Investigating lipid and glucose profiles in type 2 diabetes mellitus: Evidence from a cross-sectional study in North Sulawesi, Indonesia

 Corresponding author: Einelin J.F. Langitan Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Investigating lipid and glucose profiles in type 2 diabetes mellitus: Evidence from a cross-sectional study in North Sulawesi, Indonesia Einelin J.F. Langitan 1, *, Murniati Tiho 2 and Diana S. Purwanto 3 1 Medical Education Study Program, Faculty of Medicine, Sam Ratulangi University, Manado, Indonesia. 2 Biochemistry Section, Faculty of Medicine, Sam Ratulangi University, Manado, Indonesia. World Journal of Biology Pharmacy and Health Sciences, 2025, 21(02), 082–089 Publication history: Received on 16 December 2024; revised on 27 January 2025; accepted on 30 January 2025 Article DOI: https://doi.org/10.30574/wjbphs.2025.21.2.0100 Abstract Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder often accompanied by dyslipidemia, a condition that exacerbates cardiovascular risks. Despite extensive research, the relationship between total cholesterol levels and fasting blood sugar levels remains unclear, particularly in underrepresented populations. This study investigates the correlation between total cholesterol and fasting blood sugar levels in T2DM patients treated at ODSK Hospital, North Sulawesi, Indonesia. A cross-sectional observational study was conducted using secondary data from the medical records of 65 T2DM patients between April 2022 and July 2024. Total cholesterol and fasting blood sugar levels were classified according to established clinical guidelines. The Spearman-Rank correlation test assessed the relationship between the variables. Among the patients, 52.3% were female, and 33.8% were aged 65–74 years. Approximately 47.7% had normal cholesterol levels, while 81.5% exhibited fasting blood sugar levels ≥126 mg/dL. Statistical analysis revealed no significant correlation between total cholesterol and fasting blood sugar levels (r = -0.129, ρ = 0.307). The findings suggest independent regulation of lipid and glucose metabolism in T2DM patients. These results emphasize the need for individualized management strategies targeting both dyslipidemia and hyperglycemia as separate clinical priorities. Future research should explore larger populations and additional metabolic markers to further elucidate these complex interactions. Keywords: Type 2 diabetes mellitus (T2DM); Total cholesterol; Dasting blood sugar; Diabetes Management 1. Introduction Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder characterized by persistent hyperglycaemia due to impaired insulin action, secretion, or both [1–3]. Beyond its defining dysregulation of glucose metabolism, T2DM frequently coexists with dyslipidaemia, a condition marked by abnormal lipid levels, including elevated total cholesterol [4, 5]. Dyslipidaemia and hyperglycaemia are significant risk factors for cardiovascular disease (CVD), the leading cause of morbidity and mortality among T2DM patients [6, 7]. Thus, understanding the interplay between total cholesterol and fasting blood sugar levels holds critical implications for improving patient outcomes. Extensive research has examined the interconnection between lipid and glucose metabolism, often identifying insulin resistance as a common foundational mechanism [8–10]. Insulin resistance can impair lipid metabolism, leading to elevated levels of low-density lipoprotein cholesterol (LDL-C) and total cholesterol[11]. Conversely, poorly managed lipid profiles may exacerbate insulin resistance, compounding glycemic dysregulation [1, 12]. While some studies have reported a positive correlation between total cholesterol and fasting blood sugar levels in T2DM [13–15], others have found no significant association [16–19], highlighting a gap in the literature and the need for further investigation in diverse populations and clinical settings. World Journal of Biology Pharmacy and Health Sciences, 2025, 21(02), 082–089 83 This study explores the relationship between total cholesterol and fasting blood sugar levels in T2DM patients treated at ODSK Hospital, North Sulawesi Province, Indonesia. By focusing on a specific population, this research seeks to contribute nuanced insights to the broader discourse on lipid-glucose interplay in diabetes management. The study leverages retrospective data and employs statistical analyses to clarify whether these parameters are interrelated or operate independently in this context. The findings aim to address existing gaps in the literature and inform targeted interventions to mitigate the dual burden of dyslipidaemia and hyperglycemia. 1.1. Research Objectives and Significance The primary objective of this study is to assess the correlation between total cholesterol levels and fasting blood sugar levels in T2DM patients. This research is significant because it examines a clinically relevant issue that could enhance the understanding of metabolic regulation in diabetes. Identifying potential associations, or lack thereof, provides a foundation for improving personalized treatment approaches and public health strategies aimed at reducing cardiovascular risk in T2DM populations. 2. Material And Method This study employed a quantitative observational analytic research design with a cross-sectional approach to investigate the relationship between total cholesterol levels and fasting blood sugar levels in patients with Type 2 Diabetes Mellitus (T2DM). Data were collected retrospectively using secondary sources, specifically medical records of outpatients and inpatients diagnosed with T2DM at ODSK Hospital, North Sulawesi Province. The study period spanned from April 2022 to July 2024. 2.1. Population and Sampling The study population consisted of T2DM patients aged 19 years or older who had undertaken laboratory tests for total cholesterol and fasting blood sugar levels. The total sampling technique was used, resulting in 65 patients who met the inclusion criteria. Inclusion criteria required participants to have a confirmed diagnosis of T2DM, aged ≥19 years, and have recorded laboratory results for the two primary variables of interest. Exclusion criteria were not specified, which may limit the ability to control for confounding factors such as comorbidities or medication use. 2.2. Data Collection The medical records reviewed included demographic data (e.g., age and gender), total cholesterol levels, and fasting blood sugar levels. Total cholesterol levels were categorized based on guidelines from the National Cholesterol Education Program (NCEP ATP III) and the Indonesian Endocrinology Association (PERKENI) [20, 21]. Fasting blood sugar levels were classified according to the 2021 PERKENI criteria [22]. 2.3. Data Analysis The Kolmogorov-Smirnov test was initially employed to assess the normality of the data, as it is a well-established method for determining whether a dataset follows a normal distribution [23]. This test was particularly relevant given that clinical variables in this study, such as fasting blood sugar and total cholesterol levels, frequently exhibit skewed distributions, often clustering in abnormal ranges—a pattern commonly observed in T2DM populations [24]. Moreover, factors such as the small sample size (n = 65) and population heterogeneity (e.g., differences in disease severity, medication use, and lifestyle factors) further increased the likelihood of deviations from normality, underscoring the necessity of this evaluation [25]. Upon confirming a non-normal distribution through the Kolmogorov-Smirnov test, the Spearman rank correlation test was applied. This non-parametric test was selected due to its robustness in analyzing non-normally distributed data and its appropriateness for ordinal variables [26]. Schober & Schwarte [2018] emphasized that this test measures associations without assuming linearity or homogeneity of variances. Therefore, it was well-suited for analyzing the relationship between total cholesterol and fasting blood sugar levels in this study. All statistical analyses were performed using SPSS software version 29. 2.4. Ethical Approval This study was conducted following ethical principles of WHO 2011 standards and was approved by the Ethics Committee of Manado Health Polytechnic Ministry of Health, with approval reference number KEPK.01/12/502/2024. Due to the retrospective nature of the research, which involved the use of secondary data from medical records of patients already diagnosed with type 2 diabetes mellitus at ODSK Hospital, the need for informed consent was waived World Journal of Biology Pharmacy and Health Sciences, 2025, 21(02), 082–089 84 by the ethics committee. All patient data were anonymized and handled confidentially to ensure privacy and compliance with ethical standards. 3. Results And Discussions 3.1. Sample Characteristics The study analyzed 65 patients diagnosed with Type 2 Diabetes Mellitus (T2DM) at ODSK Hospital, North Sulawesi Province. The gender distribution revealed a slightly higher proportion of female patients (52.3%, n = 34) compared to male patients (47.7%, n = 31) (Table 1). Regarding age, the majority of patients were in the 65–74 years age group (33.8%, n = 22), with the smallest representation in the 19–24 years age group (1.5%, n = 1) (Table 2). This distribution aligns with the increasing prevalence of T2DM in older populations, as indicated by previous studies [4, 28]. Table 1 Sample Distribution by Gender Gender Frequency (n) Percentage (%) Male 31 47.7 Female 34 52.3 Table 2 Sample Distribution by Age Age Group (years) Frequency (n) Percentage (%) 19–24 1 1.5 25–34 0 0 35–44 6 9.2 45–54 13 20.0 55–64 17 26.2 65–74 22 33.8 ≥ 75 6 9.2 3.2. Total Cholesterol Levels The classification of total cholesterol levels, based on the National Cholesterol Education Program (NCEP ATP III) guidelines, showed that 47.7% of patients (n = 31) had normal cholesterol levels, 29.2% (n = 19) had borderline levels, and 23.1% (n = 15) had high cholesterol levels (Table 3). These results indicate that nearly half of the sampled T2DM patients did not exhibit elevated total cholesterol levels, a finding consistent with previous studies linking lipid imbalances to variable risk factors. Table 3 Distribution of Samples Based on Total Cholesterol Levels Cholesterol Category Frequency (n) Percentage (%) Normal 31 47.7 Borderline 19 29.2 High 15 23.1 3.3. Fasting Blood Sugar Levels Fasting blood sugar levels were categorized using the PERKENI 2021 criteria. The majority of patients (81.5%, n = 53) exhibited fasting blood sugar levels ≥126 mg/dL, indicating uncontrolled blood glucose levels. Only 18.5% (n = 12) of the patients had fasting blood sugar levels within the normal range (<126 mg/dL) (Table 4). This result highlights the prevalence of poorly managed glycemic control among T2DM patients in this population. World Journal of Biology Pharmacy and Health Sciences, 2025, 21(02), 082–089 85 Table 4 Distribution of Samples Based on Fasting Blood Sugar Levels Fasting Blood Sugar (mg/dL) Frequency (n) Percentage (%) <126 12 18.5 ≥126 53 81.5 3.4. Correlation Analysis The Kolmogorov-Smirnov normality test revealed that the data were not normally distributed (Table 5). Consequently, the Spearman-Rank correlation test was applied to assess the relationship between total cholesterol levels and fasting blood sugar levels. The analysis yielded a correlation coefficient (r) of -0.129 with a significance value (ρ) of 0.307 (ρ > 0.05) (Table 5). This indicates that there is no statistically significant relationship between total cholesterol levels and fasting blood sugar levels in the study population. Table 5 Kolmogorov-Smirnov Normality Test Results Parameter Value Sample Size (N) 65 Mean 0.0000000 Standard Deviation 91.72625587 Most Extreme Differences - Absolute 0.156 - Positive 0.156 - Negative -0.103 Test Statistic (D) 0.156 Asymptotic Significance (2-tailed) <0.001 Monte Carlo Significance (2-tailed) <0.001 99% Confidence Interval (Monte Carlo) 0.000 - 0.001 *With Lilliefors Significance Correction †Based on 10,000 Monte Carlo samples with starting seed 2000000 Note: Results indicate significant deviation from normality (p < .001) Table 6 Correlation Analysis Between Total Cholesterol and Fasting Blood Sugar Variable n Correlation Coefficient (r) Significance (ρ) Total Cholesterol and Fasting Blood Sugar 65 -0.129 0.307 The weak negative correlation coefficient (-0.129) suggests that total cholesterol levels and fasting blood sugar levels are minimally related in this sample. This may indicate that these variables operate through independent physiological mechanisms or are influenced by unmeasured confounding factors, such as dietary habits, medication use, or genetic predispositions. The absence of a significant relationship aligns with some prior studies but contrasts with others that have reported positive correlations, warranting further investigation. 3.5. Overview of Key Findings This study investigated the relationship between total cholesterol levels and fasting blood sugar levels in T2DM patients at ODSK Hospital, North Sulawesi Province. Female patients were slightly more represented in the sample, and the majority of patients were aged 65–74 years, reflecting the higher prevalence of T2DM among older adults and women in similar populations. Nearly half of the patients maintained normal cholesterol levels, yet a significant proportion exhibited uncontrolled fasting blood sugar levels, highlighting the challenge of managing hyperglycemia in this population. Importantly, no significant correlation was found between total cholesterol levels and fasting blood sugar World Journal of Biology Pharmacy and Health Sciences, 2025, 21(02), 082–089 86 levels, suggesting that lipid and glucose metabolism may operate independently in this sample. These findings provide valuable insights into the demographic and metabolic profiles of T2DM patients and underscore the need for individualized clinical management strategies. Additionally, they highlight opportunities for public health interventions targeting the dual burden of hyperglycemia and dyslipidemia through routine screening and tailored lifestyle programs. 3.6. Comparison with Existing Literature The absence of a significant relationship between total cholesterol and fasting blood sugar levels aligns with some studies that have also found no association between these parameters in T2DM populations [17–19]. For instance, Saptaningtyas et al. [2022] reported similar findings, indicating that lipid and glucose metabolism may not be directly linked. In contrast, other studies have observed positive correlations, particularly in populations with a higher prevalence of diabetic dyslipidemia [13–15]. This variation indicates that differences in population characteristics, research methodologies, or sample sizes may influence outcomes. By focusing on an underrepresented population in North Sulawesi, this study contributes valuable regional data, highlighting the role of population-specific factors such as genetic predispositions, dietary patterns, and healthcare access in shaping metabolic relationships. 3.7. Mechanistic Interpretation The observed lack of correlation between total cholesterol and fasting blood sugar levels may reflect the independent regulation of these parameters. Insulin resistance, a common feature of T2DM, affects both glucose and lipid metabolism; however, additional factors such as diet, physical activity, medication use, or genetic variability may mediate their interactions. Differences in individual responses to lifestyle interventions or pharmacological treatments may also contribute to this independence, underscoring the complexity of metabolic regulation in T2DM. 4. Practical Implications and Recommendations for Clinical Practice The findings highlight the importance of individualized management for T2DM patients. Clinicians should monitor lipid and glucose parameters independently and adopt dual-targeted interventions to address specific metabolic imbalances. Lipid management may involve statins or dietary changes targeting cholesterol subfractions, while glycemic control could focus on insulin therapy or glucose-lowering medications tailored to patient needs. Routine and detailed monitoring of both lipid and glucose profiles should become standard practice to mitigate long-term cardiovascular risks. Furthermore, the results dispel assumptions that improvements in one parameter will naturally influence the other, emphasizing the need to treat dyslipidemia and hyperglycemia as separate clinical priorities. Such individualized care ensures comprehensive management of the dual burden of T2DM, ultimately improving patient outcomes. 4.1. Public Health Implications Public health initiatives can play a crucial role in complementing clinical efforts to address dyslipidemia and hyperglycemia. Early detection programs, including routine screening for fasting blood sugar and lipid profiles, enable timely interventions. Lifestyle modification programs promoting balanced diets, regular physical activity, and smoking cessation can help address modifiable risk factors. Culturally tailored interventions and digital health tools, such as mobile applications and wearable devices, can enhance patient engagement and adherence. Educating patients about the independent management of dyslipidemia and hyperglycemia empowers them to take an active role in preventing complications and improving their quality of life. These strategies collectively support the broader goal of reducing the burden of T2DM and associated complications. 4.2. Study Limitations and Future Directions This study has several limitations. The reliance on secondary medical records may introduce inaccuracies or biases due to incomplete or inconsistent data. The relatively small sample size (n = 65) limits the generalizability of the findings to other populations. Additionally, the absence of data on confounding variables, such as dietary habits, medication use, or comorbidities, restricts the ability to explore factors influencing the relationship between total cholesterol and fasting blood sugar levels. Future studies should address these limitations by incorporating larger, more diverse populations and collecting primary data with detailed information on lifestyle and treatment variables. Longitudinal studies could also provide a more comprehensive understanding of the dynamic interactions between lipid and glucose metabolism over time. Further research into advanced diagnostic markers, such as LDL-C or glycemic variability, could offer deeper insights into the complex regulation of these parameters in T2DM patients. World Journal of Biology Pharmacy and Health Sciences, 2025, 21(02), 082–089 87 5. Conclusion This study highlights the independent regulation of total cholesterol and fasting blood sugar levels in T2DM patients, as evidenced by the absence of a significant correlation between these parameters. This finding underscores the importance of individualized management in diabetes care, where dyslipidemia and hyperglycemia should be addressed as separate clinical priorities. Routine and detailed monitoring of both parameters is essential to mitigate the risk of long-term complications, particularly cardiovascular disease. Additionally, the study emphasizes the role of public health initiatives in reducing the dual burden of hyperglycemia and dyslipidemia through early detection and lifestyle modification. Programs that promote routine screening, balanced diets, physical activity, and culturally tailored interventions can enhance patient engagement and improve outcomes. By focusing on an underrepresented population in North Sulawesi, this study adds valuable insights to the global body of diabetes research, highlighting the need to consider regional and population-specific factors in T2DM management. However, limitations such as the small sample size and the reliance on secondary data warrant further investigation. Future research should explore larger and more diverse populations, incorporate advanced diagnostic markers like LDL-C and glycemic variability, and adopt longitudinal designs to better understand the dynamic interplay between lipid and glucose metabolism. Compliance with ethical standards Acknowledgments The author would like to express their deepest gratitude to the two research supervisors who contributed to the preparation and production of this research. Their support during data collection and analysis was invaluable in ensuring the consistency of the study. The author would like to express their gratitude to the staff of ODSK Hospital, North Sulawesi, who provided access to the medical records of the patients used in this study. The author would also like to thank the Faculty of Medicine, Sam Ratulangi University for providing academic support during the research process. In addition, the authors are grateful to the Ethics Committee of the Health Polytechnic of the Ministry of Health Manado for their approval and guidance in ensuring the ethical compliance of this study. Disclosure of conflict of interest The authors declare no conflict of interest regarding the publication of this study. All funding sources, if any, were independent of the research objectives, and no external parties influenced the design, data collection, analysis, or interpretation of the results. Statement of ethical approval This study followed the WHO ethical principles established in 2011 and received ethical approval from the Ethics Committee of the Health Polytechnic of the Ministry of Health Manado, with the approval reference number KEPK.01/12/502/2024. This process confirmed that the study adhered to ethical guidelines for medical research involving patient data. Statement of informed consent This study was retrospective and used secondary data from patients' medical records at ODSK Hospital; the ethics committee did not require informed consent. Furthermore, patient data was anonymized and handled confidentially to protect privacy and ensure compliance with ethical standards. References [1] Dilworth L, Facey A, Omoruyi F. Diabetes mellitus and its metabolic complications: The role of adipose tissues. Int J Mol Sci; 22. Epub ahead of print 2 July 2021. DOI: 10.3390/ijms22147644. [2] Vieira R, Souto SB, Sánchez-López E, et al. Sugar-lowering drugs for type 2 diabetes mellitus and metabolic syndrome—review of classical and new compounds: Part-i. Pharmaceuticals; 12. Epub ahead of print 1 December 2019. DOI: 10.3390/ph12040152. World Journal of Biology Pharmacy and Health Sciences, 2025, 21(02), 082–089 88 [3] Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 2018; 14: 88–98. [4] Peng J, Zhao F, Yang X, et al. Association between dyslipidemia and risk of type 2 diabetes mellitus in middle-aged and older Chinese adults: A secondary analysis of a nationwide cohort. BMJ Open; 11. Epub ahead of print 25 May 2021. DOI: 10.1136/bmjopen-2020-042821. [5] Zaka N, Gul N, Ishfaq N, et al. Association of hypertension with Non-HDL cholesterol in patients with type 2 diabetes mellitus. Journal of the College of Physicians and Surgeons Pakistan 2020; 30: 822–827. [6] Strikić D, Vujević A, Perica D, et al. Importance of Dyslipidaemia Treatment in Individuals with Type 2 Diabetes Mellitus—A Narrative Review. Diabetology 2023; 4: 538–552. [7] Dal Canto E, Ceriello A, Rydén L, et al. Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. Eur J Prev Cardiol 2019; 26: 25–32. [8] Cohain AT, Barrington WT, Jordan DM, et al. An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease. Nat Commun; 12. Epub ahead of print 1 December 2021. DOI: 10.1038/s41467-020-20750-8. [9] Zhu L, Zhu X, Wu Y. Effects of Glucose Metabolism, Lipid Metabolism, and Glutamine Metabolism on Tumor Microenvironment and Clinical Implications. Biomolecules 2022; 12: 580. [10] Liu B, Meng Q, Gao X, et al. Lipid and glucose metabolism in senescence. Front Nutr; 10. Epub ahead of print 23 October 2023. DOI: 10.3389/fnut.2023.1157352. [11] Li M, Chi X, Wang Y, et al. Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther; 7. Epub ahead of print 1 December 2022. DOI: 10.1038/s41392-022-01073-0. [12] Lee SH, Park SY, Choi CS. Insulin Resistance: From Mechanisms to Therapeutic Strategies. Diabetes Metab J 2022; 46: 15–37. [13] Sarhadi NA, Fakhreddin-Nejad M-E, Rajabi M-H, et al. Evaluation of fasting blood sugar and lipid profile in patients with type 1 and type 2 diabetes and normoglycemic individuals in Gorgan, Northeastern Iran. Journal of Clinical and Basic Research 2019; 3: 6–10. [14] Khil J, Kim SM, Chang J, et al. Changes in total cholesterol level and cardiovascular disease risk among type 2 diabetes patients. Sci Rep; 13. Epub ahead of print 1 December 2023. DOI: 10.1038/s41598-023-33743-6. [15] Wang L, Yan N, Zhang M, et al. The association between blood glucose levels and lipids or lipid ratios in type 2 diabetes patients: A cross-sectional study. Front Endocrinol (Lausanne); 13. Epub ahead of print 6 September 2022. DOI: 10.3389/fendo.2022.969080. [16] Saptaningtyas R, Wahyuhendra R, Teguh Isworo J, et al. Correlation between fasting blood sugar and ldl cholesterol of type 2 DM patients on William Booth Hospital Semarang. Jambura Journal Of Health Sciences And Research 2022; 4: 604–608. [17] Rooiqoh QF, Tamtomo DG, Cilmiaty R. Blood sugar levels and lipid profiles of T2DM among hypertension patients in Bambanglipuro Health Centre. Epidemiology and Society Health Review (ESHR) 2022; 4: 29–35. [18] Kim MK, Han K, Park YM, et al. Associations of variability in blood pressure, glucose and cholesterol concentrations, and body mass index with mortality and cardiovascular outcomes in the general population. Circulation 2018; 138: 2627–2637. [19] Pradana MFAD, Limijadi EKS, Tjahjono K, et al. Relationship between fasting blood glucose levels and ldl cholesterol levels in patients with type-2 diabetes mellitus in Diponegoro National Hospital. DIPONEGORO MEDICAL JOURNAL (JURNAL KEDOKTERAN DIPONEGORO) 2022; 11: 53–58. [20] Sulistiowati E, Sihombing M. NCEP-ATP III and IDF criteria for metabolic syndrome predict type 2 diabetes mellitus. Universa Medicina 2016; 35: 46. [21] Soelistijo SA, Lindarto D, Decroli E, et al. Pedoman Pengelolaan dan Pencegahan Diabetes Melitus Tipe 2 di Indonesia 2021. 1st ed. Jakarta: PB-PERKENI, https://pbperkeni.or.id/catalog-buku/pedoman-pengelolaan-danpencegahan-diabetes-melitus-tipe-2-di-indonesia-2021 (2021, accessed 22 January 2025). [22] Kshanti IAM, Wibudi A, Sibarani RP, et al. Pedoman pemantauan glukosa darah mandiri 2021. 1st ed. Jakarta: PBPERKENI, https://pbperkeni.or.id/catalog-buku/pedoman-pemantauan-glukosa-darah-mandiri (2021, accessed 22 January 2025). World Journal of Biology Pharmacy and Health Sciences, 2025, 21(02), 082–089 89 [23] Mishra P, Pandey CM, Singh U, et al. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth 2019; 22: 67–72. [24] Xing L, Peng F, Liang Q, et al. Clinical Characteristics and Risk of Diabetic Complications in Data-Driven Clusters Among Type 2 Diabetes. Front Endocrinol (Lausanne); 12. Epub ahead of print 30 June 2021. DOI: 10.3389/fendo.2021.617628. [25] Shatz I. Assumption-checking rather than (just) testing: The importance of visualization and effect size in statistical diagnostics. Behav Res Methods 2024; 56: 826–845. [26] Rovetta A. Raiders of the Lost Correlation: A Guide on Using Pearson and Spearman Coefficients to Detect Hidden Correlations in Medical Sciences. Cureus. Epub ahead of print 30 November 2020. DOI: 10.7759/cureus.11794. [27] Schober P, Schwarte LA. Correlation coefficients: Appropriate use and interpretation. Anesth Analg 2018; 126: 1763–1768. [28] Hossain MJ, Al-Mamun M, Islam MR. Diabetes mellitus, the fastest growing global public health concern: Early detection should be focused. Health Sci Rep; 7. Epub ahead of print 1 March 2024. DOI: 10.1002/hsr2.2004.