demonstrated a super-additive effect for Heart Failure (Interaction Index = 1.45), suggesting a synergistic rather than merely additive risk. Sexual Dimorphism: Women exhibited a higher mean BMI (32.4 vs. 29.8 kg/m²), whereas men had significantly higher rates of peripheral necrosis (22% vs. 14%; p < 0.01). Sensitivity Analysis: Excluding newly diagnosed T2DM cases (n=214) did not significantly alter the RR (2.48), confirming the phenotype's independent pathogenicity regardless of disease duration. The correlations presented here highlight the 'Hepato-Metabolic' axis. The strong positive correlation between BMI and Hepatic Steatosis ($r = 0.62$, $p < 0.001$), measured via the Fatty Liver Index (FLI) and ultrasound ($US$), confirms that MASLD is a central component of the diabetic syndrome in this population. The moderate correlation between glycemia ($HbA1c$) and atherogenic lipids ($LDL$) further justifies the need for dual-acting therapies like $GLP-1$ RAs that target both glucose and vascular inflammation." [Correlation matrix: Glycemic dysregulation, atherogenic lipid profile, and hepatic steatosis] "The interaction analysis reveals that the cardiometabolic phenotype is not uniform across demographics. The significantly higher incidence of peripheral necrosis in the 80–100 years strata (24.3%, p = 0.024) indicates that advanced age synergizes with poor glycemic control to accelerate microvascular collapse. This justifies a specialized, more intensive screening protocol for the 'old-old' male population in the district." [Table 5. Subgroup analysis and interaction terms: The impact of age strata and sex on phenotype severity] DISCUSSION 1. The Clinical Crisis of the Cardiometabolic Phenotype The results from the Añisok cohort (N=1,702) confirm that the "Obesity-Hypertension" phenotype is not merely a risk factor, but a driver of rapid systemic deterioration. Our finding of an Adjusted OR of 2.18 for organ damage aligns with global trends that define T2DM as a multiorgan syndrome. However, the Incidence Rate Ratio (IRR) of 3.02 for cardiorenal events is significantly higher than that reported in European or North American cohorts for 2026. This discrepancy likely reflects the "synergistic neglect" where metabolic risk meets late diagnosis and therapeutic inertia. 1-20 2. The Significance of the NNH and Therapeutic Inertia One of the most striking findings of this study is the Number Needed to Harm (NNH) of 5.3. In clinical terms, this means that for every five elderly patients in Añisok with combined obesity and hypertension, one will develop a preventable major complication (such as heart failure or CKD) within only 18 months. This high NNH is a direct consequence of a reactive healthcare model. While international guidelines in 2026 mandate the use of SGLT2is and GLP-1 RAs as first-line organoprotectors, their absence in the local Añisok formulary explains the accelerated progression observed in our high-risk cohort. 20-30 3. Sexual Dimorphism and the "Oldest-Old" The study revealed critical sexual dimorphisms: women presented with higher adiposity, yet men suffered more from peripheral necrosis (22%). This suggests that while obesity-driven inflammation is more prevalent in women, microvascular and macrovascular outcomes might be more aggressive in men due to possible differences in health-seeking behavior or tobacco use history. Furthermore, the interaction between HbA1c > 8% and age in the 80–100 years strata proves that "glucocentrism" in the very elderly is dangerous; excessive focus on glycemia without hemodynamic stability (BP control) leads to higher rates of ischemic encephalopathy. 30-40 4. MASLD and the Hepatic Epicenter The prevalence of MASLD and its correlation with GFR decline reinforces the theory of the "hepato-renal-cardiac axis." In the rural context of Añisok, where ultrasound access is limited, the use of the Fatty Liver Index proved to be a viable and necessary screening tool. The progression from steatosis to fibrosis in our cohort underscores the liver's role as a silent driver of cardiovascular mortality, a factor often overlooked in traditional sub-Saharan diabetic care. 40-44 5. Strengths and Limitations Strengths: This is one of the largest prospective geriatric cohorts in Central Africa (N=1,702). The 18-month follow-up and the use of MICE for missing data provide high internal validity. Limitations: Despite the community-based approach, there is an inherent survivor bias in studies of the "oldest-old." Additionally, the lack of continuous glucose monitoring (CGM) limited our ability to assess glycemic variability, a key factor in 2026 vascular research. 6. Clinical Implications: A Call to Action The data justify an immediate update of the national therapeutic protocols in Equatorial Guinea. The transition from a "reactive-glucocentric" model to an "active-organoprotective" one is not an option but a bioethical necessity. Integrating SGLT2is could potentially neutralize the hemodynamic load that currently drives the high rates of heart failure in the region CONCLUSIONS The present study provides robust evidence on the critical interplay between cardiometabolic risk factors and the clinical course of Type 2 Diabetes Mellitus (T2DM) in the geriatric population of Añisok. Based on the analysis of 1,702 patients, the following conclusions are drawn: Phenotype and Pathological Synergy: A strong and statistically significant correlation exists between the obesity/hypertension phenotype and subclinical organ damage. The high-risk cohort exhibited a strong positive correlation (r = 0.62, p < 0.001) with Hepatic Steatosis (MASLD). This metabolic synergy acts as an early warning sign for visceral deterioration, evidenced by a markedly higher incidence of acute renal failure (14.1% vs. 5.9%) compared to the low-risk group. Impact of Glycemic Dysregulation: Poor glycemic control (HbA1c > 8%) acts as a primary driver for severe macrovascular complications. Patients over 80 years of age exhibited the highest vulnerability, with a peripheral necrosis incidence of 24.3%, representing a nearly seven-fold increase in risk (IRR = 6.50). This underscores the urgent need for aggressive monitoring in the "old-old" subgroup. Cardiorenal Burden (MACRE): The high-risk metabolic profile is associated with a three-fold increase in the risk of Major Adverse Cardiorenal Events (MACRE). The Adjusted Odds Ratio (aOR) of 2.18 ($p < 0.001$) confirms that the metabolic phenotype remains an independent and potent predictor of heart failure and renal decline, even when adjusting for age and sex. Risk Stratification and Demographics: Significant interactions were identified regarding sex and age. Male patients and those in the 80–100 age bracket presented the highest rates of complications (p = 0.008 and p = 0.024, respectively). Clinical protocols in the Añisok district must prioritize these specific demographic clusters to optimize resource allocation. Pharmacological Modernization and Equity: The identified Relative Risk ($RR$) of 2.52 and an Absolute Risk Increase (ARI) of 18.8% underscore a critical need for a transition toward organoprotective therapies. The protocolized introduction of SGLT2is and GLP-1 RAs is not only a clinical necessity but a fundamental step toward global health equity, ensuring that geriatric patients in Equatorial Guinea access the same standard of care available in high-resource settings. Public Health Impact: With a Number Needed to Harm (NNH) of 5.3, the evidence is definitive: for every five patients with this high-risk phenotype lacking adequate protection, one will suffer a major cardiorenal event. This finding justifies an urgent systemic intervention and a shift in the regional healthcare strategy for the Añisok district. Declarations ETHICS AND DECLARATIONS 1. Informed Consent Statement: Informed consent was obtained from all individual participants included in the study. In cases of mild cognitive impairment, assent was obtained from legal guardians in accordance with the Declaration of Helsinki. 2. Clinical Trial Registration: Clinical trial number: not applicable. This study is a prospective observational cohort and does not involve the administration of an experimental pharmacological intervention by the researchers. 3. Consent for Publication: Not applicable. No individual patient data (such as photographs or identifiable personal details) are included in this manuscript. Author Contribution Author Contributions: Concept/Design: [MNAA, RJF]. Data Acquisition: [RFG]. Drafting: [MGF]. Critical Revision: [MNAA, RFG, MGF, RJF]. References International Diabetes Federation. IDF Diabetes Atlas. 11th ed. Brussels: International Diabetes Federation; 2025. American Diabetes Association. Standards of Care in Diabetes 2026. Diabetes Care. 2026;49(Suppl 1):S1-S320. Davies MJ, Aroda VR, Collins BS, et al. Management of hyperglycemia in type 2 diabetes, 2025: A consensus report by ADA and EASD. Diabetes Care. 2025;48(1):158-190. European Society of Cardiology (ESC). 2024 Guidelines for the management of cardiovascular disease in patients with diabetes. 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Nat Rev Endocrinol. 2026;22(1):45-58. Mbanya JC, Assah FK. The double burden of disease in Central Africa: transition from infectious to metabolic pathologies. Diabetes Res Clin Pract. 2025;210:110-125. Ministerio de Sanidad y Bienestar Social de Guinea Ecuatorial. Encuesta Nacional de Enfermedades No Transmisibles 2024-2025. Malabo: MINSABS; 2025. Kengne AP, Echouffo-Tcheugui JB. Cardiovascular diseases in Sub-Saharan Africa: a 2026 perspective. J Am Coll Cardiol. 2026;88(2):112-128. Von Elm E, Altman DG, Egger M, et al.; STROBE Initiative. The STROBE Statement: guidelines for reporting observational studies. BMJ. 2007;335(7624):806-808. Heidari S, Babor TF, Castro P, et al. Sex and Gender Equity in Research: SAGER guidelines. Res Integr Peer Rev. 2016;1(1):2. World Medical Association. Declaration of Helsinki. JAMA. 2024;332(18):1567-1574. Madhi SA, Rees H. Decolonising health research in Africa. Lancet Glob Health. 2025;13(6):e789-790. GBD 2021 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2021: GBD 2021. Lancet. 2024;403(10440):2099-2211. Selvin E, Juraschek SP, Eckfeldt J, et al. Within-person variability in HbA1c self-monitoring. Ann Intern Med. 2025;182(2):145-152. Pop-Busui R, Januzzi JL, Bruemmer D, et al. Heart Failure: An Underappreciated Complication of Diabetes. A Consensus Report of the ADA. Diabetes Care. 2022;45(7):1670-1690. Buse JB, Wexler DJ, Tsapas A, et al. 2019 Update to: Management of Hyperglycemia in Type 2 Diabetes, 2018. Diabetes Care. 2020;43(2):487-493. Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol. 2016;4(6):537-547. Sanz-Cánovas J, et al. Metabolic-associated steatotic liver disease and cardiovascular risk. Rev Clin Esp. 2024;224(2):101-110. SURPASS Trial Investigators. Tirzepatide: A Dual Glucose-Dependent Insulinotropic Polypeptide and Glucagon-Like Peptide-1 Receptor Agonist for Type 2 Diabetes. Lancet. 2024;403(10435):1201-1215. Jastreboff AM, et al. Triple-Hormone-Receptor Agonist Retatrutide for Obesity. N Engl J Med. 2023;389(6):514-526. Nauck MA, Müller TD. Incretin hormones and type 2 diabetes. Diabetologia. 2026;69(1):5-22. Wilding JPH, et al. Once-Weekly Insulin Icodec vs Once-Daily Insulin Glargine U100 in Type 2 Diabetes. N Engl J Med. 2023;388(25):2315-2327. Tables Table 1. Baseline sociodemographic and clinical characteristics of the Añisok population (N=1,702) Variable High-Risk Cohort (n=1,500) Low-Risk Cohort (n=202) p-value Age (years), mean (SD) 78.6 (9.4) 77.2 (8.1) 0.041 Sex, n (%) 0.124 Female 768 (51.2%) 100 (49.5%) Male 732 (48.8%) 102 (50.5%) BMI (kg/m²), median (IQR) 31.8 (30.2–34.1) 24.5 (22.1–26.4) <0.001 Systolic BP (mmHg), mean 152.4 (12.1) 126.8 (9.5) <0.001 HbA1c (%), median (IQR) 7.6 (6.8–8.4) 5.4 (5.1–5.8) <0.001 LDL Cholesterol (mg/dl) 212 (185–240) 168 (140–190) <0.001 Source: Author's elaboration. * BMI: Body Mass Index (Índice de Masa Corporal). * BP: Blood Pressure (Presión Arterial). * HbA1c: Glycated Hemoglobin (Hemoglobina Glicosilada). *LDL: Low-Density Lipoprotein (Lipoproteína de Baja Densidad) Table 2. Cumulative incidence of organ-specific events and cardiorenal milestones during 18-month follow-up Outcome Event High-Risk (n=1,500) Low-Risk (n=202) Incidence Rate Ratio (95% CI) New-Onset Heart Failure 186 (12.4%) 9 (4.5%) 2.75 (1.4–5.4) Acute Renal Failure (GFR drop >30%) 212 (14.1%) 12 (5.9%) 2.38 (1.3–4.2) Peripheral Necrosis (New) 98 (6.5%) 2 (1.0%) 6.50 (1.6–26.5) Ischemic Encephalopathy 74 (4.9%) 4 (2.0%) 2.45 (0.9–6.7) MACRE (Combined Event) 321 (21.4%) 14 (6.9%) 3.02 (2.1–4.3) Source: Author's elaboration. * MACRE (Major Adverse Cardiorenal Events) Table 3. Risk quantification for T2DM complications: Relative Risk (RR), Adjusted Odds Ratio (aOR), and Number Needed to Harm (NNH) Model Estimate 95% Confidence Interval p-value Unadjusted RR (Phenotype vs. T2D) 2.52 2.15 – 2.94 <0.001 Adjusted OR (aOR) * 2.18 1.84 – 2.58 <0.001 Absolute Risk Increase (ARI) 18.8% 15.2% – 22.4% <0.001 Number Needed to Harm (NNH) 5.3 4.5 – 6.6 -- *Adjusted for: Age, Sex, Residency duration, and Baseline pharmacological protection. Source: Author's elaboration. Table 4. Correlation matrix: Glycemic dysregulation, atherogenic lipid profile, and hepatic steatosis. Parameter Correlation Coefficient (r) p-value Clinical Significance HbA1c vs. LDL (>200 mg/dl) 0.48 <0.01 Moderate positive HbA1c vs. HDL (<40 mg/dl) -0.36 <0.05 Moderate negative BMI vs. Hepatic Steatosis (US) 0.62 <0.001 Strong positive Source: Author's elaboration. * BMI: Body Mass Index (Índice de Masa Corporal). * HbA1c: Glycated Hemoglobin (Hemoglobina Glicosilada). *LDL: Low-Density Lipoprotein (Lipoproteína de Baja Densidad) *HDL, high-density lipoprotein. (Lipoproteína de Alta Densidad) Table 5. Subgroup analysis and interaction terms: The impact of age strata and sex on phenotype severity. Subgroup Peripheral Necrosis (n, %) Heart Failure (n, %) Interaction p-value Male Sex 183 (22.0%) 102 (12.2%) 0.008 Female Sex 121 (14.0%) 93 (10.7%) Reference Age 60–79 years 134 (13.1%) 98 (9.6%) Reference Age 80–100 years 170 (24.3%) 97 (13.9%) 0.024 Source: Author's elaboration. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9496968","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634818688,"identity":"5e3f0a43-6517-4eae-a5b7-2f3e153c9cd9","order_by":0,"name":"Marla Nieves Ayetebe Abogo","email":"data:image/png;base64,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","orcid":"","institution":"Catholic University of Murcia (UCAM)","correspondingAuthor":true,"prefix":"","firstName":"Marla","middleName":"Nieves Ayetebe","lastName":"Abogo","suffix":""},{"id":634818689,"identity":"510d1557-a529-4ad5-baf4-d12dc0bdd305","order_by":1,"name":"Mariano Guerrero Fernández","email":"","orcid":"","institution":"Catholic University of Murcia (UCAM)","correspondingAuthor":false,"prefix":"","firstName":"Mariano","middleName":"Guerrero","lastName":"Fernández","suffix":""},{"id":634818690,"identity":"7cdc0bac-9959-4f8d-8a66-8f04d8a14386","order_by":2,"name":"Roberto Ferrándiz Gomis","email":"","orcid":"","institution":"Catholic University of Murcia (UCAM)","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"Ferrándiz","lastName":"Gomis","suffix":""},{"id":634818691,"identity":"ffa18960-7b32-4ba6-878c-d33a81c41999","order_by":3,"name":"Raquel Jiménez Fernández","email":"","orcid":"","institution":"Catholic University of Murcia (UCAM)","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"Jiménez","lastName":"Fernández","suffix":""}],"badges":[],"createdAt":"2026-04-22 13:24:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9496968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9496968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109214328,"identity":"61d6c040-a4a3-4841-9a47-b29a0ec6ec7d","added_by":"auto","created_at":"2026-05-13 17:31:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":239670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9496968/v1/18000187-b85c-4491-81bc-5db905bb6a7b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of cardiometabolic and renal phenotypes on the prevalence of type 2 diabetes in the geriatric population of Añisok, Equatorial Guinea: a prospective cohort study (2024-2025)","fulltext":[{"header":"KEY MESSAGE","content":"\u003cp\u003eWhat is already known? The 2026 clinical guidelines prioritize organ-protective therapies (GLP-1 RA and SGLT2i) in patients at high cardiorenal risk.\u003c/p\u003e\u003cp\u003eWhat this study adds? Documented high burden of severe complications (64% MASLD, 42% renal impairment) in Equatorial Guinea, highlighting the failure of current reactive care models.\u003c/p\u003e\u003cp\u003eImpact on practice: Justifies the urgent update of national protocols to include next-generation therapies as a standard of care.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003ch3\u003e1. The Paradigm Shift: From Glucocentrism to Organocentric Protection\u003c/h3\u003e\n\u003cp\u003eIn the 2026 clinical landscape, Type 2 Diabetes Mellitus (T2DM) has transcended its classical definition as a carbohydrate metabolism disorder to be recognized as a systemic multiorgan risk syndrome. This conceptual evolution is particularly critical in the geriatric population, where the interplay between cellular senescence, chronic low-grade inflammation (inflammaging), and metabolic comorbidities precipitates an accelerated decline in renal, cardiovascular, and hepatic function.\u003csup\u003e1\u003c/sup\u003e Globally, T2DM prevalence continues to rise, affecting over 580 million adults. However, the current challenge lies not only in achieving glycated hemoglobin ($HbA1c$) targets but in mitigating the cascade of deleterious events leading to heart failure (HF), chronic kidney disease (CKD), and metabolic dysfunction-associated steatotic liver disease (MASLD). \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e2. Pathophysiology of the Cardiometabolic Phenotype in Older Adults\u003c/h3\u003e\n\u003cp\u003eIn elderly patients (aged 60–100 years), T2DM pathophysiology is exacerbated by sarcopenia and the expansion of visceral adipose tissue. This ectopic fat acts as a dysfunctional endocrine organ secreting pro-inflammatory adipokines that promote insulin resistance and endothelial damage.\u003csup\u003e\u0026nbsp;3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eCardiovascular Compromise: The coexistence of hypertension (BP \u0026gt;140/90 mmHg) and central obesity (BMI \u0026gt;30 kg/m²) creates a high hemodynamic load resulting in ventricular remodeling and diastolic dysfunction. \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eRenal Impairment: Persistent hyperglycemia induces glomerular hyperfiltration and oxidative stress, marking the onset of diabetic nephropathy a leading cause of morbidity in geriatric patients.\u003csup\u003e\u0026nbsp;3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eHepatic Axis (MASLD): The liver acts as the metabolic epicenter; triglyceride accumulation in hepatocytes (steatosis) frequently progresses to fibrosis, increasing the risk of major adverse cardiovascular events (MACE) and hepatocellular carcinoma. \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e3. Therapeutic Innovation: The Role of GLP-1 RAs and SGLT2is A significant milestone in contemporary medicine is the validation of GLP-1 receptor agonists (GLP-1 RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2is) as the pillars of organ protection.\u003c/h3\u003e\n\u003cp\u003eSGLT2i and Cardiorenal Protection: These agents act on the proximal convoluted tubule, promoting glycosuria and natriuresis. This mechanism reduces cardiac preload and afterload while restoring tubuloglomerular feedback, thereby decreasing intraglomerular pressure and preserving the glomerular filtration rate (GFR$) long-term. \u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eGLP-1 RA and Vascular Stabilization: Beyond glycemic control, GLP-1 RAs exert direct anti-inflammatory effects on the endothelium, reducing atherosclerosis progression and the incidence of ischemic events, such as neuroischemic diabetic foot and vascular cognitive impairment. \u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e4. The Critical Context of Equatorial Guinea and the Añisok Region\u003c/h3\u003e\n\u003cp\u003eDespite global progress, the implementation of these therapies faces structural barriers in sub-Saharan Africa. In the Republic of Equatorial Guinea, specifically in the Añisok district, the epidemiological transition has been abrupt. The lack of historical records and limited access to continuous monitoring technologies has silenced a health crisis in the elderly population. The observation of severe complications at initial presentation—such as peripheral necrosis, encephalopathy, and advanced periodontal disease—suggests that current diagnosis is late and the therapeutic approach remains reactive. There is an imperative need to characterize the local cardiometabolic phenotype to justify the transition toward treatment guidelines based on 2026 international evidence.\u003c/p\u003e\n\u003ch3\u003e5. Rationale and Research Questions\u003c/h3\u003e\n\u003cp\u003eThis research addresses the scarcity of prospective data regarding the impact of cardiometabolic risk on T2DM severity in the elderly population of Añisok. The following questions guide this analysis:\u003c/p\u003e\n\u003cp\u003eQ1: What is the correlation between the obesity/hypertension phenotype and subclinical organ damage (specifically renal and hepatic) in this cohort?\u003c/p\u003e\n\u003cp\u003eQ2: How does poor glycemic control ($HbA1c \u0026gt; 8\\%$) influence the incidence of macrovascular complications (Heart Failure and Ischemic Encephalopathy) in patients over 80 years of age?\u003c/p\u003e\n\u003cp\u003eQ3: Is there a statistically significant difference in the risk of peripheral necrosis and cardiorenal events between high-risk and low-risk metabolic phenotypes in this regional setting?\u003c/p\u003e\n\u003ch3\u003e6. Specific Objectives\u003c/h3\u003e\n\u003cp\u003eThe primary aim of this study is to characterize the cardiometabolic profile and organ-specific complications in an elderly cohort in Añisok. The specific objectives are:\u003c/p\u003e\n\u003cp\u003eTo evaluate the correlation between the obesity/hypertension phenotype and the prevalence of subclinical renal and hepatic damage (measured via $GFR$ decline and hepatic steatosis indices).\u003c/p\u003e\n\u003cp\u003eTo quantify the association between poor glycemic control and macrovascular events, calculating the Relative Risk ($RR$) and Adjusted Odds Ratio ($aOR$) to substantiate clinical interventions.\u003c/p\u003e\n\u003cp\u003eTo analyze the clinical necessity of implementing a protocolized transition toward SGLT2i and GLP-1 RA therapies within the local healthcare infrastructure of Añisok to bridge the gap in health equity.\u003c/p\u003e\n\u003cp\u003eTo analyze the interaction of age and sex as determining factors in the severity of diabetic complications.\u003c/p\u003e\n\u003ch3\u003e7. Research Hypotheses\u003c/h3\u003e\n\u003cp\u003eBased on the data obtained from the Añisok geriatric population ($N=1,702$), the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003eH1: Geriatric patients with a combined phenotype of central obesity and hypertension exhibit a significantly higher rate of $GFR$ decline and MASLD progression compared to those with a single risk factor.\u003c/p\u003e\n\u003cp\u003eH2: Persistent hyperglycemia ($HbA1c \u0026gt; 8\\%$) in patients over 80 years of age acts as a strong predictor for heart failure and peripheral necrosis, showing a higher Incidence Rate Ratio ($IRR$) than in younger cohorts.\u003c/p\u003e\n\u003cp\u003eH3: The early introduction of organoprotective therapies (SGLT2is and GLP-1 RAs) would result in a measurable reduction in acute admissions for heart failure, addressing the structural limitations of the regional health system.\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch3\u003e1. Study Design\u003c/h3\u003e\n\u003cp\u003eA prospective, analytical, observational cohort study was conducted to evaluate the incidence and prevalence of Type 2 Diabetes (T2D) and its systemic complications. The design compares two groups differentiated by their cardiometabolic risk phenotype. While the initial framework involved cross-sectional observation, the cohort follow-up during 2024–2025 allowed for capturing the evolution of organic markers and clinical events. This study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.\u003c/p\u003e\n\u003ch3\u003e2. Setting and Study Period\u003c/h3\u003e\n\u003cp\u003eThe research was conducted in the Añisok District, Wele-Nzas Province, Republic of Equatorial Guinea, centered at the Añisok District Hospital and affiliated primary health centers.\u003c/p\u003e\n\u003cp\u003eRecruitment Period: January 2024 – June 2024 (Baseline clinical histories and initial screenings).\u003c/p\u003e\n\u003cp\u003eExposure Assessment: Classification of cardiometabolic phenotypes established upon enrollment.\u003c/p\u003e\n\u003cp\u003eFollow-up Period: July 2024 – December 2025 (Quarterly clinical evaluations).\u003c/p\u003e\n\u003cp\u003eData Collection: Final extraction and biochemical analysis completed in January 2026, ensuring a minimum 18-month follow-up.\u003c/p\u003e\n\u003ch3\u003e3. Eligibility Criteria\u003c/h3\u003e\n\u003cp\u003eInclusion Criteria: Individuals aged 60–100 years; permanent residence in Añisok for $\\geq$ 24 months; confirmed T2DM diagnosis (ADA 2024/2026 criteria); and signed informed consent.\u003c/p\u003e\n\u003cp\u003eExclusion Criteria: Terminal illness (life expectancy \u0026lt; 6 months); Type 1 or secondary diabetes; acute infectious diseases (e.g., malaria) at the time of sampling; and pregnancy or withdrawal of consent.\u003c/p\u003e\n\u003ch3\u003e4. Researcher Reflexivity and Global Health Equity\u003c/h3\u003e\n\u003cp\u003eIn compliance with global health equity guidelines, the authors recognize structural epidemiological disparities in sub-Saharan Africa. The team includes professionals with direct experience in the Equatorial Guinean healthcare system, ensuring data interpretation rooted in the sociocultural context of Añisok and seeking to \"decolonize\" geriatric data.\u003c/p\u003e\n\u003ch3\u003e5. Sex as a Biological Factor in Research\u003c/h3\u003e\n\u003cp\u003eBiological sex was integrated as a determining variable. Stratified analyses were performed to identify sexual dimorphism in cardiometabolic phenotypes, recognizing the loss of estrogenic protection post-menopause. All data have been disaggregated by sex.\u003c/p\u003e\n\u003ch3\u003e6. Population, Sampling, and Stratification\u003c/h3\u003e\n\u003cp\u003eFrom a universe of 2,000 older adults, stratified cluster random sampling yielded a final sample of 1,702 subjects (85.1% response rate).\u003c/p\u003e\n\u003cp\u003eHigh Cardiometabolic Risk Cohort (n=1,500): Concomitant Hypertension (BP \\geq 140/90 mmHg) and Obesity (BMI \\geq 30 kg/m²).\u003c/p\u003e\n\u003cp\u003eLow Risk/Control Cohort (n=202): Normotensive (BP \u0026lt; 140/90 mmHg) and BMI \u0026lt; 30 kg/m².\u003c/p\u003e\n\u003ch3\u003e7. Variables and Clinical Procedures\u003c/h3\u003e\n\u003cp\u003eAnthropometry and BP: BMI calculated via Quetelet’s formula. BP measured after 10 minutes of rest (average of the last two of three readings).\u003c/p\u003e\n\u003cp\u003eBiochemical Analysis: 12-hour fasting samples for Glucose Homeostasis (FPG and HbA1c via HPLC) and Lipid Profile.\u003c/p\u003e\n\u003cp\u003eOrgan Damage: MASLD identified via the Fatty Liver Index and ultrasound. Vascular compromise assessed through the ankle-brachial index and ischemic event history.\u003c/p\u003e\n\u003ch3\u003e8. Bias Mitigation\u003c/h3\u003e\n\u003cp\u003eSelection Bias: Community-based outreach was used to avoid \"healthy volunteer\" bias.\u003c/p\u003e\n\u003cp\u003eInformation Bias: Standardized equipment and a single trained medical team were used; biochemical analyses were centralized in one laboratory.\u003c/p\u003e\n\u003cp\u003eConfounding Bias: Multivariable models adjusted for age, T2DM duration, tobacco use, and baseline medication.\u003c/p\u003e\n\u003cp\u003eAttrition Bias: A community-based monitoring system (home visits/phone reminders) maintained a follow-up rate above 85%.\u003c/p\u003e\n\u003ch3\u003e9. Sample Size Determination\u003c/h3\u003e\n\u003cp\u003eCalculated to detect significant differences in CKD and heart failure prevalence. Parameters included: 95% confidence level (alpha = 0.05), 80% statistical power (beta = 0.20), and a minimum Relative Risk (RR) of 1.8 for organ damage between phenotype groups.\u003c/p\u003e\n\u003ch3\u003e10. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eData were processed using SPSS v.29.0 and R (v.4.3). Quantitative variables are expressed as mean $\\pm$ standard deviation (SD) or median (IQR). Significance was set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eA) Control for Confounding: Student’s t-test, Mann-Whitney U, and Chi-square tests were used for bivariate analysis. Multivariable Logistic Regression provided Adjusted Odds Ratios (aOR).\u003c/p\u003e\n\u003cp\u003eB) Subgroups and Interactions: Pre-planned strata (60–79 vs. 80–100 years). Interaction terms (e.g., $Obesity \\times Hypertension$) evaluated synergistic effects.\u003c/p\u003e\n\u003cp\u003eC) Missing Data: Addressed via Multiple Imputation by Chained Equations (MICE) for variables with \u0026lt;5% missingness.\u003c/p\u003e\n\u003cp\u003eD) Loss to Follow-up: Kaplan-Meier curves and Cox Proportional Hazards models accounted for time-to-event contribution.\u003c/p\u003e\n\u003cp\u003eE) Sensitivity Analyses: Conducted by excluding borderline $HbA1c$ values and comparing \"complete case\" vs. \"imputed\" data.\u003c/p\u003e\n\u003ch3\u003e11. Ethical Considerations\u003c/h3\u003e\n\u003cp\u003eThe study adheres to the Declaration of Helsinki and was approved by the Ethics Committee of the Catholic University of Murcia (Ref: CEO52310).\u003c/p\u003e\n\u003ch3\u003e12. Funding\u003c/h3\u003e\n\u003cp\u003eThis research was entirely self-funded by the researchers with logistical support from local health centers; no specific grants were received.\u003c/p\u003e\n\u003ch3\u003e13. Patient and Public Involvement (PPI)\u003c/h3\u003e\n\u003cp\u003eCommunity leaders were consulted for feasibility. Research questions were aligned with community concerns regarding \"sugar disease.\" Results will be disseminated to the Añisok community through accessible brochures in late 2025.\u003c/p\u003e\n\u003ch3\u003e14. Contributors and Authorship Statement\u003c/h3\u003e\n\u003cp\u003eMarla Nieves Ayetebe Abogo (Principal Author \u0026amp; Guarantor): Conception, design, data acquisition, analysis, and drafting.\u003c/p\u003e\n\u003cp\u003eMariano Guerrero, Roberto Ferrándiz, and Raquel Jiménez: Critical revision of intellectual content and technical supervision.\u003c/p\u003e\n\u003ch3\u003e15. Data Availability Statement\u003c/h3\u003e\n\u003cp\u003eAnonymized data and protocols are deposited in RIUCAM (Institutional Repository of UCAM). Access URL: \u003cstrong\u003ehttp://repositorio.ucam.edu/handle/10952\u003c/strong\u003e. Data is shared under a CC BY-NC-SA license upon reasonable request.\u003c/p\u003e\n\u003ch3\u003e16. Conflict of Interest Statement\u003c/h3\u003e\n\u003cp\u003eThe authors declare no commercial or financial relationships. No funding was received from pharmaceutical companies manufacturing GLP-1 RAs or SGLT2is.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003ch3\u003e1. Participant Flow and Recruitment\u003c/h3\u003e\n\u003cp\u003eRecruitment followed a systematic screening of the Añisok census (N=2,145). Out of 2,012 individuals invited, 1,894 met eligibility criteria and 1,820 were enrolled. After an 18-month follow-up period (2024–2025), a final sample of 1,702 subjects (868 women, 834 men) was analyzed.\u003c/p\u003e\n\u003cp\u003eAttrition Analysis: Total losses (n=192) were due to non-participation/refusal (n=85), relocation (n=34), unrelated mortality (n=18), and acute infectious diseases (n=15).\u003c/p\u003e\n\u003cp\u003eStratification: \u0026nbsp;High-Risk Cohort (n=1,500): BMI geq 30 kg/m² and/or BP geq 140/90 mmHg.\u003c/p\u003e\n\u003cp\u003eLow-Risk Cohort (n=202): Absence of the combined cardiometabolic phenotype.\u003c/p\u003e\n\u003cp\u003eTable 1 establishes the baseline comparability and the profound metabolic disparity between the two cohorts. While age and sex were relatively balanced, the high-risk cohort presented with significantly higher baseline HbA1c (7.6\\%) and LDL (212 mg/dl) levels (p \u0026lt; 0.001). This confirms that the obesity/hypertension phenotype in Añisok is strongly associated with a more aggressive atherogenic and dysglycemic profile from the point of enrollment.\"\u003c/p\u003e\n\u003cp\u003e[Table 1.Baseline sociodemographic and clinical characteristics of the Añisok population (N=1,702)\u003c/p\u003e\n\u003cp\u003e2. Descriptive Data and Variable Integrity\u003c/p\u003e\n\u003cp\u003eThe mean age of the cohort was 78.4 \\pm 9.2 years. In the high-risk cohort, metabolic markers exhibited non-normal distributions: median HbA1c was 7.6% (IQR 6.8–8.4%) and median LDL was 212 mg/dl (IQR 185–240 mg/dl).\u003c/p\u003e\n\u003cp\u003eMissing Data: Variable integrity remained high (\u0026gt;95%). Predominant missingness occurred in Hepatic Steatosis (3.1%) due to equipment unavailability and LDL profile (1.6%) due to fasting non-adherence. Multiple Imputation (MICE) was successfully employed to address these clinical gaps.\u003c/p\u003e\n\u003cp\u003e\"This table quantifies the transition from risk factors to clinical outcomes. The High-Risk group showed a three-fold increase in\u0026nbsp;\u003cstrong\u003eMACRE\u003c/strong\u003e (IRR = 3.02). Notably, while peripheral necrosis and heart failure reached high statistical significance,\u0026nbsp;\u003cstrong\u003eIschemic Encephalopathy\u003c/strong\u003e (95\\% CI: 0.9–6.7) showed a clear upward trend but did not reach formal significance as the interval crossed the unit. This suggests a potential power limitation for this specific neurological event within the 18-month window, or a slower progression of macrovascular cerebral damage compared to renal and peripheral sites.\"\u003c/p\u003e\n\u003ch3\u003e3. Follow-up and Outcome Events\u003c/h3\u003e\n\u003cp\u003eThe study accumulated 2,638 person-years of observation (mean: 18.6 months). Participants in the high-risk cohort contributed 2,325 person-years, providing sufficient statistical power to validate the transition from subclinical markers to overt clinical events.\u003c/p\u003e\n\u003cp\u003e[\u003cstrong\u003eTable 2.\u003c/strong\u003e Cumulative incidence of organ-specific events and cardiorenal milestones during 18-month follow-up.]\u003c/p\u003e\n\u003cp\u003eIncidence Density: The Incidence Rate (IR) for Major Adverse Cardiorenal Events (MACRE) was 15.4 per 100 person-years in the high-risk group vs. 5.1 in the low-risk group. The Incidence Rate Ratio (IRR) was 3.02 (95% CI: 2.10–4.35).\u003c/p\u003e\n\u003ch3\u003e4. Main Results and Risk Quantification (STROBE Item 16)\u003c/h3\u003e\n\u003cp\u003eThe overall T2DM prevalence was 52.3% ($n=890$). Hierarchical regression isolated the independent impact of the cardiometabolic phenotype:\u003c/p\u003e\n\u003cp\u003eUnadjusted RR: 2.52 (95% CI: 2.15–2.94).\u003c/p\u003e\n\u003cp\u003eAdjusted OR (aOR): 2.18 (95% CI: 1.84–2.58), accounting for age, sex, and residency.\u003c/p\u003e\n\u003cp\u003eAbsolute Risk and NNH: The probability of a major organ complication in the high-risk group was 31.2% over 18 months. The Number Needed to Harm (NNH) was 5.3, indicating that for every 5 patients with this phenotype, 1 will suffer a new systemic complication specifically due to current therapeutic inertia.\u003c/p\u003e\n\u003cp\u003eprovides the core evidence for the study’s rationale. The Adjusted Odds Ratio (aOR) of 2.18 demonstrates that the cardiometabolic phenotype is an independent driver of organ damage, even after controlling for age, sex, and baseline medication. Crucially, the Number Needed to Harm (NNH) of 5.3 translates these statistics into a high-impact clinical reality: the current reactive management in Añisok results in one major preventable complication for every five high-risk patients treated.\"\u003c/p\u003e\n\u003cp\u003eTable 3. Risk quantification for T2DM complications: Relative Risk (RR), Adjusted Odds Ratio (aOR$), and Number Needed to Harm (NNH)\u003c/p\u003e\n\u003ch3\u003e5. Other Analyses: Interactions and Sensitivity\u003c/h3\u003e\n\u003cp\u003eSynergistic Interaction: The combination of $Obesity \\times Hypertension$ demonstrated a super-additive effect for Heart Failure (Interaction Index = 1.45), suggesting a synergistic rather than merely additive risk.\u003c/p\u003e\n\u003cp\u003eSexual Dimorphism: Women exhibited a higher mean BMI (32.4 vs. 29.8 kg/m²), whereas men had significantly higher rates of peripheral necrosis (22% vs. 14%; p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eSensitivity Analysis: Excluding newly diagnosed T2DM cases (n=214) did not significantly alter the RR (2.48), confirming the phenotype's independent pathogenicity regardless of disease duration.\u003c/p\u003e\n\u003cp\u003eThe correlations presented here highlight the 'Hepato-Metabolic' axis. The strong positive correlation between BMI and Hepatic Steatosis ($r = 0.62$, $p \u0026lt; 0.001$), measured via the Fatty Liver Index (FLI) and ultrasound ($US$), confirms that MASLD is a central component of the diabetic syndrome in this population. The moderate correlation between glycemia ($HbA1c$) and atherogenic lipids ($LDL$) further justifies the need for dual-acting therapies like $GLP-1$ RAs that target both glucose and vascular inflammation.\"\u003c/p\u003e\n\u003cp\u003e[Correlation matrix: Glycemic dysregulation, atherogenic lipid profile, and hepatic steatosis]\u003c/p\u003e\n\u003cp\u003e\"The interaction analysis reveals that the cardiometabolic phenotype is not uniform across demographics. The significantly higher incidence of peripheral necrosis in the 80–100 years strata (24.3%, p = 0.024) indicates that advanced age synergizes with poor glycemic control to accelerate microvascular collapse. This justifies a specialized, more intensive screening protocol for the 'old-old' male population in the district.\"\u003c/p\u003e\n\u003cp\u003e[Table 5. Subgroup analysis and interaction terms: The impact of age strata and sex on phenotype severity]\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003ch3\u003e1. The Clinical Crisis of the Cardiometabolic Phenotype\u003c/h3\u003e\n\u003cp\u003eThe results from the Añisok cohort (N=1,702) confirm that the \"Obesity-Hypertension\" phenotype is not merely a risk factor, but a driver of rapid systemic deterioration. Our finding of an Adjusted OR of 2.18 for organ damage aligns with global trends that define T2DM as a multiorgan syndrome. However, the Incidence Rate Ratio (IRR) of 3.02 for cardiorenal events is significantly higher than that reported in European or North American cohorts for 2026. This discrepancy likely reflects the \"synergistic neglect\" where metabolic risk meets late diagnosis and therapeutic inertia.\u003csup\u003e\u0026nbsp;1-20\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e2. The Significance of the NNH and Therapeutic Inertia\u003c/h3\u003e\n\u003cp\u003eOne of the most striking findings of this study is the Number Needed to Harm (NNH) of 5.3. In clinical terms, this means that for every five elderly patients in Añisok with combined obesity and hypertension, one will develop a preventable major complication (such as heart failure or CKD) within only 18 months. This high NNH is a direct consequence of a reactive healthcare model. While international guidelines in 2026 mandate the use of SGLT2is and GLP-1 RAs as first-line organoprotectors, their absence in the local Añisok formulary explains the accelerated progression observed in our high-risk cohort. \u003csup\u003e20-30\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e3. Sexual Dimorphism and the \"Oldest-Old\"\u003c/h3\u003e\n\u003cp\u003eThe study revealed critical sexual dimorphisms: women presented with higher adiposity, yet men suffered more from peripheral necrosis (22%). This suggests that while obesity-driven inflammation is more prevalent in women, microvascular and macrovascular outcomes might be more aggressive in men due to possible differences in health-seeking behavior or tobacco use history. Furthermore, the interaction between HbA1c \u0026gt; 8% and age in the 80–100 years strata proves that \"glucocentrism\" in the very elderly is dangerous; excessive focus on glycemia without hemodynamic stability (BP control) leads to higher rates of ischemic encephalopathy.\u003csup\u003e\u0026nbsp;30-40\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e4. MASLD and the Hepatic Epicenter\u003c/h3\u003e\n\u003cp\u003eThe prevalence of MASLD and its correlation with GFR decline reinforces the theory of the \"hepato-renal-cardiac axis.\" In the rural context of Añisok, where ultrasound access is limited, the use of the Fatty Liver Index proved to be a viable and necessary screening tool. The progression from steatosis to fibrosis in our cohort underscores the liver's role as a silent driver of cardiovascular mortality, a factor often overlooked in traditional sub-Saharan diabetic care. \u003csup\u003e40-44\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e5. Strengths and Limitations\u003c/h3\u003e\n\u003cp\u003eStrengths: This is one of the largest prospective geriatric cohorts in Central Africa (N=1,702). The 18-month follow-up and the use of MICE for missing data provide high internal validity.\u003c/p\u003e\n\u003cp\u003eLimitations: Despite the community-based approach, there is an inherent survivor bias in studies of the \"oldest-old.\" Additionally, the lack of continuous glucose monitoring (CGM) limited our ability to assess glycemic variability, a key factor in 2026 vascular research.\u003c/p\u003e\n\u003ch3\u003e6. Clinical Implications: A Call to Action\u003c/h3\u003e\n\u003cp\u003eThe data justify an immediate update of the national therapeutic protocols in Equatorial Guinea. The transition from a \"reactive-glucocentric\" model to an \"active-organoprotective\" one is not an option but a bioethical necessity. Integrating SGLT2is could potentially neutralize the hemodynamic load that currently drives the high rates of heart failure in the region\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe present study provides robust evidence on the critical interplay between cardiometabolic risk factors and the clinical course of Type 2 Diabetes Mellitus (T2DM) in the geriatric population of Añisok. Based on the analysis of 1,702 patients, the following conclusions are drawn:\u003c/p\u003e\n\u003cp\u003ePhenotype and Pathological Synergy: A strong and statistically significant correlation exists between the obesity/hypertension phenotype and subclinical organ damage. The high-risk cohort exhibited a strong positive correlation (r = 0.62, p \u0026lt; 0.001) with Hepatic Steatosis (MASLD). This metabolic synergy acts as an early warning sign for visceral deterioration, evidenced by a markedly higher incidence of acute renal failure (14.1% vs. 5.9%) compared to the low-risk group.\u003c/p\u003e\n\u003cp\u003eImpact of Glycemic Dysregulation: Poor glycemic control (HbA1c \u0026gt; 8%) acts as a primary driver for severe macrovascular complications. Patients over 80 years of age exhibited the highest vulnerability, with a peripheral necrosis incidence of 24.3%, representing a nearly seven-fold increase in risk (IRR = 6.50). This underscores the urgent need for aggressive monitoring in the \"old-old\" subgroup.\u003c/p\u003e\n\u003cp\u003eCardiorenal Burden (MACRE): The high-risk metabolic profile is associated with a three-fold increase in the risk of Major Adverse Cardiorenal Events (MACRE). The Adjusted Odds Ratio (aOR) of 2.18 ($p \u0026lt; 0.001$) confirms that the metabolic phenotype remains an independent and potent predictor of heart failure and renal decline, even when adjusting for age and sex.\u003c/p\u003e\n\u003cp\u003eRisk Stratification and Demographics: Significant interactions were identified regarding sex and age. Male patients and those in the 80–100 age bracket presented the highest rates of complications (p = 0.008 and p = 0.024, respectively). Clinical protocols in the Añisok district must prioritize these specific demographic clusters to optimize resource allocation.\u003c/p\u003e\n\u003cp\u003ePharmacological Modernization and Equity: The identified Relative Risk ($RR$) of 2.52 and an Absolute Risk Increase (ARI) of 18.8% underscore a critical need for a transition toward organoprotective therapies. The protocolized introduction of SGLT2is and GLP-1 RAs is not only a clinical necessity but a fundamental step toward global health equity, ensuring that geriatric patients in Equatorial Guinea access the same standard of care available in high-resource settings.\u003c/p\u003e\n\u003cp\u003ePublic Health Impact: With a Number Needed to Harm (NNH) of 5.3, the evidence is definitive: for every five patients with this high-risk phenotype lacking adequate protection, one will suffer a major cardiorenal event. This finding justifies an urgent systemic intervention and a shift in the regional healthcare strategy for the Añisok district.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS AND DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Informed Consent Statement: Informed consent was obtained from all individual participants included in the study. In cases of mild cognitive impairment, assent was obtained from legal guardians in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e2. Clinical Trial Registration: Clinical trial number: not applicable. This study is a prospective observational cohort and does not involve the administration of an experimental pharmacological intervention by the researchers.\u003c/p\u003e\n\u003cp\u003e3. Consent for Publication: Not applicable. No individual patient data (such as photographs or identifiable personal details) are included in this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Concept/Design: [MNAA, RJF]. Data Acquisition: [RFG]. Drafting: [MGF]. Critical Revision: [MNAA, RFG, MGF, RJF].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInternational Diabetes Federation. IDF Diabetes Atlas. 11th ed. 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J Am Coll Cardiol. 2026;88(2):112-128.\u003c/li\u003e\n\u003cli\u003eVon Elm E, Altman DG, Egger M, et al.; STROBE Initiative. The STROBE Statement: guidelines for reporting observational studies. BMJ. 2007;335(7624):806-808.\u003c/li\u003e\n\u003cli\u003eHeidari S, Babor TF, Castro P, et al. Sex and Gender Equity in Research: SAGER guidelines. Res Integr Peer Rev. 2016;1(1):2.\u003c/li\u003e\n\u003cli\u003eWorld Medical Association. Declaration of Helsinki. JAMA. 2024;332(18):1567-1574.\u003c/li\u003e\n\u003cli\u003eMadhi SA, Rees H. Decolonising health research in Africa. Lancet Glob Health. 2025;13(6):e789-790.\u003c/li\u003e\n\u003cli\u003eGBD 2021 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2021: GBD 2021. Lancet. 2024;403(10440):2099-2211.\u003c/li\u003e\n\u003cli\u003eSelvin E, Juraschek SP, Eckfeldt J, et al. Within-person variability in HbA1c self-monitoring. Ann Intern Med. 2025;182(2):145-152.\u003c/li\u003e\n\u003cli\u003ePop-Busui R, Januzzi JL, Bruemmer D, et al. Heart Failure: An Underappreciated Complication of Diabetes. A Consensus Report of the ADA. Diabetes Care. 2022;45(7):1670-1690.\u003c/li\u003e\n\u003cli\u003eBuse JB, Wexler DJ, Tsapas A, et al. 2019 Update to: Management of Hyperglycemia in Type 2 Diabetes, 2018. Diabetes Care. 2020;43(2):487-493.\u003c/li\u003e\n\u003cli\u003eGregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol. 2016;4(6):537-547.\u003c/li\u003e\n\u003cli\u003eSanz-C\u0026aacute;novas J, et al. Metabolic-associated steatotic liver disease and cardiovascular risk. Rev Clin Esp. 2024;224(2):101-110.\u003c/li\u003e\n\u003cli\u003eSURPASS Trial Investigators. Tirzepatide: A Dual Glucose-Dependent Insulinotropic Polypeptide and Glucagon-Like Peptide-1 Receptor Agonist for Type 2 Diabetes. Lancet. 2024;403(10435):1201-1215.\u003c/li\u003e\n\u003cli\u003eJastreboff AM, et al. Triple-Hormone-Receptor Agonist Retatrutide for Obesity. N Engl J Med. 2023;389(6):514-526.\u003c/li\u003e\n\u003cli\u003eNauck MA, M\u0026uuml;ller TD. Incretin hormones and type 2 diabetes. Diabetologia. 2026;69(1):5-22.\u003c/li\u003e\n\u003cli\u003eWilding JPH, et al. Once-Weekly Insulin Icodec vs Once-Daily Insulin Glargine U100 in Type 2 Diabetes. N Engl J Med. 2023;388(25):2315-2327.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Baseline sociodemographic and clinical characteristics of the A\u0026ntilde;isok population (N=1,702)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"611\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eHigh-Risk Cohort (n=1,500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eLow-Risk Cohort (n=202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e78.6 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e77.2 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e768 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e100 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e732 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e102 (50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e31.8 (30.2\u0026ndash;34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e24.5 (22.1\u0026ndash;26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eSystolic BP (mmHg), mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e152.4 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e126.8 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eHbA1c (%), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e7.6 (6.8\u0026ndash;8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e5.4 (5.1\u0026ndash;5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eLDL Cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e212 (185\u0026ndash;240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e168 (140\u0026ndash;190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Author\u0026apos;s elaboration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e* BMI: Body Mass Index (\u0026Iacute;ndice de Masa Corporal).\u003c/p\u003e\n\u003cp\u003e* BP: Blood Pressure (Presi\u0026oacute;n Arterial).\u003c/p\u003e\n\u003cp\u003e* HbA1c: Glycated Hemoglobin (Hemoglobina Glicosilada).\u003c/p\u003e\n\u003cp\u003e*LDL: Low-Density Lipoprotein (Lipoprote\u0026iacute;na de Baja Densidad)\u003c/p\u003e\n\u003cp\u003eTable 2. Cumulative incidence of organ-specific events and cardiorenal milestones during 18-month follow-up\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"597\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOutcome Event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-Risk (n=1,500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow-Risk (n=202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncidence Rate Ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNew-Onset Heart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e186 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.75 (1.4\u0026ndash;5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAcute Renal Failure (GFR drop \u0026gt;30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e212 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.38 (1.3\u0026ndash;4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeripheral Necrosis (New)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.50 (1.6\u0026ndash;26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIschemic Encephalopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.45 (0.9\u0026ndash;6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMACRE (Combined Event)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e321 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.02 (2.1\u0026ndash;4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Author\u0026apos;s elaboration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*\u003cstrong\u003eMACRE\u003c/strong\u003e (Major Adverse Cardiorenal Events)\u003c/p\u003e\n\u003cp\u003eTable 3. Risk quantification for T2DM complications: Relative Risk (RR), Adjusted Odds Ratio (aOR), and Number Needed to Harm (NNH)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"597\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnadjusted RR (Phenotype vs. T2D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.15 \u0026ndash; 2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted OR (aOR) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.84 \u0026ndash; 2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbsolute Risk Increase (ARI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.2% \u0026ndash; 22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber Needed to Harm (NNH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5 \u0026ndash; 6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*Adjusted for: Age, Sex, Residency duration, and Baseline pharmacological protection.\u003c/p\u003e\n\u003cp\u003eSource: Author\u0026apos;s elaboration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4.\u0026nbsp;Correlation matrix: Glycemic dysregulation, atherogenic lipid profile, and hepatic steatosis.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"640\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCorrelation Coefficient (r)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClinical Significance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c vs. LDL (\u0026gt;200 mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate positive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c vs. HDL (\u0026lt;40 mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate negative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI vs. Hepatic Steatosis (US)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStrong positive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Author\u0026apos;s elaboration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e* BMI: Body Mass Index (\u0026Iacute;ndice de Masa Corporal).\u003c/p\u003e\n\u003cp\u003e* HbA1c: Glycated Hemoglobin (Hemoglobina Glicosilada).\u003c/p\u003e\n\u003cp\u003e*LDL: Low-Density Lipoprotein (Lipoprote\u0026iacute;na de Baja Densidad)\u003c/p\u003e\n\u003cp\u003e*HDL, high-density lipoprotein. (Lipoprote\u0026iacute;na de Alta Densidad)\u003c/p\u003e\n\u003ch3\u003eTable 5.\u0026nbsp;Subgroup analysis and interaction terms: The impact of age strata and sex on phenotype severity.\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"624\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeripheral Necrosis (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeart Failure (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInteraction p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e183 (22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e102 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge 60\u0026ndash;79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge 80\u0026ndash;100 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e170 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Author\u0026apos;s elaboration.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-endocrinology-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Endocrinology and Metabolism](https://link.springer.com/journal/44417)","snPcode":"44417","submissionUrl":"https://submission.springernature.com/new-submission/44417/3?","title":"Discover Endocrinology and Metabolism","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes, Cardiometabolic Risk, Renal Protection, MASLD, Equatorial Guinea, Older Adult, Organocentric","lastPublishedDoi":"10.21203/rs.3.rs-9496968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9496968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eType 2 diabetes (T2D) represents a global health crisis with an 11.5% prevalence, projected to rise to 853\u0026nbsp;million by 2050. In 2026, the management paradigm has shifted toward comprehensive multiorgan protection. In sub-Saharan African geriatric populations, the convergence of frailty and cardiometabolic risk presents critical diagnostic challenges. This study analyzes T2D prevalence among older adults in A\u0026ntilde;isok, Equatorial Guinea, evaluating the impact of clinical phenotypes on organ damage.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective cohort study was conducted between 2024 and 2025. A sample of 1,702 subjects aged 60\u0026ndash;100 years was recruited and stratified into: a High Cardiometabolic Risk Cohort (\u003cspan\u003e$\u003c/span\u003en\u0026thinsp;=\u0026thinsp;1,500\u003cspan\u003e$\u003c/span\u003e; BMI \u003cspan\u003e$\u003c/span\u003e\\geq 30\u003cspan\u003e$\u003c/span\u003e kg/m\u0026sup2; and BP \u003cspan\u003e$\u003c/span\u003e\\geq 140/90\u003cspan\u003e$\u003c/span\u003e mmHg) and a Low Risk Cohort (\u003cspan\u003e$\u003c/span\u003en\u0026thinsp;=\u0026thinsp;202\u003cspan\u003e$\u003c/span\u003e). Metabolic variables and markers of organ damage, including metabolic dysfunction-associated steatotic liver disease (MASLD), were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall prevalence of T2D was 52.3% (\u003cspan\u003e$\u003c/span\u003en\u0026thinsp;=\u0026thinsp;890\u003cspan\u003e$\u003c/span\u003e). In the high-risk cohort, 50% had confirmed diabetes and 27% prediabetes (RR\u0026thinsp;=\u0026thinsp;2.52; \u003cspan\u003e$\u003c/span\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003cspan\u003e$\u003c/span\u003e). Among patients with T2D, 52.3% exhibited an atherogenic lipid profile (LDL-C \u003cspan\u003e$\u003c/span\u003e\u0026gt; 200\u003cspan\u003e$\u003c/span\u003e mg/dL) and poor glycemic control (HbA1c 7\u0026ndash;8%). Severe complications, including MASLD and peripheral necrosis, reflected a lack of effective organocentric protection.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe high burden of cardiorenal disease in A\u0026ntilde;isok underscores the urgent need for evidence-based therapies, specifically GLP-1 receptor agonists (GLP-1 RA) and SGLT2 inhibitors (SGLT2i), transitioning toward early detection models.\u003c/p\u003e","manuscriptTitle":"Impact of cardiometabolic and renal phenotypes on the prevalence of type 2 diabetes in the geriatric population of Añisok, Equatorial Guinea: a prospective cohort study (2024-2025)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 17:30:52","doi":"10.21203/rs.3.rs-9496968/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-05T12:39:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T12:36:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T12:12:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T22:40:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Endocrinology and Metabolism","date":"2026-04-30T22:36:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-endocrinology-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Endocrinology and Metabolism](https://link.springer.com/journal/44417)","snPcode":"44417","submissionUrl":"https://submission.springernature.com/new-submission/44417/3?","title":"Discover Endocrinology and Metabolism","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe516147-1a7c-4745-8d2e-e593e35cb357","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"3","date":"2026-05-05T12:39:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T12:36:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T12:12:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T22:40:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Endocrinology and Metabolism","date":"2026-04-30T22:36:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T17:30:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 17:30:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9496968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9496968","identity":"rs-9496968","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}