Introduction: Obesity and overweight are chronic conditions characterized by excessive adiposity that negatively impact health. They are among the most significant risk factors for respiratory failure, diabetes mellitus, cardiovascular disease, and hypertension. Genetic factors may contribute to some of these conditions, but they are mainly associated with the lifestyle in the majority. However, neither genetics nor lifestyle can fully explain all cases. Recent studies have proven that microRNA-21 causes the systemic hypertension, many cardiac pathologies, and some cancers. MicroRNA-21 is viewed as an important future biomarker for many critical conditions. We explored the role of microRNA-21 in the causation of overweight and obesity. There is conflicting data about this association in the literature. Determining if there is an association may help us in better understanding and managing this condition.

Aim: To determine if there is a link between microRNA-21 expression and body mass index (BMI) in an Indian adult population. We also compared the lipid profiles (total cholesterol, HDL, LDL, and triglycerides) of participants (grouped by their BMI) to get a better understanding.

Patient and methods: The study was conducted in Pondicherry, India and had 50 participants, with 30 as controls having normal BMI, and 20 categorized as overweight or obese as per BMI.

Results: The microRNA-21 levels in circulation were analyzed using Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR). The expression of microRNA-21 was statistically higher in the overweight/obese cohort compared to the normal BMI individuals (p=0.034). A significant difference was also noted in the total cholesterol levels, with the overweight/obese group having higher values than normal BMI group (p=0.004).

Conclusion: This is the first study of its kind in the Indian population to establish that individuals classified as overweight or obese exhibit higher expression of microRNA-21 and elevated total cholesterol levels compared to those with a normal BMI.

BMI, circulating microRNA, lipid profile, overweight, obesity, qRT-PCR

In the last few decades, there has been a worrying increase in obesity rates worldwide. This affects people irrespective of age, socioeconomic status, and societal background, in both the developed Western world and developing nations. According to the World Health Organization (WHO), there are around 3 billion people (including 427 million children, adolescents, and teenagers) who are overweight globally, with 890 million of them falling into the category of obesity, thereby putting them at increased susceptibility for various health issues such as diabetes, hepatic pathology, hypertension, and even mental health disorders.^[1] The Indian National Family Health Survey (NFHS-5; 2019-2021) places the scope of overweight or obese individuals (BMI >25 kg/m²) in India as 22.9% for men and 24% for women, with greater disease burden in urban regions compared to rural areas (29.8% vs. 19.3% for men, and 33.2% vs. 19.7% for women).^[2] Since 1990, the global prevalence of obesity in adults has increased by over 100%, and adolescent obesity has seen a fourfold increase.^[1] Obesity is also a key factor in the severity of infectious diseases such as influenza and COVID-19.^[3] Given the widespread nature of this problem, it is important to focus on personalized approaches to prevent and treat obesity. Obesity has major economic consequences, as it burdens healthcare systems and economies with higher healthcare costs and lower productivity levels.

Obesity has many underlying causes, including psychosocial, environmental, and heritable traits. In today’s world, sedentary lifestyle and the consumption of highly processed, high-calorie foods are becoming more and more common. The situation has been worsened by increasing urbanization and ‘westernization’, which have increased access to inexpensive, high-calorie, low-nutrient food options.^[4] These elements, along with roles in the growth of a sedentary lifestyle and decreased physical activity, lead to obesity and weight gain. Genetic obesity can be grouped into the following categories: mono-, polygenic, and syndromic.^[5] Each type has specific genetic markers that can lead to obesity, impacting various bodily functions. Furthermore, the development of obesity is influenced by epigenetic factors, which include mechanisms such as microRNAs and DNA methylation that control cellular processes and gene expression.^{[6, 7]}

To diagnose overweight or obesity, we use the person’s weight (in kilograms), and height (in meters) to determine the body mass index (BMI), which is computed as weight divided by height squared. BMI serves as an indicator, with the addition of measurements like waist circumference aiding in identifying obesity. For infants, children, and adolescents, the categories used to define obesity based on BMI may vary depending on age and gender. The WHO currently suggests specific ranges for BMI classification: 18.5–24.9 kg/m² for healthy weight, 25.0–29.9 kg/m² for overweight, and greater than 30 kg/m² for obese individuals.^[1] These BMI recommendations may not be appropriate for all populations, particularly for ethnic groups like Indians, as they are mainly based on research with Western populations.^{[8, 9]} Compared to people of other ethnicities, research suggests that people of Indian descent may be more prone to developing insulin resistance and cardiovascular diseases at lower BMI levels.^[10] A consensus group has proposed BMI cut-off values specific to the Indian population as follows: healthy BMI range: 18.0–22.9 kg/m², overweight for values between 23.0–24.9 kg/m², and obesity was categorized as greater than 25 kg/m².^[11]

Elevated BMI levels have been linked to an increased risk of cardio-metabolic issues.^[12] However, BMI alone does not differentiate between lean and fat tissues, leading to the observation that many overweight (~50%) and obese (~30%) individuals do not display any metabolic changes or health issues.^[13] Therefore, it is important to find biomarkers for identifying obesity and its associated metabolic disorders.

Small non-coding RNA (ncRNA) molecules – known as microRNAs – composed of just 19-22 base pairs regulate gene expression, ultimately affecting cell processes like proliferation, differentiation, and apoptosis. By preventing messenger RNA (mRNA) translation and breaking down mRNA molecules, these microRNAs can silence target genes, and occasionally even have a positive impact on gene expression and translation.^{[14, 15]} MicroRNAs are crucial in biological functions, and changes in microRNA expression have been associated with diverse diseases apart from obesity, including cardiovascular diseases, hypertension, cancers, and neurological disorders.^[16–19] In addition to being linked to BMI and metabolic health, circulating microRNAs also target crucial genes associated with immunometabolism by influencing the expression of cytokines and adipokines.^[20] Notably, people with metabolic diseases such as obesity have different circulating microRNA profiles than people in good health. It is still unclear, nevertheless, how much microRNAs regulate human posttranscription. Human obesity is associated with an overexpression of microRNA-21, a crucial microRNA that is frequently increased in chronic disorders and that modulates transforming growth factor beta (TGF-β) signaling to promote adipogenic differentiation.^[21]

Previous studies have shown a varying outcome when it comes to the expression of microRNA-21 and BMI. Ghorbani et al reported that obesity is linked to reduced microRNA-21 levels.^[22] Yang et al., Mendez-Mancilla et at., and Guglielmi et al., however, concluded that microRNA-21 expression is increased in obese individuals.^[23–25]

In our current research we aimed to determine if there is any link between the microRNA-21 expression and BMI in an Indian adult population. We also compared the lipid profiles (total cholesterol, high density lipoprotein -HDL, low density lipoprotein -LDL, and triglyceride levels) of the participants to get a better understanding.

Study population

A total of 57 adult participants were enrolled from the in-patients of Vinayaka Mission’s Medical College and Hospital (VMMC) in Puducherry, India. Written informed consent was obtained from all the participants and ethics approval was secured from the institutional ethical committee of VMMC. Individuals over 18 years of age, and without any acute or chronic inflammation, cancer, or endocrine issues including diabetes were enrolled in the study. Seven individuals were excluded due to high white blood cell counts/sepsis. Fifty persons were included in the study finally and informed written consent was obtained from all of them after explaining the details of the study to them in their own language. Information on demographics, medical history, and various clinical measurements like age, gender, comorbidities, family medical history, BMI, and fasting lipid profile were collected. BMI was computed by dividing weight (in kg) by the height squared (in meters). Normal, overweight, and obesity were categorized based on the Indian Consensus Group criteria, dividing individuals into overweight/obesity (BMI 23-24.9 and >25 kg/m² respectively) and normal range (BMI 18.0–22.9 kg/m²). Accurate and calibrated tools were used for anthropometric measurements. The tests for the lipid profile (triglycerides, total cholesterol, HDL, LDL – all reported in milligrams/deciliter or mg/dL) were done using fasting samples and conducted at the VMMC Clinical Laboratory using standard methods.

The results obtained in our study are summarized in Table 1.

Circulating microRNA-21 expression

Through the use of qRT-PCR, we reviewed the relative levels of circulating microRNAs among individuals classified as normal BMI and overweight or obese. The study demonstrated an increase in circulating microRNA-21 levels in the overweight/obese category (4.354±6.315 relative units) compared to the normal BMI population (1.956±2.606 relative units) and the difference was noteworthy with a p-value of 0.034 (Fig. 1).

Figure 1.

Comparison of circulating microRNA-21 levels between individuals with normal BMI and those classified as overweight or obese. The figure illustrates the difference in microRNA-21 levels, highlighting higher expression in the overweight/obese group relative to the normal BMI group.

Lipid profile comparison

The overweight/obese group has higher mean values for LDL (111.38±36.296 vs. 96.916±29.692; p=0.098), total cholesterol (181.6±37.839 vs. 157.7±23.186; p=0.008), and triglycerides (164.81±60.279 vs. 152.64±39.216; p=0.238), but lower mean HDL (39.4±8.665 vs. 43.636±6.938; p=0.067) compared to the normal range BMI group. The data has been represented graphically, showing LDL, HDL, TG in Fig. 2 and HDL in Fig. 3, respectively. Only the difference in the value of total cholesterol reached statistical significance.

Figure 2.

Comparison of LDL (Low-Density Lipoprotein), HDL (High-Density Lipoprotein), and triglyceride (TG) levels between individuals in the normal BMI group and those in the overweight/obese group. All values are shown in mg/dL, highlighting variations in lipid profiles associated with each BMI category.

Figure 3.

Comparison of total cholesterol (CHOL) values between individuals with normal BMI and those in the overweight/obese group. Total cholesterol levels are shown in mg/dL, illustrating differences between the two BMI categories.

Obesity is a well-known global health issue that stems from an inequity in the assimilation and disbursement of energy. This not only leads to the excessive accumulation of fat tissue but also triggers various metabolic dysfunctions like diabetes, hypertension, and hyperlipidemia.

In our research, we utilized BMI with the Indian cut-off values as a criterion for assessing the metabolic health status of the study participants, along with including blood parameters related to renal and lipid metabolism. The primary aim of our study was to evaluate for the potential of microRNA-21 as a biomarker for obesity. The findings of our study revealed that the circulating serum microRNA-21 expression was significantly higher in overweight and obese individuals in comparison to those with normal BMI. Possible roles of microRNA-21 in the development of obesity could be by modulating the balance of lipid storage in the body – promoting adipogenesis, and reducing lipolysis.^{[26, 27]}

Chartoumpekis et al. reported an increase in microRNA-342-3p, microRNA-142-3p, microRNA-142-5p, microRNA-21, microRNA-146a, microRNA-146b, microRNA-379, and a decrease in microRNA-122, microRNA-133b, microRNA-1, microRNA-30a, microRNA-192, and microRNA-203 levels as obesity developed in mice.^[28] A rise in microRNA-21 levels was discovered in the white adipose tissue of obese mice when compared to those with a leaner body mass, showing a positive correlation with BMI.^[29] Lopez and colleagues reported elevated levels of microRNA-21 in participants with obesity in comparison to normal range BMI individuals.^[30] Furthermore, Yang et al. showed a link between microRNA-21 and hip and waist circumference, waist-hip ratio, and BMI.^[23] However, Mendez-Mancilla et al. found that microRNA-21 and microRNA-29 had a weak association with BMI.^[24] Conversely, Ghorbani et al. reported that there is a decreased serum microRNA-21 level in obesity (in diabetic and non-diabetic subjects).^[22] Studies by Ortega and Murri indicated a noticeable decrease in microRNA-21 levels in obese individuals, with negative correlations observed between microRNA-21 levels and parameters like waist circumference, BMI, fasting blood sugar, and glycated hemoglobin.^{[31, 32]}

Faheem et al. stated that in the general population, BMI has a positive correlation with random blood sugar levels and total cholesterol.^[33] Zhu et al. found that BMI had a risk impact on altered lipid profiles (odds ratio of 1.37).^[34] Lu et al. demonstrated that all lipid parameters were significantly connected to BMI even after extensive adjustment for potential factors. Notably, HDL exhibited a negative association with BMI.^[35] Another large study from China showed that total cholesterol, TG, and LDL were positively linked to BMI of lactating women, but inversely related to HDL.^[36] Reuter et al. reported that this association was even present in children who were overweight or obese and had a higher chance of having altered lipid profiles compared to underweight or normal-weight children.^[37]

The reason behind the differences noted in various studies remains unclear. One potential explanation for this inconsistency could be the variations in where the samples were obtained from (plasma versus serum) or the differences in the groups of people studied. MicroRNAs in body fluids are transported by a variety of transporters, including high- and low-density lipoproteins, apoptotic blebs, and extracellular vesicles (exosomes, microparticles, and apoptotic blebs). They can also be found in a form that is not encased in a vesicle but is linked to RNA-binding proteins. Hence, further research utilizing more advanced technologies is necessary to distinguish between unprotected and membrane-enclosed microRNA.

Our findings show that there is a significant difference in the levels of microRNA-21 in the serum between individuals classified as overweight/obese by BMI as compared to those who had a healthy or normal BMI in the Indian population. Also, there is a significant variation in the total cholesterol levels of people with overweight/obese BMI as compared to normal BMI. However, the same association was not statistically significant for LDL, HDL, and TG.

The primary limitations of our study include the limited number of participants and the lack of follow-up. While analyzing circulating microRNA-21 against BMI has significance, it is crucial to validate its findings through larger multicenter studies to better understand its prognostic, diagnostic, and therapeutic significance in clinical environments.

I would like to express my gratitude to Dr. Suganya Ph.D., Assistant Professor – Research, central research laboratory, VMMC for assisting me through the microRNA extraction process.

The authors of this study have no competing / conflict of interests. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.