Prevalence of comorbidities in patients with type–2 diabetes mellitus

Akın, Seydahmet; Bölük, Cem

doi:10.1016/j.pcd.2019.12.006

Cited by 25 publications

(16 citation statements)

References 12 publications

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“…Our study also found a significant prevalence of diabetic nephropathy, coronary artery disease and sexual dysfunctions by increasing age and male gender. Similar data were reported by previous studies where researchers have reported an increasing prevalence of male diabetic patients developing secondary health complications 4,26 . The order of comorbid prevalent in our study was CKD, GAD, Bone diseases and CVD, respectively.…”

Section: Discussionsupporting

confidence: 91%

“…Our comprehensive data further suggests that the highest prevalence of individual belong to urban areas compared to rural areas. Overall, our results are in accordance with the worldwide prevalence of diabetic populations, which are expected to occur lower than in rural areas or less urbanized communities 26 . Education is a profound tool, and formal education has been often linked with increased awareness regarding the development and management of the disease.…”

Section: Discussionsupporting

confidence: 90%

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The multifactorial burden of Type 2 Diabetes mellitus: a study from clinical settings of University Hospital, Karachi, Pakistan

Zaheer¹,

Zeb²,

Baloch³

et al. 2020

Int. j. endorsing health sci. res.

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Background: Diabetes is a multifactorial disease posing a socioeconomic threat to the worldwide community. The prevalence of Type 2 Diabetes Mellitus (T2DM) is continuously increasing in Pakistan and becoming a serious concern for the general public as well as concerned clinicians. This case-control study aimed to analyze the modifiable and non-modifiable risk factors associated with T2DM in the Pakistani population. Methodology: A structured questionnaire was filled by the T2DM patients attending the diabetic clinic at Dow University Hospital. Non-diabetic subjects were recruited from the general population. Participants were questioned regarding their age, body mass index (BMI), educational status, diabetes management practices and comorbidities associated with T2DM. Results: Our results suggest that BMI, gender, increasing age, educational status are significantly associated with T2DM. Our data also suggested that approximately 80% of T2DM patients had at least one comorbidity, whereas 59 % had two or more comorbidities. The most prevalent comorbidities found in our study group were in the order of Chronic Kidney disease (CKD) (15.8 %), Generalized Anxiety Disorder (GAD) (14.4 %), Bone disease (14 %) and Cardiovascular Disease (CVD) (12.6 %) respectively. Conclusion: Compared to worldwide data, our results are in accordance with the global prevalence with respect to age, gender and BMI, with CKD being the most prevalent comorbidity in contrast to CVD found worldwide.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 90%

The multifactorial burden of Type 2 Diabetes mellitus: a study from clinical settings of University Hospital, Karachi, Pakistan

Zaheer¹,

Zeb²,

Baloch³

et al. 2020

Int. j. endorsing health sci. res.

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“…According to the results, this study suggested that age and the course of diabetes in T2DM patients are important and immutable predictive risk factors for T2DM patients with stroke. Old age means the decline of the function of various tissues and organs of the body, pointing out that the risk of T2DM patients with stroke is affected by age ( 1 ). A study of 3,776 T2DM subjects identified age as an important risk factor ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

“…Data from the latest IDF Diabetes Atlas showed that the number of adults aged 20–79 years in the world suffering from diabetes was ~463 million in 2019. Diabetes mellitus (DM) is a great growing public health burden in China as the prevalence estimated at 11.6%, whereas that of prediabetes was ~50.1% ( 1 ).…”

Section: Introductionmentioning

confidence: 99%

Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus

et al. 2020

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Purpose: Stroke has sparked global concern as it seriously threatens people's life, bringing about dramatic health burdens on patients, especially for type 2 diabetes mellitus (T2DM) patients. Therefore, a risk scoring model is urgently valuable for T2DM patients to predict the risk of stroke incidence and for positive health intervention. Methods: We randomly divided 4,335 T2DM patients into two groups, training set ( n = 3,252) and validation set ( n = 1,083), at the ratio of 3:1. Characteristic variables were then selected based on the data of training set through least absolute shrinkage and selection operator regression. Three models were established to verify predictive ability. Foundation model was composed of basic information and physical indicators. Biochemical model consisted of biochemical indexes. Integrated model combined the above two models. Data of three models were then put into logistic regression analysis to form nomogram prediction models. Tools including C index, calibration plot, and curve analysis were implemented to test discrimination, calibration, and clinical use. To select the best predicting model, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were put into effect. Results: Eleven risk factors were determined, including age, duration of T2DM, estimated glomerular filtration rate, systolic blood pressure, diastolic blood pressure, low-density lipoprotein, high-density lipoprotein, triglyceride, body mass index, uric acid, and glycosylated hemoglobin A 1c , all with significant P -values through logistic regression analysis. In the training set, areas under the curve of three models were 0.810, 0.819, and 0.884, whereas in the validation set, they were 0.836, 0.832, and 0.909. Through calibration plot, the S:P values in the training set were 0.836, 0.754, and 0.621 and were 0.918, 0.682, and 0.666 separately in the validation set. In terms of the decision curve analysis, the risk thresholds were, respectively, 8–73%, 8–98%, and 8%~ in the training set and 8–70%, 8–90%, and 8–95% in the validation set. With the aid of NRI and IDI, integrated model is proved to be the best model in training set and validation set. Besides, internal validation was conducted on all the subjects in this study, and the C index was 0.890 (0.873–0.907). Conclusion: This study established a model predicting risk of stroke for T2DM patients through a community-based survey.

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“…Diabetic kidney disease (DKD) is the most common microvascular complication in patients with diabetes mellitus (DM). Approximately 20–40% of DM patients worldwide will develop DKD ( American Diabetes Association, 2019b ; Akin and Boluk, 2020 ). Persistent albuminuria [urine albumin – creatinine ratio (UACR) > 30 mg/g] is one of the clinical characteristics of DKD patients and also one of the laboratory indicators used in the clinical diagnosis of DKD.…”

Section: Introductionmentioning

confidence: 99%

Urine Metabolomics Analysis in Patients With Normoalbuminuric Diabetic Kidney Disease

Feng

Yang

et al. 2020

Front. Physiol.

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Objective Diabetic kidney disease (DKD) leads to low high albuminuria and gradually progresses to very high albuminuria with kidney insufficiency. However, about 20–40% of DKD is normoalbuminuric DKD (NADKD), which has impaired kidney function but normal urine albumin. This study is to investigate the urine metabolomic profiles of patients with NADKD and albuminuria DKD (ADKD). Methods In total, 95 patients were divided into a simple diabetes mellitus group (SDM group), an ADKD group, and a NADKD group. All subjects were analyzed for urine metabolites using non-targeted metabolomics based on ultra-performance liquid chromatography – tandem mass spectrometry. Results The urine metabolomic profiles of the SDM group, NADKD group, and ADKD group were significantly different, and 65 different metabolites were identified among the three groups. Metabolic pathway analysis of these differential metabolites found that the top three significantly changed metabolic pathways were linoleic acid metabolism, citrate cycle, and, arginine and proline metabolism. There are 12 metabolites enriched in these three metabolic pathways. In detail, compared with those in the SDM group, the levels of γ-linolenic acid in the ADKD group were increased significantly, while the levels of succinic acid, cis -aconitic acid, citric acid, L -proline, L -erythro-4-hydroxyglutamate, N -methylhydantoin, N -carbamoylputrescine, spermidine, and 5-aminopentanoic acid were reduced significantly; compared with those in the NADKD group, the levels of linoleic acid, γ-linolenic acid, and L -malic acid in the ADKD group were increased significantly ( P < 0.05), while the levels of L -proline, L -erythro-4-hydroxyglutamate, N -carbamoylputrescine, and spermidine were significantly reduced ( P < 0.05). However, there were no significant difference between the SDM group and NADKD group ( P > 0.05). Conclusion The urine metabolomic profiles between the NADKD group and the ADKD group are significantly different. Specifically, these two groups have distinct levels of linoleic acid, γ-linolenic acid, L -malic acid, L -proline, L -erythro-4-hydroxyglutamate, N -carbamoylputrescine, and spermidine.

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Prevalence of comorbidities in patients with type–2 diabetes mellitus

Cited by 25 publications

References 12 publications

The multifactorial burden of Type 2 Diabetes mellitus: a study from clinical settings of University Hospital, Karachi, Pakistan

The multifactorial burden of Type 2 Diabetes mellitus: a study from clinical settings of University Hospital, Karachi, Pakistan

Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus

Urine Metabolomics Analysis in Patients With Normoalbuminuric Diabetic Kidney Disease

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