Managing Type 2 Diabetes Mellitus (T2DM) poses a complex challenge, with patient outcomes often influenced by various factors such as treatment adherence, socio-economic status, body mass index (BMI), and health literacy.¹

Understanding these factors is crucial in enhancing patient care, minimizing complications, and optimizing overall well-being.² The World Health Organization emphasizes the importance of diabetes self- management training, with health literacy playing a pivotal role in patients' knowledge and awareness of their disease.³ Limited health literacy correlates with less successful disease management, emphasizing the significance of improving health literacy for enhanced self-care behaviors.⁴

    Factors influencing T2DM patient outcomes are multifaceted, ranging from treatment adherence and BMI to socio-economic status and health literacy. Recognizing and addressing these factors is imperative in tailoring effective interventions, improving patient outcomes, and reducing the burden of complications associated with T2DM.⁵ A comprehensive and patient-centered approach is essential for achieving optimal results in diabetes management.⁶

"Microvascular disease" (damage to small blood vessels) and "macrovascular disease" (damage to arteries) are the broad categories for complications in diabetes. Microvascular issues include retinopathy (eye disease), nephropathy (kidney disease), and neuropathy (neural damage).⁷ The major macrovascular complications include accelerated cardiovascular disease resulting in myocardial infarction and cerebrovascular disease manifesting as strokes.⁸

      Traditional complications associated with diabetes mellitus encompass a range of health issues, including stroke, coronary heart disease, heart failure, peripheral neuropathy, retinopathy, diabetic kidney disease, and peripheral vascular disease. Refer to Figure-1 for a visual representation of these complications.⁹ These diverse complications underscore the systemic impact of diabetes on various organs and highlight the need for holistic healthcare strategies to manage and mitigate these risks.¹⁰

       Diagnosing diabetes, and three methods are outlined. In the absence of unequivocal hyperglycemia, each diagnosis must be confirmed on a subsequent day using any of the three methods specified. However, it's noteworthy that the medical community does not currently recommend the use of hemoglobin A1c (A1C) for diabetes diagnosis. This underscores the importance of confirming diagnoses through established methods and highlights the ongoing considerations and developments in diagnostic approaches within the medical field.¹¹

A robust connection exists between T2DM and overweight or obesity, with estimates suggesting that around 90% of T2DM patients fall into these categories. Individuals with higher BMI often experience poorer glycemic control and an increased risk of complications, such as neuropathy, nephropathy, cardiovascular disease, and peripheral vascular disease.¹²

This is a prospective, case control and Randomized study with and without family history of type 2 diabetes was conducted in the Department of Biochemistry, Index Medical College.

Inclusion criteria

Age: Above 35 years.

Gender: Both male and female.

First degree relatives of Type 2 Diabetics.

Exclusion criteria:-Morbid obese,

Pregnant,

Lactating women,

Kyphosis,

Scoliosis,

Chronic alcoholism,

Smoking.

Number of groups – two

• Group 1 (n=112): Apparently healthy individuals.
• Group 2 (n=112): First degree relatives of type 2 diabetes.

      Documentation of basal and anthropometric parameters: The participants were told to empty their bladders before beginning the anthropomorphic assessment. A stadiometer in the upright position was used to measure height, and a weighing machine was used to assess weight. Weight (kg) divided by height (metre squared) will get the BMI.

       Body composition measurement: - A body fat analyser that operates on the basis of the bioelectrical impedance analysis (BIA) approach was used to measure the distribution of body fat. The resistance between the conductors will provide an estimate of body fat between a set of electrodes since skeletal, muscular, and adipose tissue have different electrical resistances. Muscle that is devoid of fat is an excellent conductor because it is high in water and electrolytes, whereas fat is anhydrous and does not conduct electricity well.

        Blood collection and storage: After a vein puncture, the blood was drawn, allowed to clot, and centrifuged at 3,000 RPM for 10 minutes at 4 °C (using a Remi refrigerated centrifuge). The serum will then be separated and frozen at -80 °C for analysis.

        Measuring Total Antioxidant Stress (TAOS): This involves evaluating the aggregate antioxidant capacities of all its components, like fats, proteins, vitamins, glutathione, uric acid, and so on. The test relies on the sample's ability to impede metmyoglobin's conversion of ABTS (2,2'-Azino-di-[3-ethylbenzthiazoline sulphonate]) to ABTS by means of antioxidants. The absorbance at 750 nm or 405 nm may be used to track the quantity of ABTS generated. Antioxidants in the sample block the absorbance at 750 nm or 405 nm at the reaction conditions utilised, to a degree proportional to their amount. Trolox, a water-soluble tocopherol analogue, is used to quantify the millimolar Trolox equivalents of the antioxidants in the sample and compare their efficacy to it.

        Measurement of malondialdehyde: Biological materials may be directly and quantitatively assessed for MDA using the Thiobarbituric Acid Reactive Substances (TBARS) Assay Kit. First, samples or standards containing unknown concentrations of MDA are treated with TBA at 95°C. It is possible to read the samples and standards using spectrophotometry or fluorometry [model no:-922] after a brief incubation. Unknown samples' MDA concentration is ascertained using.

Data Analysis:-

Statistical Analysis plan:- R 3.2.3 for Windows were used to carry out the statistical analysis. SPSS software version 25 were used. The mean ± SD was used to express the data. Kolmogorov-Smirnov test was used to determine normality. To study the association of sympathovagal imbalance with other parameters, analysis was used depending on the normality of the data.  

Table 1:  Distribution of Height (cm)

The mean height of controls without a family history of type 2 diabetes (168.92 cm) is slightly greater than that of cases with a family history (167.73 cm), but the difference is not statistically significant (p = 0.338). This means that any observed difference in height could be due to random variation rather than a true difference related to family history of type 2 diabetes.

In the second set, controls without a family history have a mean height of 169.79 cm compared to 163.72 cm for cases with a family history. The p-value here is 0.666, which also indicates that the difference is not statistically significant. This suggests that, similarly, the height difference observed could be attributed to chance rather than a meaningful effect.

Table 2:  Distribution of Weight (kg)

The mean weight for controls without a family history of type 2 diabetes is 60.28 kg, while for cases with a family history, it is 71.15 kg. The p-value is 0.035, which is below the commonly used significance threshold of 0.05. This suggests that the difference in weight between the two groups is statistically significant. The presence of a family history of type 2 diabetes is associated with a higher mean weight in this group.

For the second set of measurements, the mean weight for controls is 59.80 kg, and for cases with a family history, it is 66.44 kg. The p-value here is 0.300, which is above 0.05, indicating that the difference in weight is not statistically significant. This suggests that in this subgroup, the observed weight difference could be due to chance rather than a systematic effect related to family history.

Table 3:  Distribution of BMI (kg/m²)

The mean BMI for controls without a family history of type 2 diabetes is 22.99 kg/m², while for cases with a family history, it is significantly higher at 27.72 kg/m². The p-value is 0.004, which is below the common significance threshold of 0.05. This indicates a statistically significant difference in BMI between the two groups, suggesting that individuals with a family history of type 2 diabetes tend to have a higher BMI compared to those without such a family history.

In the second measurement, the mean BMI for controls is 22.55 kg/m², while for cases with a family history it is 27.44 kg/m². However, the p-value here is 0.845, which is well above the 0.05 threshold for statistical significance. This indicates that the difference in BMI between the two groups in this measurement is not statistically significant, meaning any observed difference could be due to random variation.

Table 4: Distribution of Body fat among the study population

Mean Body Fat: Controls have a mean body fat percentage of 26.15%, whereas cases have a higher mean of 28.95%. P value: The p-value is 0.215, which is above the standard significance level of 0.05. This indicates that the difference in body fat percentage between the two groups is not statistically significant. Therefore, in this measurement, there is no strong evidence to suggest that having a family history of type 2 diabetes is associated with a significantly different body fat percentage compared to controls.

Mean Body Fat: Controls have a mean body fat percentage of 26.38%, while cases have a mean of 26.55%. P value: The p-value is 0.004, which is well below the 0.05 threshold. This indicates a statistically significant difference in body fat percentage between the two groups in this measurement. The higher body fat percentage in cases with a family history of type 2 diabetes suggests that individuals with a family history may have a higher body fat percentage compared to those without such a history.

Table 5:  Visceral fat (%) distribution among the study population

Mean Visceral Fat: Controls have a mean visceral fat percentage of 8.48%, while cases with a family history have a higher mean of 9.51%. P value: The p-value is 0.000, which is well below the 0.05 threshold for statistical significance. This indicates a statistically significant difference in visceral fat percentage between the two groups. The higher mean visceral fat percentage in individuals with a family history of type 2 diabetes suggests a notable association between having a family history and higher visceral fat.

Mean Visceral Fat: Controls have a mean of 8.38%, while cases have a mean of 11.35%.

P value: The p-value is 0.003, which is also below the 0.05 threshold. This indicates a statistically significant difference in visceral fat percentage, with cases showing a higher percentage compared to controls. The significant p-value reinforces the association between having a family history of type 2 diabetes and increased visceral fat.

Table 6: Total antioxidant status markers levels among the study population

Mean TAOS: Controls have a mean TAOS level of 2.49 mM, while cases have a much lower mean of 0.75 mM. This suggests that cases with a family history of type 2 diabetes have lower antioxidant status compared to controls. P value: The p-value is 0.065, which is above the conventional significance threshold of 0.05 but close to it. This indicates a trend towards statistical significance. While the result is not traditionally considered significant, it suggests that the difference in TAOS levels may be meaningful and could warrant further investigation.

Mean TAOS: Controls have a mean TAOS of 2.44 mM, and cases have a mean of 0.50 mM. The difference in means is substantial, with cases showing a significantly lower antioxidant status. P value: The p-value is 0.002, which is well below the 0.05 threshold. This indicates a statistically significant difference in TAOS levels between the two groups, with cases having significantly lower antioxidant status compared to controls.

Table 7: Malondialdehyde markers levels based on the study population

Mean MDA: Controls have a mean MDA level of 8.18 mM, while cases with a family history have a mean of 13.09 mM. This substantial difference indicates that individuals with a family history of type 2 diabetes have much higher levels of MDA, suggesting increased oxidative stress. P value: The p-value is 0.000, which is significantly below the 0.05 threshold for statistical significance. This indicates a highly significant difference between the two groups, supporting that individuals with a family history of type 2 diabetes have elevated MDA levels compared to controls.

Mean MDA: Controls have a mean of 7.78 mM, while cases have a mean of 15.07 mM. The difference in means is even more pronounced in this subgroup. P value: The p-value is 0.000, again indicating a highly significant difference. This reinforces the finding that individuals with a family history of type 2 diabetes have markedly higher MDA levels, reflecting greater oxidative stress.

Mean Visceral Fat: Controls have a mean visceral fat percentage of 8.48%, while cases with a family history have a higher mean of 9.51%. P value: The p-value is 0.000, which is well below the 0.05 threshold for statistical significance. This indicates a statistically significant difference in visceral fat percentage between the two groups. The higher mean visceral fat percentage in individuals with a family history of type 2 diabetes suggests a notable association between having a family history and higher visceral fat.

        Mean Visceral Fat: Controls have a mean of 8.38%, while cases have a mean of 11.35%. P value: The p-value is 0.003, which is also below the 0.05 threshold. This indicates a statistically significant difference in visceral fat percentage, with cases showing a higher percentage compared to controls. The significant p-value reinforces the association between having a family history of type 2 diabetes and increased visceral fat.

      Similar research by Memon H et al. similarly revealed an increase in visceral fat in the case group.¹³ Diabetes patients also had higher levels of visceral fat, according to Michaelidou M et al. from Gwalior.¹⁴

      In contrast, our study assumes that those with a family history of type 2 diabetes have more visceral fat than people without such a history. Therefore, in these two animal models, specific intra-abdominal fat depots are important in controlling insulin action and glucose tolerance. One possible method by which these depots control the activity of insulin at remote locations. An further explanation is that elevated glycerol and FFA plasma levels hinder the effects of insulin in the liver and muscle. Indeed, it has been proposed that visceral fat is immune to insulin's antilipolytic actions, and that removing it

Mechanisms causing visceral fat to grow

The concentration of fasting blood and visceral fat in those with a family history of type 2 diabetes were shown to positively correlate in this research showed that the only part of the body's fat that positively linked with faster gluconeogenesis was the visceral fat region; glycogenolysis did not. This data aligns with the theory that hepatic insulin resistance is caused by enhanced gluconeogenesis and increased transport of FFA to the liver from an enlarged visceral adipose tissue mass.¹⁵

History of type 2 diabetes had significantly higher MDA levels (11.89 + 9.25 mM) than the controls (6.04 + 0.97 mM). The increase in peroxidative damage to lipids from oxidative stress acquired during diabetes may be the cause of the observed rise in malondialdehyde production. Several investigations using the Thio Barbituric acid reactive substances (TBARS) technique to estimate MDA corroborate the idea of increased oxidative stress in diabetes mellitus.¹⁶

       In addition, our research revealed that the battle of antioxidants against oxidative stress to reduce oxidative damage is reflected in the decline of antioxidants. MDA levels are within normal ranges when the overall antioxidant status is adequate and sufficient to counteract oxidative stress, and vice versa. The overall amount of endogenous and exogenous antioxidants is indicated by the total antioxidant status. Thus, it provides a comprehensive image of the antioxidant state. Since the many antioxidants in the system cooperate to prevent the oxidative damage brought on by free radicals, this is more significant than testing each antioxidant separately.¹⁷

Our intellectual curiosity in this prevalent disease will undoubtedly be satiated by the molecular genetic description of T2DM pathogenesis, but will it have an effect on healthcare? As of right present, our ability to anticipate T2DM is restricted. The principal strategy for treating this illness is still pharmacological intervention rather than prevention. Multiple modest genetic risk factors can be combined to predict risk in the Lyssenko and colleagues model, despite challenges to the model's genetic risk factor combination. A comprehensive knowledge of the interplay between genetic variations and their surroundings, lifestyle choices, and medical interventions is necessary to fully appreciate the efficacy of genetic techniques.

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