Predictors Of Dyslipidemia Research Articles

Comparison of obesity indicators for predicting cardiovascular risk factors and multimorbidity among the Chinese population based on ROC analysis

To date, the best obesity-related indicators (ORIs) for predicting hypertension, dyslipidaemia, Type 2 diabetes mellitus (T2DM) and multimorbidity are still controversial. This study assessed the ability of 17 ORIs [body mass index (BMI), body fat percentage (BF%), c-index, Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), waist-hip ratio (WHR), waist-to-height ratio (WHtR), body roundness index (BRI), abdominal volume index (AVI), triglyceride glucose index (TYG), lipid accumulation product (LAP), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), waist triglyceride index (WTI) and cardiometabolic index (CMI)] to predict hypertension, dyslipidemia, T2DM, and multimorbidity in populations aged 40–69 years. From November 2017 to December 2022, 10,432 compliant residents participated in this study. Receiver operating characteristic curves were used to assess the ability of ORIs to predict target diseases across the whole population and genders. The DeLong test was used to analyse the heterogeneity of area under curves (AUCs). Multivariable logistic regression was used to analyse the association of ORIs with hypertension, dyslipidaemia, T2DM, and multimorbidity. The prevalence of hypertension, dyslipidaemia, T2DM, and multimorbidity was 67.46%, 39.36%, 12.54% and 63.58%, respectively. After excluding ORIs associated with the target disease components, in the whole population, CVAI (AUC = 0.656), BMI (AUC = 0.655, not significantly different from WC and AVI), CVAI (AUC = 0.645, not significantly different from LAP, CMI, WHR, and WTI), and TYG (AUC = 0.740) were the best predictor of hypertension, dyslipidemia, T2DM, and multimorbidity, respectively (all P < 0.05). In the male population, BF% (AUC = 0.677), BMI (AUC = 0.698), CMI (AUC = 0.648, not significantly different from LAP and CVAI), and TYG (AUC = 0.741) were the best predictors (all P < 0.05). In the female population, CVAI (AUC = 0.677), CUN-BAE (AUC = 0.623, not significantly different from BF%, WC, WHR, WHtR, BRI and BMI), CVAI (AUC = 0.657, not significantly different from WHR), TYG (AUC = 0.740) were the best predictors (all P < 0.05). After adjusting for all covariates, all ORIs were significantly associated with hypertension, dyslipidaemia, T2DM, and multimorbidity (all P < 0.05), except for ABSI and hypertension and BAI and T2DM, which were insignificant. Ultimately, after considering the heterogeneity of prediction of ORIs among different populations, for hypertension, BF% was the best indicator for men and CVAI for the rest of the population. The best predictors of dyslipidaemia, T2DM, and multimorbidity were BMI, CVAI and TYG, respectively. Screening for common chronic diseases in combination with these factors may help to improve the effectiveness.

Diabetic dyslipidemia and its predictors among people with diabetes in Ethiopia: systematic review and meta-analysis

BackgroundDyslipidemia is an imbalance of lipid profiles. It increases the chance of clogged arteries and may cause heart attacks, strokes, and other circulatory disorders. Dyslipidemia affects the general population, but its severity is higher in diabetic populations. As a result, the chance of dyslipidemia-associated morbidity and mortality is highest in diabetic patients. In Ethiopia, around 2 to 6.5% of the population live with diabetes, but their lipid profiles are inconsistent across the studies. Therefore, this study aimed to estimate the pooled prevalence of diabetic dyslipidemia and its predictors among people with diabetes in Ethiopia.MethodA systematic review and meta-analysis was conducted. The searches were carried out in MEDLINE via PubMed and OVID, EBSCO, Embase, and other supplementary gateways such as Google and Google Scholar, for articles published up to June 2023. The articles were searched and screened by title (ti), abstract (ab), and full text (ft). The quality of the eligible studies was assessed by the Newcastle–Ottawa scale. The heterogeneity was detected by the Cochrane Q statistic test and the I-squared (I2) test. Then subgroup analysis and meta-regression analysis were used to identify the source of the variations. A random or fixed-effect meta-analysis model was used to estimate the overall pooled prevalence and average effects. The publication bias was assessed by the funnel plot asymmetry test and/or Begg and Mazumdar’s test for rank correlation (p-value < 0.05). The protocol has been registered in an international database, the prospective register of systematic reviews (PROSPERO), with reference number CRD42023441572.ResultA total of 14 articles with 3662 participants were included in this review. The pooled prevalence of diabetic dyslipidemia in Ethiopia was found to be 65.7% (95% confidence interval (CI): 57.5, 73.9), I2 = 97%, and p-value < 0.001. The overall prevalence of triglycerides (TG) and high-density lipoprotein cholesterol (HDL-c) were found to be 51.8% (95% CI: 45.1, 58.6) and 44.2% (95% CI: 32.8, 55.7), respectively, among lipid profiles. In meta-regression analysis, the sample size (p value = 0.01) is the covariate for the variation of the included studies. Being female (adjusted odds ratio (AOR): 3.9, 95% CI: 1.5, 10.1), physical inactivity (AOR: 2.6, 95% CI: 1.5, 4.3), and uncontrolled blood glucose (AOR: 4.2, 95% CI: 1.9, 9.4) were found to be the determinants of dyslipidemia among diabetic patients.ConclusionThis review revealed that the prevalence of diabetic dyslipidemia is high among people with diabetes in Ethiopia. Being female, having physical inactivity, and having uncontrolled blood glucose were found to be predictors of dyslipidemia among people with diabetes. Therefore, regular screening of lipid profiles and the provision of lipid-lowering agents should be strengthened to reduce life-threatening cardiovascular complications. Furthermore, interventions based on lifestyle modifications, such as regular physical activity and adequate blood glucose control, need to be encouraged.

Linking elevated HbA1c with atherogenic lipid profile among high risk cardiovascular patients at Qassim, Saudi Arabia.

The relationship between glycated hemoglobin (HbA1c) and an atherogenic lipid profile which is associated with a higher risk of cardiovascular disease is of interest. A retrospective cross-sectional study was conducted on 83 participants aged between 14 and 77 years. Their venous blood was drawn to determine the HbA1c and fasting lipid profile including total cholesterol triglycerides and high-density lipoprotein cholesterol (HDL-C) low-density lipoprotein cholesterol (LDL-C) non-HDL cholesterol and the LDL/HDL ratio were also calculated. The correlations between HbA1c levels and these lipid profile parameters were analyzed. The study showed a significant correlation between HbA1c and LDL-C non-HDL-C and the LDL/HDL ratio. Although there was no significant difference in total cholesterol levels among all groups the levels of total cholesterol and HbA1c were positively correlated. HDL-C exhibited direct correlations with HbA1c there was no correlation between HbA1c and clinical characteristics except for age. Data shows that HbA1c can be used as a predictor of dyslipidemia in diabetic patients there is a significant correlation between HbA1c and an atherogenic lipid profile which highlights the importance of glycemic control in reducing the risk of cardiovascular disease.

1166A&gt;C polymorphism of the &lt;i&gt;AGTR1&lt;/i&gt; gene as a marker metabolic disorders in the North residents

BACKGROUND: dyslipidemia is currently considered to be one of cardiovascular risk factors. Angiotensin II receptor type I (AGTR1) genetic polymorphisms are known as candidate genes for hypertension, diabetes, as well as for diabetes and obesity complications. Until now, there are not much data on how 1166A&gt;C (rs5186) polymorphism of the AGTR1 gene correlates with Northerners’ carbohydrate and lipid metabolism disorders. In addition, the data are contradictory. Following on from this, we see it is relevant to study the subject.AIM: this research assessed variants of 1166A&gt;C (rs5186) polymorphism of the AGTR1 gene as a predictor of dyslipidemia, carbohydrate metabolism disorders, overweight, and hypertension.MATERIALS AND METHODS: the North residents from Magadan Region, Caucasian by ethnicity, aged from 24 to 56 (average age 43.7± 1.4 yrs) participated in the survey. By real-time polymerase chain reaction we determined the single nucleotide polymorphism of the AGTR1 (rs5186) gene. We also analyzed physical development and cardiovascular variables as well as the concentrations of glucose, insulin, glycosylated hemoglobin, C-reactive protein, total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The insulin resistance index and the atherogenicity coefficient were calculated using standard methods.RESULTS: the examined subjects were one hundred and one volunteers. According to the results of genetic analysis, 55 people were assigned to the group of homozygotes for the wild type (AA) and 46 people were assigned to the group of the AGTR1*C allele variant carriers (heterozygotes and homozygotes AC+CC). Our findings contributed to the evidence on more unfavorable lipid pictures showed by the AGTR1*C allele variant carriers: significantly high values of total cholesterol (5,77±0,11, р=0.045), low-density lipoproteins (3,87±0,09, р=0.009), triglycerides (1,43±0,06, р=0.035), and atherogenicity coefficient (3,61±0,10, р=0.001), along with significantly low values of high-density lipoproteins (1,30±0,03, р=0,008). The above indicators were observed as opposed to significantly high fasting glycemia (5,74±0,14, р=0.006) and glycosylated hemoglobin (5,74±0,09, р=0.001) exhibited by the AA homozygotes subjects whose indices could be defined as the state of prediabetes. No intergroup differences were found in anthropometric or cardiovascular variables.CONCLUSION: thus, we could see impairments in the lipid pictures of the AGTR1*С polymorphic variant carriers along with the optimization of carbohydrate metabolism and no effect on the blood pressure or anthropometric characteristics.

Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine

BackgroundsDyslipidemia is a prominent risk factor for cardiovascular diseases and one of the primary independent modifiable factors of diabetes and stroke. Statins can significantly improve the prognosis of dyslipidemia, but its side effects cannot be ignored. Traditional Chinese Medicine (TCM) has been used in clinical practice for more than 2000 years in China and has certain traits in treating dyslipidemia with little side effect. Previous research has shown that Mutual Obstruction of Phlegm and Stasis (MOPS) is the most common dyslipidemia type classified in TCM. However, how to compose diagnostic factors in TCM into diagnostic rules relies heavily on the doctor's experience, falling short in standardization and objectiveness. This is a limit for TCM to play its advantages of treating dyslipidemia with MOPS.MethodsIn this study, the syndrome diagnosis in TCM was transformed into the prediction and classification problem in artificial intelligence The deep learning method was employed to build the classification prediction models for dyslipidemia. The models were built and trained with a large amount of multi-centered clinical data on MOPS. The optimal model was screened out by evaluating the performance of prediction models through loss, accuracy, precision, recall, confusion matrix, PR and ROC curve (including AUC).ResultsA total of 20 models were constructed through the deep learning method. All of them performed well in the prediction of dyslipidemia with MOPS. The model-11 is the optimal model. The evaluation indicators of model-11 are as follows: The true positive (TP), false positive (FP), true negative (TN) and false negative (FN) are 51, 15, 129, and 9, respectively. The loss is 0.3241, accuracy is 0.8672, precision is 0.7138, recall is 0.8286, and the AUC is 0.9268. After screening through 89 diagnostic factors of TCM, we identified 36 significant diagnosis factors for dyslipidemia with MOPS. The most outstanding diagnostic factors from the importance were dark purple tongue, slippery pulse and slimy fur, etc.ConclusionsThis study successfully developed a well-performing classification prediction model for dyslipidemia with MOPS, transforming the syndrome diagnosis problem in TCM into a prediction and classification problem in artificial intelligence. Patients with dyslipidemia of MOPS can be accurately recognized through limited information from patients. We also screened out significant diagnostic factors for composing diagnostic rules of dyslipidemia with MOPS. The study is an avant-garde attempt at introducing the deep-learning method into the research of TCM, which provides a useful reference for the extension of deep learning method to other diseases and the construction of disease diagnosis model in TCM, contributing to the standardization and objectiveness of TCM diagnosis.

Glycosylated Hemoglobin (HbA1c) as a Marker for Dyslipidemia in Type 2 Diabetes Mellitus

Objective: The purpose of this research was to examine the effectiveness of HbA1c as a predictor of dyslipidemia in patients with Type-2 Diabetes Mellitus. Study Design: Cross-sectional Place and Duration: Department of Pathology; Women Medical &amp; Dental College Abbottabad in collaboration with Jinnah International Hospital, Abbottabad; April 2022 – September 2022 Methods: After obtaining their agreement, a total of 110 people with Type-2 diabetes mellitus were included in the research. Statistics and background information were recorded. Fasting lipid profile (cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, and cholesterol/HDL), HbA1c, creatinine, and electrocardiogram were conducted as part of the comprehensive systemic examination to detect the presence of complications or co-morbidities. Mean, frequency, and correlation analyses are performed in SPSS 22.0. When doing a correlation analysis, the Pearson Chi-square test was typically utilized. Results: Twenty-two patients (20%) had an HbA1c below 7%, whereas eighty-eight patients (80%) had a HbA1c beyond 7%. Dyslipidemia was seen in 62 individuals (56.4%). Direct connections were found between HbA1c and BMI, cholesterol, triglycerides, and low-density lipoprotein (LDL), while an inverse correlation was found between HbA1c and high-density lipoprotein (HDL). When comparing female and male patients, females were shown to have considerably greater TG levels. HbA1c levels were also significantly correlated with the presence of metabolic syndrome, particularly among women (P0.003). Conclusion: According to the findings of our research, HbA1c is not only useful as a biomarker of long-term glycaemic control, but it is also an excellent predictor of lipid profile. Keywords: Lipid Profile, Dyslipidemia, Type 2 Diabetes mellitus, HbA1c

Prevalence and predictors of dyslipidemia among HAART treated and HAART naive HIV positive clients attending Debre Tabor Hospital, Debre Tabor, Ethiopia

BackgroundHighly active anti-retroviral therapy has been reported to be associated with a number of side effects in human immunodeficiency virus patients among which dyslipidemia isa common metabolic disorder. MethodsA Hospital based comparative cross-sectional study among 228 HIV positive patients was conducted from July to August 2020. Socio-demographic and clinical data were collected using structured questionnaires. Fasting venous blood sample was collected and analyzed for Lipid profiles. EDTA sample was analyzed for CD4+ T cell determination. Anthropometric measurement was done. Data were analyzed using SPSS version 22. Independent t-test was done. Logistic and binary regression was done. ResultA total of 228 HIV patients were enrolled in the study. Prevalence of dyslipidemia in HAART naive and HAART treated patients was 61 (53.5%) and 84 (73.7%), respectively. The prevalence of Total Cholesterol ≥200 mg/dl was 50% and 30%; High density lipoprotein cholesterol <40 mg/dl was 43.8% and 36%; Low density lipoprotein cholesterol ≥130 mg/dl was 48.3% and 28.1%; and Triglyceride ≥ 150 mg/dl 59.6% and 39% among HAART treated and HAART naive, respectively. Age greater than 40 years (AOR = 3.27, 95% C.I: 1.47–7.25), blood pressure ≥140/90 (AOR = 16.13, 95% C.I: 5.81–44.75), being on HAART (AOR = 2.73, 95% C.I: 1.35–5.53) and body mass index >25 kg/m2 (AOR = 1.92, 95% C.I: 1.20–4.81) were identified as determinants of dyslipidemia. ConclusionThe mean value of lipid profile was significantly higher among HAART treated as compared to those HAART naive HIV positive clients.

Anthropometric markers of abdominal adiposity and its relation to dyslipidemia and insulin resistance among overweight/obese schoolchildren and adolescents

Objective: to assess the relationship between anthropometric indicators and laboratorial markers of cardiovascular risk in overweight/obese children and adolescents, in order to verify whether any anthropometric indicator has a better potential for use in screening cardiovascular risk in the population. Method: retrospective cross-sectional study enrolling 237 individuals aged 7 to 18 years. Body mass index (BMI), waist circumference (WC), waist circumference/height index (WC/H), glucose, HOMA-IR, total cholesterol (TC), LDL, HDL, triglycerides and TC/HDL and LDL/HDL indexes were obtained. Associations between anthropometric and laboratory markers were tested in contingency tables using the chi-square test. Correlations were tested by Spearman's correlation. Results: higher WC (Freedman cutoffs) was associated with lower levels of HDL and higher score in the TC/HDL and LDL/HDL indexes, but, using +2 z-scores as the cutoff, there were associations with low HDL and higher HOMA-IR. WC/H indicator (0.5 cutoff) was not associated with any of the outcomes, but, using +2 z-scores, an association was found with HOMA-IR. Z-scores of WC, WC/H and BMI showed positive correlation with HOMA-IR, TC/HDL and HOMA-IR, respectively. Negative correlations were found between WC and WC/H z-scores with HDL. WC and WC/H z-score were related to changes in HDL and HOMA-IR. Conclusions: there seems to be an advantage in using WC alone as a possible predictor of dyslipidemia and insulin resistance in children and adolescents. It is not possible to state that WC, WC/H or BMI measurements differ in their abilities to identify Brazilian children and adolescents with risk factors for cardiovascular diseases.

Prevalence, patterns and predictors of dyslipidaemia in Nigeria: a report from the REMAH study.

The aim of this study was to determine the prevalence and predictors of dyslipidaemia in adults in Nigeria. Using the WHO criteria, we determined dyslipidaemia using serum lipid levels of 3 211 adult Nigerians, aged at least 18 years, obtained between March 2017 and February 2018 from two communities (rural and urban) in a state from each of the six geopolitical zones of Nigeria. The overall prevalence of low high-density lipoprotein cholesterol (l-HDL), elevated low-density lipoprotein cholesterol (e-LDL), hypertriglyceridaemia (h-TG) and hypercholesterolaemia (h-CHL) were 72.5,13.6, 21.4 and 7.5%, respectively. The adjusted odds of h-CHL [odds ratio (95% confidence interval) 1.47 (1.10-1.95)], h-TG [1.89 (1.48-2.41)] and e-LDL [1.51 (1.03-2.15)] increased with obesity. Being a rural dweller increased the odds of h-TG [1.55 (1.29-1.85)], e-LDL [1.38 (1.10-1.73)] and l-HDL [1.34 (1.14-1.58)]. The odds of h-CHL [2.16 (1.59-2.95)], h-TG [1.21 (1.01-1.47)], e-LDL [1.42 (1.13-1.80)] and l-HDL [0.78 (0.65-0.93)] increased with hypertension. Diabetes mellitus doubled only the odds of h-TG [2.04(1.36-3.03)]. The prevalence of dyslipidaemia, particularly low HDL-C, is high among adult Nigerians.

Gender Difference in Lipid Profile in Hypertensive Patients’ Hospital-Based Study

Background and objectives: Hypertension and dyslipidemia are the integral components of metabolic syndrome. It has emerged as cause of substantial mortality in both genders. The objective of the study was to find out the prevalence of dyslipidemia in both gender and difference in pattern of lipid profile in hypertensive patients. Methods: This was an observational study conducted in 2018 to 2019 at Sir Syed university Hospital Karachi Pakistan. Sample size of 250 diagnosed cases of hypertension (comprising equal number of males and females) were collected. The lipid values were interpreted in normal or high-risk category by applying NCEP ATPIII criterion. Fasting sample were collected for sugar and lipid profile. Anthropometric, demographic and biochemical data was tabulated. Categorical variables were displayed as mean ± standard deviation and percentage respectively, compared with chi-square test. Student 't' test was use to assess the difference for the lipid profile at 5% significance level. The prevalence of dyslipidemia was estimated for men and women, and pattern in lipid profile were compared. The factors associated with dyslipidemia in both genders were calculated by multiple logistic regression expressed as odd Ratio with 95% confidence interval (C.I). To evaluate the effect of age on dyslipidemia we made four age groups 25-65 years and lipid pattern was compared. Results: Dyslipidemia was found 63% of hypertensive group, 72% women were dyslipidemia than 54% men and pattern of lipid in males showed low LDL and high cholesterol and in females’ high triglycerides and low HDL. The Predictors of dyslipidemia in female were old age, BMI kg/m2 high blood pressure level, depression and lack of physical activity and in men smoking intake of fried, salty food, smoking and obesity with increasing age. Females have more triglycerides total cholesterol and low HDL compared to men. Conclusion: Dyslipidemia was more found in women with hypertension but as the age increasing more women have dyslipidemia. while treating hypertensive patient’s lipid lowering management gender heterogeneity should be considered.

Evaluation of glycated hemoglobin (HbA1C) as a predictor of dyslipidemia in type 2 diabetic patients

Evaluation of glycated hemoglobin (HbA1C) as a predictor of dyslipidemia in type 2 diabetic patients

Glycemic control and dyslipidemia in type 2 diabetic patients in an Indian rural tribal locality: A family medicine practitioner’s perspective

Background: Diabetes and dyslipidemia commonly coexist, frequently associated with various cardio-vascular (CV) risk factors and good glycemic control is key for prevention of long-term CV complications. Although diabetes and dyslipidemia commonly coexist in India, there is a lack of evidence on pattern of dyslipidemia and whether dyslipidemia is adequately managed or not, particularly in rural population in a real-world setting. Aims and Objectives: This study was conducted to assess present glycemic status and lipid profile of the population residing in a rural tribal locality of Jharkhand (India) as part of project for fellowship in diabetes course by Department of Endocrinology, DEDU, CMC, Vellore. Materials and Methods: This non-interventional cross-sectional study was conducted in a tribal locality of Jharkhand (India) after concept note approval for ethical clearance from CMC Vellore. Whole-blood and sera of diabetic patients were analyzed for fasting-blood-sugar, Glycated-hemoglobin (HbA1c), total-cholesterol (CH), triglycerides (TGs), high-density-lipoprotein-cholesterol, low-density-lipoprotein-cholesterol, and very-VLDL-C. Correlation test of HbA1c with lipid-ratios and individual lipid indexes was done. Results: Mean Hb1Ac level was 7.24 ± 1.80 and interestingly, was marginally higher (7.31 ± 1.92 vs. 6.92 ± 1.16) in patients with DM &lt;5 years as compared to those with DM &gt;5 years. Mixed dyslipidemias were common with abnormal TG, LDL, VLDL, High-density lipoprotein (HDL), and Total CH values. Hb1Ac-levels showed significant positive correlation with serum CH, TG, LDL, and VLDL levels while significant negative correlation with HDL levels in the study. Conclusion: Apart from a reliable indicator of long-term glycemic control, HbA1c can also be used as a predictor of dyslipidemia and thus early diagnosis of dyslipidemia can prevent life-threatening CV-complications.

Correlation between glycated hemoglobin HbA1c and serum lipid profile in patients with type 2 diabetes

Background: Hyperlipidemia is a common risk factor for diabetes that leads to cardiovascular disease, one of the causes of death of diabetic patients. The aim of this study was to investigate the association between HbA1c levels and serum lipids in Libyan patients with type 2 diabetes. Material and methods: The study was conducted in 2019 on 325 patients (174 males, 151 females) with type 2 diabetes referred to a private clinical laboratory in Tripoli, Libya. Blood samples were collected for measurement of HbA1c, fasting blood glucose and serum lipid concentrations. Diabetes was defined according to the American Diabetes Association criteria. The data were analyzed using an independent t-test and Pearson’scorrelation test. Results: The ages of the patients ranged from 40 to 83 years, with a mean of 51.52 ± 14.32 years SD. No significant correlation between HbA1c and age was noted (r=0.011, p=0.063). There was a significant positive correlation betweenHbA1c level and fasting blood glucose (r =0.641, p=0.000), low-density lipoprotein (r = 0.240, p = 0.000), total cholesterol (r = 0.223, p = 0.000) and triglycerides (r=0.140, p 0.067). The correlation between HbA1c and high-density lipoprotein-C was negative but not significant (r= -0.088, p = 0.123). Conclusion: HbA1c could be used as a predictor of dyslipidemia and thus it may serve as an indicator of the development of cardiovascular disease in patients with type-2 diabetes mellitus

Correlation between glycated hemoglobin HbA1c and serum lipid profile in patients with type 2 diabetes

Background: Hyperlipidemia is a common risk factor for diabetes that leads to cardiovascular disease, one of the causes of death of diabetic patients. Theaimof this study was to investigate the association between HbA1c levels and serum lipids in Libyan patients withtype 2 diabetes. Material and methods: The study was conducted in 2019 on 325 patients (174 males, 151 females) with type 2 diabetes referred to a private clinical laboratory in Tripoli, Libya. Blood samples were collected for measurement of HbA1c, fasting blood glucose and serum lipid concentrations. Diabetes was defined according to the American Diabetes Association criteria.The data were analyzed using an independent t-test and Pearson’scorrelation test.Results: The ages of the patients ranged from 40 to 83 years, with a mean of 51.52 ± 14.32 years SD. No significant correlation between HbA1c and age was noted (r=0.011, p=0.063). There was a significant positive correlation betweenHbA1c level and fasting blood glucose (r =0.641, p=0.000), low-density lipoprotein (r = 0.240, p = 0.000), total cholesterol (r = 0.223, p = 0.000) and triglycerides(r=0.140,p 0.067). The correlation between HbA1c and high-density lipoprotein-C was negative but not significant (r= -0.088, p = 0.123). Conclusion: HbA1c could be used as a predictor of dyslipidemia and thus it may serve as anindicator of the development of cardiovascular disease in patients with type-2 diabetes mellitus.

AACE2021-A-1046: Frequency of Dyslipidemia and Associated Factors Among Patients with Type 2 Diabetes Mellitus in Peshawar

Atherogenic dyslipidemia in type 2 diabetes mellitus is a significant modifiable factor for cardiovascular disease (CVD), contributing to a high risk of morbidity and mortality. Poor glycemic control in diabetic patients is considered a secondary cause of dyslipidemia, with data from earlier studies indicating that it is a predictor of dyslipidemia, with higher total cholesterol (TC), trigerylcerides (TG), low density lipoprotein (LDL), and decreased high density lipoprotein (HDL). Dyslipidemia is a modifiable risk factor for CVD, thus understanding its impact is critical.

Role of Lipid Profile and Fasting Blood Glucose in Metabolic Syndrome

Background: The metabolic syndrome is defined by the clustering of risk factors that predisposes people to diabetes and cardiovascular disease (CVD. MetS had a high (58%) prevalence of dyslipidemia, and increasing age, greater BMI, central obesity, hypertension, and high blood glucose levels have been found as independent predictors of dyslipidemia.&#x0D; Aim of the Work: This study aimed to role of lipid profile and fasting blood glucose in metabolic syndrome.&#x0D; Subjects and Methods: The research was carried out at the Index Medical College and Research Center in Indore, in the Department of Biochemistry. A total of 120 people with metabolic syndrome and 120 healthy controls were included in the study. A blood sample was taken from the anticubital vein and centrifuged to obtain serum, which was then evaluated for lipid profile and FBS levels quantitatively. Result and Conclusion: Present study suggested that the FBG is the most critical factor in MS, and all aspects of the lipid profile have significant relationships with MS.&#x0D; Keywords: Metabolic syndrome, fasting blood glucose

Correlation between Glycosylated Haemoglobin and Dyslipidaemia in Patients with Type 2 Diabetes Mellitus

Introduction: Diabetes mellitus is one of the leading noncommunicable diseases all over the world including India. Diabetes ischaracterized by chronic hyperglycemia and disturbances of carbohydrate, lipid and protein metabolism. Glycated hemoglobin (HbA1c) level of ≥6.5% has been included as a criterion for diagnosis of diabetes. Impaired lipid profile is commonly present in type 2 diabetes. Materials and Methods: This observational cross-sectional study was carried out in the Department of General Medicine at Tertiary Care Teaching Hospital over a period of 6 months. A total 90 patients with T2DM of age more than 30 years were selected for the purpose. Fasting blood glucose (FBG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triglyceride (TG) and glycated haemoglobin (HbA1c) levels were evaluated. Test of significance was calculated by unpaired Student's 't' test. Correlation studies (Pearson's correlation) were performed between glycated haemoglobin (HbA1c) and serum lipid profile. Result: In our study, among 90 Type 2 diabetic individuals included in this study, 39 were male and 51 were female. Distribution of Glucose Triad results of FBS, PPBS and HbA1c levels of patients presented as Mean±SD, mean FBS was 187.62±48.32, mean PPBS was 227.46±83.14 and mean HbA1c was 7.97±0.69. Mean total cholesterol was 193.34 ± 13.73, mean total triglyceride was 201.65 ± 14.94, Mean HDL was 36.38 ± 3.74, mean LDL was 116.63 ± 8.74 and VLDLwas 40.33 ± 1.73. HbA1c positively and significantly correlated with total cholesterol (r=0.204), LDL (r=0.297), HbA1c negatively and significantly correlated with HDL (r= - - 0.136), and did not show any show correlation with VLDL (r=0.053) and total triglycerides (r=0.027). Conclusion: The study concluded that HbA1c value correlate well with lipid profile in-diabetes patients. So, HbA1c can be used as a predictor of dyslipidemia in type 2 diabetes. This may help in predicting the lipid profile levels from the degree of glycemic control and therefore, identifying the patients with increased risk of diabetic complications.

Association between HbA1c and dyslipidemia among sample of Iraqi Patients with Type2 DM

Background: Diabetic Mellitus is considered as a public health concern. More than 8 percent of the United States has diabetes. Diabetes is a serious risk factor for Atherosclerotic cardiovascular disease (ASCVD) and an important cause of mortality. ASCVD is the commonest cause of death in the Western world. Diabetes was defined as a high risk condition for ASCVD. In adults with diabetes with ASCVD or multiple ASCVD risk factors it is important to prescribe high intensity statin to reduce LDL at least to 50%.&#x0D; Objective: To investigate association between dyslipidemia and HbA1c and to detect benefit of using some statins in decreases the risk of CVD.&#x0D; Material and method: A prospective randomized single dose study was carried out at a private clinic in Wasit governorate-Iraq; included patients with type 2 diabetes. Clinical biochemical lab assessment and re assessment was carried out before and after 3 months of receiving Rosuvastatin 20 mg/ day. A questionnaire paper was used, including sociodemographic and clinical features (age, gender, measuring weight, height, and waist circumference; biochemical markers [total cholesterol, HDL, LDL, TG and HbA1c]).&#x0D; Results: A total of 256 type 2 DM patients were included; receiving 20 mg of rosuvastatin as a single dose for 3 consecutive months. 83 (32.4%) of them were males and 173 (67.6%) were females. Mean age of male (52.0710.486) and that of female (53.1110.410). the mean difference of (BMI, WC, HbA1c, LDL, TG, and cholesterol) among all studied sample after treatment was significantly lower than mean difference that measured before treatment, except that for HDL; where it was significantly higher after treatment, P &lt;0.001. Mean differences of HbA1c and total cholesterol were reduced significantly after treatment among males, females, age &lt;45 yrs., and age 45 yrs., P&lt;0.01; without significant differences in between groups, P&gt;0.05.&#x0D; Conclusions: HbA1c value associated with level of lipid profile in diabetic patients. All age groups and both gender have had benefit of rosuvastatin treatment in reduction of lipid cholesterol as well as HbA1c. Rosuvastatin can be used by type 2 diabetics’ regardless age and gender. HbA1c can be used as a predictor of dyslipidemia in type 2 diabetes.

CORRELATION BETWEEN HEMOGLOBIN A1C AND SERUM LIPID PROFILE WITH TYPE 2 DIABETES: A CROSS-SECTIONAL STUDY

Background: Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. Patients with type 2 diabetes (T2DM) have an increased prevalence ofdyslipidemia, which contributes to their high risk of cardiovascular diseases (CVDs). This study is an attempt to determine the correlation between hemoglobin A1C (HbA1c) and serum lipid profile and to evaluate the importance of HbA1c as an indicator of dyslipidemia with Type II diabetes mellitus. Methods: The study had 240 participants between the age groups of 20-70 years included 80 males and 40 females in diabetic group whereas the control group included 78 males and 42 females. T.cholesterol, Triglycerides, HDL were calculated by semi automated analyzer and LDL and VLDL were calculated by Friedewald’s Formula. HbA1c was measured by turbidMetric Method. Results: Data was represented as mean ± standard deviation or median with statistically significant values of T. Cholesterol, LDL, HDL, TGs and HbA1c (P&lt; 0.001). There was a significant positive correlation between HbA1c, TC, TG, and LDL-C. Patients with HbA1c value greater than 7.0% had significantly higher value of cholesterol, LDL-C, and compared with patients with an HbA1c value up to 7.0%. Conclusion: Significant positive correlation of HbA1c with Lipid profiles in our study suggests that HbA1c can also be used as a predictor of dyslipidemia in addition to a glycemic control parameter for prevention of complication.

Prevalence and predictors of dyslipidemia among hypertensive patients in Lumame Primary Hospital, Amhara, Ethiopia: A cross-sectional study

BackgroundSerum lipid profile abnormalities are major predictors for coronary artery diseases. The relationship between demographic factors and dyslipidemia in Ethiopia is not completely explored. Thus, this study aimed to assess the prevalence and predictors of dyslipidemia among hypertensive patients in Lumame Primary Hospital. MethodsA cross-sectional study was conducted from June to August 30, 2020, on the hypertensive patients in Lumame Primary Hospital. All adult hypertensive patients who visited the adult hypertensive care services during the study period were included. Interview-guided self-administered questionnaire and a chart review were used for data collection. Statistical Package for the Social Sciences (SPSS) software version 24.0 was used for data analysis. ResultsOut of 372 hypertensive patients, 190(51.1%) were females and the mean age of the study participants was 43.56 years (SD ± 4.31). The overall prevalence of dyslipidemia in this study was 48.4%. Besides the overall prevalence, the prevalence of TC, TG, LDL-c, and HDL-c was 73(19.6%), 91(24.5%), 60(16.1%), and 115(30.9%), respectively. Females were at higher risk for having high levels of TC (AOR = 2.31, 95% CI = 1.54–3.13), TG (AOR = 1.70, 95% CI = 1.34–3.79), LDL-c (AOR = 2.15, 95% CI = 1.56–2.86), and HDL-c (AOR = 2.67, 95% CI = 1.44–5.67) than males. Respondents who were from urban were at higher risk for having high levels of TC (AOR = 1.98, 95% CI = 1.04–6.83), TG (AOR = 1.78, 95% CI = 1.09–2.86), LDL-c (AOR = 3.01, 95% CI = 1.45–7.43), and HDL-c (AOR = 2.01, 95% CI = 1.94–4.55) than respondents who were from rural. Similarly, obese respondents were at higher risk for having high levels of TC (AOR = 2.03, 95% CI = 1.64–2.00), TG (AOR = 3.78, 95% CI = 1.06–6.42), LDL-c (AOR = 1.92, 95% CI = 1.66–2.12), and HDL-c (AOR = 4.23, 95% CI = 2.84–4.32) than to respondents who were underweight. ConclusionThe prevalence of dyslipidemia among hypertensive patients was high. Independent variables such as age, gender, residence, family history of HTN, smoking, alcohol drinking, fruit diet habits, physical activity, DM, and BMI were significant determinants of dyslipidemia.