Diagnosing Type 1 Diabetes in the Eighth Decade of Life
Indhumathi Kuberan1
1 Indu’s Diabetes Clinics, Mumbai, Maharashtra, India
Introduction/Background: Older adults with Type 1 diabetes are a heterogenous group and have not been well studied. An increased proportion of individuals with Type 1 diabetes are living into the later decades of life. There are very few cases mentioned in literature, diagnosed as Type 1 DM in eighth decade of life or later. In this abstract, I am going to present a case of Type 1 diabetes newly diagnosed in a 78-year-old.
Aims & Objectives: Recognition of Type 1 diabetes in geriatric patients is important because if misclassified as Type 2 diabetes, it may lead to inappropriate management and occurrence of morbidity. It is important to make a prompt diagnosis to start basal-bolus insulin therapy.
Material & Methods: A 78-year-old female, with no previous history of diabetes was admitted with urinary tract infection and ketoacidosis. After stabilization, the patient was discharged on premix insulin and oral anti-diabetic medication. The patient was progressively losing weight; glycemic excursions were severe, and patient was readmitted in a month with ketoacidosis. The patient was investigated thoroughly. She had GAD 65 antibody positive and undetectable C PEPTIDE levels. She was initiated on basal-bolus insulin therapy. The challenges we faced in this patient were complete denial by patient and her family initially, reluctance for MDI (multiple daily injection insulin regimen, titration of insulin doses based on SMBG (self-monitoring of blood glucose), prevention of hypoglycemia.
Results: With the help of a collaborative and integrated team approach, patient has gained weight, maintains an HBA1C of 8% on basal-bolus insulin therapy. Other factors such as polypharmacy, frailty, adequate nutrition, and cognition were all taken into consideration.
Conclusions: Type 1 DM as differential diagnosis in newly diagnosed diabetic ketoacidosis in an older adult, though rare, should always be borne in mind.
Risk of Co-morbidities, Poor Sleep Quality and Mental Health in Overweight and Obese Post-menopausal Women with Diabetes from South India
Satyavani Kumpatla1, Divyabharathi Samraj1 and Vijay Viswanathan1
1 M.V. Hospital for Diabetes and Prof. M. Viswanathan Diabetes Research Centre (IDF Centre for Excellence in Diabetes care), Royapuram, Chennai, Tamil Nadu, India
Introduction/Background: Women undergo metabolic changes during the transition to menopause. Diabetes combined with menopausal status may significantly reduce QoL and cause anxiety and depression.
Aims & Objectives: To compare the risk of co-morbidities, sleep quality and mental health in overweight and obese post- versus pre-menopausal women with diabetes.
Material & Methods: 388 women aged 27–70 years were screened in a tertiary care center for diabetes in Chennai, and divided into Group 1 pre- and Group 2 post-menopause (absence of menstruation for 12 previous consecutive months). A questionnaire was used to collect clinical, biochemical, co-morbidities, and menstruation details between January and October 2023. Pittsburgh Sleep Quality Index was used to assess the sleeping pattern, WHO Quality of Life, Global Anxiety and Depression scale was administered.
Results: 68.3% of women were in post-menopausal status during the screening. Overweight and obesity were similar in both groups (13 vs. 18.9), (87% vs. 81.1%). Group 2 had a longer duration of diabetes. Those on combination therapy of OHA+insulin were significantly higher in Group 2. (42.3% vs. 35.8%, P = .015). Comorbid conditions HTN and dyslipidemia were significantly higher in Group 2. (58.1 vs. 38.2 and 71.3% vs. 57.7%, P < .001). Presence of microvascular complications was also higher in Group 2. Higher proportion of them had poor glycemic control (HbA1C >8%) in Group 1 (68.3% vs. 56.6%, P = .009). Median duration of breastfeeding was significantly higher in Group 2 (1.5 vs. 1 year, P = .039). Poor sleep quality was highly prevalent in Group 2 (56.1% vs. 32%, P < .001). Anxiety and depression assessed based on scores showed higher anxiety and depression in Group 2 (39.6% vs. 25.2%, P = .006). Overall, QoL score was poor in Group 2 (81 vs. 91, P < .001).
Conclusion: The study highlights the risk of co-morbidities, and microvascular complications among overweight and obese post-menopausal women with diabetes compared to pre-menopausal women. Poor sleep quality, anxiety, depression and overall poor QoL were observed in post-menopausal women. Poor glycemic control was higher in pre-menopausal women. Screening post-menopausal women with diabetes for sleep disturbances, depression and anxiety and appropriate intervention may improve their overall QoL.
Unveiling Hidden Diabetes: Investigating Prevalence and Prediction After Acute Stroke in a Tertiary Care Setting
G Sai Sanjeev Kumar1 and Somesh Chougule1
1 JJ Hospital Mumbai, Maharashtra, India
Introduction/Background: Stroke is the most common cause of physical disability. Diabetes increases the risk of ischemic stroke by two to four-fold. The prevalence of recognized diabetes in acute stroke patients is between 8%–20% and 6%–42% of patients may have undiagnosed diabetes before presentation with stroke.
Aims & Objectives: To study the prevalence of unrecognized diabetes and impaired glucose tolerance following acute stroke and to measure the accuracy of hyperglycemia and HBA1C in predicting the presence of unrecognized diabetes at 3 weeks post-stroke.
Material & Methods: This is a prospective observational study conducted on acute stroke patients who were not known diabetics admitted to JJ Hospital between December 2022 and December 2023. A detailed history and examination were done. The stroke was confirmed by imaging. RBS, FBS, PPBS, and HbA1C levels were done. Treatment for stroke along with appropriate treatment for glycemic control was initiated. At 3 weeks post stroke followed up in OPD with OGTT and HBA1C.
Results: 33.3% of patients have prediabetes and 23.3% are diabetic based on OGTT. Two hours post-OGTT and HbA1C at admission were found to be good predictors of diabetes and prediabetes after acute stroke.
Conclusions: Investigating acute stroke patients by fasting, 2h post-OGTT and HbA1C levels helps in detecting a significant number of previously unrecognized diabetes and prediabetes. Early detection and treatment reduce other cardiovascular events.
Incidence of Delay in Treatment-seeking Behavior Among T2D Patients—A Clinic-based Cross-sectional Descriptive Study in India
Shubhashree Patil1, Bharat Saboo2, Priti Sanghavi3, Tanuja Shah4, Sybal Dbritto5, Aashna Patil6, Seema A. Bagri7, Rohini S. Gajare8, Aparna Gangurde Muley9, Ayaz Ansari10, Mrinalini Singh11, Charusheela Kolhe12, Kinnary R. Shah13, Sanjyoti Khot14, Pranjali Shah15, Hardik Bambhania16, Rukiya Shaikh17 and Seema Jashnani18
1 Diabetes and Wellness Clinic, Andheri East, Mumbai, Maharashtra, India
2 Prayas Diabetes Center, Indore, Madhya Pradesh, India
3 Sanghavi’s Diabetes Clinic, Chandivali, Mumbai, Maharashtra, India
4 Asha Healthcare, Kandivali West, Mumbai, Maharashtra, India
5 Dr. Sybal’s Diabetes Care Clinic, Nalasopara West, Mumbai, Maharashtra, India
6 Dr Aashna’s Diabetes Care, Mira Road (East), Mumbai, Maharashtra, India
7 Dr Bagri’s Diabetes Care Centre, Thane, Mumbai, Maharashtra, India
8 Dr Rohinis Diabetes and Thyroid Clinic, Vikhroli (E), Mumbai, Maharashtra, India
9 Apple Hospital, Airoli, Mumbai, Maharashtra, India
10 First Care Hospital, Bhiwandi, Mumbai, Maharashtra, India
11 Hira Mongi Navneet Hospital, Bhandup, Mulund, Mumbai, Maharashtra, India
12 Disha Diabetes Care Clinic, Virar (W), Mumbai, Maharashtra, India
13 Shree Diabetes and Eye Clinic Goregaon, Kandivali, Mumbai, Maharashtra, India
14 Parulekar Hospital, Airoli, Navi Mumbai, Maharashtra, India
15 Apt Diabetes Clinic, Mulund, Mumbai, Maharashtra, India
16 HR’s Clinic, Borivili West, Mumbai, Maharashtra, India
17 Blue Circle Clinic, Mulund, Mumbai, Maharashtra, India
18 OM Clinic, Goregaon, Mumbai, Maharashtra, India
Introduction: Delay in T2D treatment hastens progression, increases the risk of complications, worsens symptoms, and makes it difficult for patients to achieve glycemic control. However, there is no Indian data available to indicate the gap between patients’ diagnosis and the start of treatment and its impact on glycemic control.
Aims & Objectives: This cross-sectional descriptive study aimed to assess the incidence of delayed treatment-seeking behavior among treatment-naive patients with Type 2 Diabetes Mellitus (T2DM) in India.
Material & Methods: This multi-centric, prospective cross-sectional real-world study was conducted across 20 sites in Mumbai, a metro in western India. Drug-naive T2DM patients who visited study sites for routine consultation were enrolled after providing study participation consent. Data from September 7th, 2023 to February 29th, 2024, were analyzed.
Results: In this study involving 487 enrolled patients (average age 46.0 [11.9] years, 55% male), the mean HbA1C was 9.5% (2.3). Notably, 47% reported delaying treatment by over 3 months after diagnosis. The time gap between the start of treatment and diagnosis was as follows: within a month - 26%, 1–2 months - 27%, 3–6 months - 20%, and >6 months - 27%. Among patients who delayed treatment, 58% tried alternative treatments, and 32% relied on lifestyle/diet changes only. Among the alternative treatments tried, 61% attempted Ayurveda, 10% used homemade remedies, 24% tried Homeopathy and 5% Unani.
Conclusions: The study highlights a substantial delay in seeking treatment among newly diagnosed T2DM patients in a metro (Mumbai) in India, where access to specialists is good. These delays lead to poorer initial glycemic control. This study emphasizes the importance of enhanced population education to start early intervention in the management of T2DM to prevent complications and improve long-term outcomes.
A Study of Plasma Fibrinogen Level and a PTT in Relation to Glycaemic Control in T2 DM Patient at a Tertiary Care Teaching Hospital, Jaipur
Dheeraj Garg1, Sunil Yadav1 and Parul Garg1
1 JNU Hospital, Jaipur, Rajasthan, India
Introduction: Diabetes contributes to the initiation and progression of microvascular and macrovascular complications. Shortened activated partial thromboplastin time (aPTT) values may reflect a hypercoagulable state, which is associated with increased thrombotic risk and adverse cardiovascular events. Increased levels of fibrinogen are common in Type II diabetes.
Aim and Objective: To evaluate the coagulation profile (activated partial thromboplastin time, prothrombin time and fibrinogen) in Type 2 diabetes in order to assess the impairment of the coagulation cascade and to see its association with glycemic control
Material and Method: Hospital-based observational study on 100 diabetic subjects divided into two categories, that is, good glycemic and poor glycemic control groups was conducted at Medicine Department JNU Hospital Jaipur HBA1C ≤7 was considered as having Good Glycemic Control group and HBA1C >7 was considered as having Poor Glycemic Control group. The following tests were used for making results: (a) Chi-square test, (b) Student t-test, and (c) one-way ANOVA.
Results: In our study, the poorly glycemic control group had significantly higher fibrinogen levels than good controlled glycemic group. The median fibrinogen was 2.9 g/L in good glycemic control, whereas 5.5 g/L in poor glycemic control group. We analyze our data on the basis of APTT values, which were significantly lower in the poor glycemic control group as compared to the good controlled glycemic group.
Conclusion: Clinical tests for APTT and fibrinogen are inexpensive and easily available tests for detecting coagulation abnormality. The patients with poorly controlled diabetes and having a longer duration of diabetes frequently have an altered coagulation profile so treating the hypercoagulable state may help individuals with diabetes mellitus avoid developing micro and macrovascular problems.
Comparative Study of Clinical Profile of Type 1 Versus Type 2 Diabetes Mellitus Patients Requiring Hospitalization in a Tertiary Care Hospital in a Metropolitan City in India
Harshada H. Rane1, Vidya Nagar1 and Vijay Joglekar1
1 Department of General Medicine, Grant Government Medical College, Mumbai, Maharashtra, India
Introduction/Background: Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. It is classified into Type 1 (T1DM) and Type 2 (T2DM), each with distinct etiologies and clinical presentations. Hospitalizations due to diabetes-related complications impose a significant burden on healthcare systems. Understanding the differences in the clinical profiles of T1DM and T2DM patients requiring hospitalization is crucial for optimizing management strategies and resource allocation in tertiary care settings. This study aims to compare the clinical characteristics, co-morbidities, complications, and treatment modalities of hospitalized T1DM and T2DM patients.
Aims & Objectives
Aim: To study the clinical profile and indication for hospitalization of patients with Type 1 and Type 2 diabetes.
Objectives:
Primary
To study the clinical profile of Type 1 and 2 diabetes cases admitted at a tertiary care hospital.
To study the indications for hospitalization of Type 1 and 2 diabetes cases.
Secondary
To study the outcome in these hospitalized cases in terms of:
Duration of hospital stay.
Need for ICU admissions.
Complications.
Mortality.
Material & Methods
Study Design: Hospital-Based Prospective Study.
Study Setting: Tertiary care hospital in a metropolitan city in India.
Study Duration: September 2023 to February 2024
Study Sample Size: 400.
Study Participants: Hospitalized patients diagnosed with T1DM or T2DM.
Data Collection: Retrieval of medical records including demographic details, clinical parameters, co-morbidities, complications, laboratory investigations, and treatment received.
Statistical Analysis: Descriptive statistics, chi-square test, independent t-test.
Results: The present study was conducted in a tertiary care hospital in which 400 participants were included in the study Type 2 DM patients were 72 and Type 2 diabetes mellites patients were 328.
Conclusions: In this comparative discussion, we analyze the findings from two separate studies, one conducted by Shikdar et al.1 and another conducted by our team. Both studies aim to investigate the clinical profiles of patients with Type 1 (T1DM) and Type 2 (T2DM) diabetes mellitus requiring hospitalization. Here, we compare and contrast the results from both studies to draw comprehensive insights into the clinical characteristics, co-morbidities, complications, treatment modalities, and outcomes of hospitalized T1DM and T2DM patients.
Our study, similar to the study by Shikdar et al., found a significantly higher prevalence of hypertension and dyslipidemia among T2DM patients compared to T1DM patients. Interestingly, our study observed a higher prevalence of chronic kidney disease (CKD) among T2DM patients, indicating a potential association between T2DM and renal complications, which aligns with findings from the study by Shikdar et al. In our study, T1DM patients showed a higher incidence of acute complications such as diabetic ketoacidosis (DKA) and heart failure, whereas T2DM patients were more prone to infections and hypoglycemia, which is consistent with findings from the study by Shikdar et al. Both studies highlight the importance of tailored management strategies to address the specific acute complications associated with each diabetes subtype. Our study found a higher utilization of insulin therapy among T1DM patients, whereas T2DM patients were more likely to receive oral hypoglycemic agents and other medications, similar to findings from the study by et al.
Regarding outcomes, both studies indicate comparable rates of in-hospital mortality between T1DM and T2DM patients. Our study showed differences in the length of hospital stay, with T1DM patients having a longer duration, whereas T2DM patients had a shorter stay, which contrasts with findings from the study by Shikdar et al. Laboratory findings in our study revealed variations in parameters such as blood glucose levels, HbA1C, creatinine, and lipid profiles between T1DM and T2DM patients, which is consistent with findings from the study by Shikdar et al.
In conclusion, both studies, including our own, provide valuable insights into the clinical profiles and outcomes of hospitalized T1DM and T2DM patients. While there are some differences in findings between the two studies, such as the length of hospital stay, several consistent trends emerge regarding co-morbidities, complications, treatment modalities, and outcomes. These findings underscore the importance of personalized approaches to diabetes management and highlight the need for further research to elucidate the complex interactions between diabetes subtypes and hospitalization outcomes.
Reference
1. Shikdar, Y. A., Mosli, H. H., Shikdar, N. A., Alshanketi, R. M., Shikdar N. A., Malebary, R. M., Aboznadah, W. M., Shikdar, M. A. (2022, May 25). Diabetes mellitus and related admission factors among hospitalized patients in King Abdul-Aziz University Hospital in Jeddah, Saudi Arabia. Cureus, 14(5), e25312. https://doi.org/10.7759/cureus.25312
Incidence of LV Diastolic Dysfunction in People with T2DM Having a Normal Renal Function in Comparison with Impaired Renal Function
Sanjay Sud1
1 Konnagar Municipal Hospital, Hooghly West Bengal, India
Aims & Objectives: The aim of this study was to compare the incidence of LV diastolic dysfunction in people with T2DM having a normal renal function with those having impaired renal function.
Material & Methods: Adult people (non-pregnant) diagnosed with T2DM (as per the ADA criteria) were enrolled with proper informed consent and protocol approved by the hospital ethical committee. Those having T2DM for <2 years were excluded from the study. Their renal status was assessed by calculating the eGFR by CKD-EPI (2021). Those with eGFR ≥ 60 mL/min/1.73 m2, were included in the normal renal parameter group. Those with eGFR ≤ 59 mL/min/1.73 m2 were included in the group of impaired renal function. This group was further divided as per CKD stages. Transthoracic echocardiography was used to assess the diastolic function. 228 (120 males and 108 females) people with T2DM were included in the study. 76 (44 males and 32 females) had impaired renal function.
Results: It was found that ~ 72% of those with a normal renal function had Grade I LV diastolic dysfunction, whereas ~ 92% of those with an impaired renal function had Grade I LV diastolic dysfunction.
Conclusions: This study proved that there was a significantly higher prevalence of LV diastolic dysfunction in people with T2DM having an impaired renal function, hence assessment of LVDD by Echocardiography in all people with T2DM with and without renal compromise should routinely be done at the earliest opportunity so that preventive management to initiate reduction/progression of LV dysfunction can be done to reduce the associated mortality and morbidity in PWD.
Prediction of Spectrum of Fatty Liver Disease and Liver Fibrosis in Primary Care Setting
C. Muralidharan1 and C. Balaji2
1 Vijaya Clinic Diabetes Care Centre, Dindigul Diabetic Education And Research Centre, Dindigul, Tamil Nadu, India
2 RSSDI Tamilnadu Chapter, Meenakshi Hospital, Kumbakonam, Tamil Nadu, India
Introduction:
NAFLD, as the name implies, is a condition with more than 5% steatosis in hepatocytes with causes other than alcohol and is often unknown.
NAFLD affects nearly 25%–30% of adults in the general population up to 70% of patients with Type 2 DM and all patients with obesity.
Majority of people have one form of NAFLD, with only a few developing another.
NAFLD can be caused by oxidative stress, inflammatory adipose tissue, WBC infiltration, cytokine generation and gut bacteria-induced inflammation.
NAFLD patients may experience complications like.
A. Hepatic complications: NAFLD is associated with an increased risk of progressing to NASH, Cirrhosis and Liver Cancer Cirrhosis can cause liver failure, necessitating a liver transplant.
B. Other complications: Individuals with NAFLD are much more likely to suffer from the following complications or consequences
Type 2 DM
Chronic Kidney Disease (CKD)
Metabolic Syndrome with components of hypertension, dyslipidemia, insulin resistance and diabetes.
High blood pressure and abnormal levels of fats – Cholesterol and Triglycerides – in the blood
Objective: To predict, the spectrum of fatty liver disease and liver fibrosis in a primary care setting using Fibroscan.
Materials and Methods: People with diabetes mellitus, people with hypertension/overweight/obesity and people with no history of comorbid condition were all included in this study to predict the spectrum of fatty liver disease and liver fibrosis—using Fibroscan – Echosens (liver elastography).
Study Place: In ward No: 30, Dindigul Municipal Corporation.
Sample Size: 107 patients of which 48 men and 59 women were enrolled in this study.
Methodology: Participants selected for the study were subjected to Fatty Liver/Liver Fibrosis screening using Fibroscan Echosens and results were analyzed.
Result:
Conclusion:
Screening should be done in asymptomatic but clinically severe fibrosis may be detected through screening in high-risk individuals, such as those with T2DM, obesity with metabolic complications, a family history of cirrhosis, or significant alcohol use.
It is recommended to use APRI and FIB-4 as the initial screening tools for the assessment of hepatic fibrosis in the primary care setting.
It is recommended that an LSM cut-off ≥ 8.2 kPa and ≥ 13.6 kPa may be used for detecting the presence of significant fibrosis and cirrhosis, respectively, in clinical practice.
USG (abdomen) or controlled attenuation parameter (CAP) on transient elastography should be used as the non-invasive tests for the detection of hepatic steatosis.
Primary care physicians play a vital role in the early detection and management of fatty liver disease.
NAFLD is often first detected in primary care or metabolic clinics, where clinicians must decide which patients need additional care.
Since it is mostly asymptomatic, the challenges in detection and treatment are even greater.
The appropriate timing of intervention depends on fibrosis risk staging, primary care screening, and consistent, timely, evidence-based, broadly accessible management strategies.
Healthy diet and weight loss are the cornerstone of management for NAFLD patients. Physical inactivity has been demonstrated to worsen the severity of liver disease.
An Alpha-test of Diabetology.co.in—An Algorithm Driven Personalized & Precision Medicine Prescription System for Treatment-naive Patients with Type 2 Diabetes
Om J. Lakhani1, Arvind Gupta2, Priti Tripathi1 and Chaitasey Mehta3
1 Department of Endocrinology, Zydus Hospital, Ahmedabad, Gujarat, India
2 Department of Internal Medicine and Diabetes, Rajasthan Hospital, Jaipur, Rajasthan, India
3 Head of Medical Services, Zydus Hospital, Ahmedabad, Gujarat, India
Introduction: This study evaluates Diabetology.co.in, an innovative Algorithm Driven Prescription System developed for personalized and precision treatment of treatment-naive patients with Type 2 Diabetes. It focuses on integrating computational medicine with clinical practice, leveraging artificial intelligence for optimized diabetes management.
Aims & Objectives: The primary objective of this study was to alpha-test the diabetes medication treatment generated by the application and its algorithm. The results produced were then compared to the medications prescribed by author 1 to the same patient.
The secondary aim was to examine the profiles of newly diagnosed Type 2 Diabetes patients visiting the endocrinology outpatient clinic.
Materials and Methods: A retrospective pilot study was conducted at a tertiary multispeciality hospital, assessing Diabetology.co.in’s alpha version. Data from the last 50 adult patients with Type 2 Diabetes from the outpatient Endocrinology OPD were analyzed. These patients were treatment-naive, excluding pregnant women and those with positive insulin antibodies or glucocorticoid use. Data, including clinical and laboratory parameters, were manually input into the system, which then generated evidence-based prescription recommendations using its algorithmic processing.
Results: The system processed data from 50 patients, with an average age of 41.9 years and a 40% female demographic. The application effectively utilized inputs like Body Mass Index, Glomerular Filtration Rate, and HbA1C levels to generate prescriptions. Metformin was universally recommended, with insulin prescribed for half of the patients, and SGLT2 inhibitors for 30%. The software’s suggestions showed a significant match with actual clinical prescriptions, indicating its accuracy and potential in aiding clinical decision-making. Notably, the software identified an overprescription tendency in clinician practices and provided insights into patient profiles through advanced data analysis capabilities, such as correlations between triglyceride levels and BMI.
Conclusion: Diabetology.co.in demonstrated high efficacy in generating precise and personalized treatment recommendations for newly diagnosed Type 2 diabetes patients. It aligns closely with actual clinical prescriptions, showcasing its potential to reduce overprescription and contribute to evidence-based diabetes care.
To Evaluate the Relationship of Risk Factor Between Diabetes Mellitus and Thyroid Dysfunction (Thyrobetes) in JNU
Madhumati Varma1
1 Jaipur National University Medical College and Hospital, Jaipur, Rajasthan, India
Introduction: Type 2 diabetes mellitus (T2DM) and thyroid dysfunction are prevalent endocrine disorders with documented mutual influence. These conditions collectively represent a significant burden on global health, with diabetes alone affecting over half a billion individuals. Notably, autoimmune hypothyroidism is associated with Type 1 diabetes, while subclinical hypothyroidism is linked to Type 2 Diabetes. Understanding the interplay between these disorders is crucial to discerning their sequential development and the factors influencing the transition from diabetes to thyroid dysfunction and vice versa.
Aims & Objectives: This study aims to elucidate the factors underlying the relationship between diabetes and thyroid dysfunction, assessing thyroid hormone levels (TSH, free T3, free T4), lipid profiles, fasting and postprandial blood glucose, HbA1C, insulin levels, anti-thyroid peroxidase (TPO) antibodies, and neck ultrasonography in select thyroid patients. The objectives include identifying correlations between these factors and the development of diabetes and thyroid diseases, as well as proposing preventive measures for these conditions.
Materials and Methods: Conducted at the research unit of JNU Hospital, Jaipur, this study included 771 participants with diabetes, thyroid dysfunction, or both. Data collection involved patient questionnaires, physical examinations, and laboratory analyses, including thyroid function tests and metabolic profiles. Patients with acute illnesses affecting thyroid function, those taking medications influencing thyroid hormones, or those with fever were excluded from the study. Data analysis employed Excel and SPSS software, utilizing various statistical methods.
Results: Among the 771 patients, 45% had T2DM, 1.95% were prediabetic, 52.7% had hypothyroidism, and 5.2% had hyperthyroidism. Notably, 58 patients exhibited both diabetes and thyroid dysfunction. Correlation analysis revealed significant associations between hypothyroidism and postprandial glucose levels, as well as, between fasting and postprandial glucose levels and cholesterol and triglyceride levels.
Conclusions: Individuals with diabetes are more susceptible to developing prediabetes from hypothyroidism, while T2DM is more likely to precede hypothyroidism. Both hypothyroidism and T2DM are associated with a high prevalence of dyslipidemia, emphasizing the importance of effective management strategies for these conditions. Routine monitoring of glucose and TSH levels in individuals with diabetes or thyroid disorders is recommended to facilitate early intervention and improve outcomes.
Prevalence of Microvascular and Macrovascular Complications in Geriatric Patients with Type 2 DM in Primary Care Setting
Indhumathi Kuberan1
1 Indu’s Diabetes Clinics, Mumbai, Maharashtra, India
Introduction/Background: When literature was reviewed it was found that prevalence studies of microvascular and macrovascular complications of Type 2 Diabetes have been done extensively in the general population. There are very few studies that have been conducted on the elderly. Literature also showed that only a few studies have been done on the co-morbidities associated with Type 2 diabetes in the elderly.
Aims & Objectives: This study aims to screen and analyze Type 2 diabetic patients above the age of 60 years for microvascular and macrovascular complications and related co-morbidities.
Material & Methods: A prospective study with a cross-sectional design was conducted at my private diabetes clinics as well as, Apex Super Speciality Hospital at Borivali Mumbai. Patients were screened for retinopathy, Neuropathy, Nephropathy (Microvascular), Cardiovascular disease, Cerebrovascular disease, and Peripheral vascular disease (Macrovascular). Information was also gathered on comorbid conditions such as hypertension, Hypothyroidism, Obstructive Sleep Apnea, Arthritis, Chronic Obstructive Pulmonary Disease, and Congestive Cardiac failure.
Results: In terms of individual macrovascular complications, the prevalence of CVD was 24.5%, CEVD was 2%, and PVD was 0.5%. In terms of microvascular complications, the prevalence of neuropathy was 31% and nephropathy was 11% and retinopathy was 4%. Number of complications is significantly and positively co-related with a duration of diabetes. In terms of co-morbidities, hypertension was prevalent in 54.5%, anemia in 25%, arthritis in 17.5%, hypothyroidism in 14%, OSA & CCF in 3.5% each, COPD in 0.5%. More than one comorbidity was observed in 58.5% of the subjects.
Conclusions: A high prevalence of diabetes complications in the elderly was found. Among all complications, Neuropathy was the most prevalent complication followed by CVD and Nephropathy. Among macrovascular, CVD was the most prevalent and among microvascular, Neuropathy was the most prevalent complication. Duration of diabetes co-related significantly with a number of complications experienced. Hypertension was the most prevalent comorbidity associated with Type 2 diabetes in the elderly followed by anemia and arthritis.
