Introduction
Obesity is one of the largest public health challenges of this century worldwide [1, 2]. According to report of World Health Organization in 2016, the number of adults living with overweight reached 1.9 billion, which corresponds to 39% of the global adult population, of whom 650 million exhibited obesities [1]. Another important health problem is an increase in the number of older people in the world [3, 4]. There is evidence that ageing, and obesity are interrelated and may influence each other [5, 6]. An increase in body weight can trigger the processes of accelerated ageing and thus contribute to the progression of metabolic and age-associated diseases [7].
Despite the large number of new developments in both dietary therapy and drug treatment, the number of obese patients, according to the National Task Force on Treatment of Obesity, is increasing in all age groups [8]. It’s mainly related to lifestyle, which is characterized by an extremely high-calorie diet and a lack of motor activity. Today it is especially important, so that obesity is a significant risk factor for cardiovascular disease and type 2 diabetes [9, 10]. According to WHO data, overweight and obesity contribute to the development of up to 44–57% of cases of type 2 diabetes, 17–23% of cases of coronary heart disease, 17% of hypertension, 30% of gallstone disease, 14% of osteoarthritis, and 11% of malignant tumours [1]. These diseases have a clear association with age. With accelerated ageing, there is an early decline in adaptive mechanisms of all physiological systems of the body, there is a significant decrease in physical and mental activity, which contributes to the early development of age-related pathology.
Accelerated ageing is determined by biological age (BA) [11, 12]. One may discuss accelerated ageing if the biological age is ahead of the chronological age (ChA). BA is the correspondence of an individual morphofunctional level to some average statistical norm of a given population, which reflects the unevenness of development, maturity and ageing of various physiological systems and the rate of age-related changes in the adaptation of the body’s capabilities. The reasons for different rates of ageing can be genetic and epigenetic factors, as well as environmental factors. It is not possible to accurately determine the beginning of old age based on biological characteristics, since individuals with the same calendar age are not always the same age biologically [13, 14]. Thus, BA serves as an assessment of the individual age status of the organism.
Telomere length is considered one of the markers of biological ageing [15-17]. Telomeres are structures located at the ends of chromosomes and progressively decrease with each cell division. Telomere length plays a key role in preserving genomic stability. Telomere length is a balance between processes that shorten telomeres during cell division with incomplete DNA replication, and processes that lengthen telomeres under the action of telomerase, an RNA-protein complex that adds telomeric repeats to the ends of DNA molecules. Ageing, inflammation, and oxidative stress accelerate the process of telomere shortening [18, 19]. Telomerase counteracts this process, maintaining and lengthening the length of telomeres. Telomerase activity and telomere length play a decisive role in cellular aging and in the pathobiology of several human diseases. Recent studies have shown that the telomere length can be changed under the influence of genetic and epigenetic factors, sex hormones, active forms of oxygen, and inflammatory reactions. Telomere length is often shorter in obese patients. In people with obesity, excess adipose tissue plays a key role in the development of a chronic and systemic inflammatory condition, which can cause a shortening of telomere length. There are emerging data indicating a bidirectional relationship between telomere length and obesity. Obese patients show signs of increased inflammation, oxidative stress, and premature ageing [20–22].
Since multiple attempts to develop generally accepted standards for measuring BA have not been successful, recently epigenetic age has attracted the most attention as the one that most accurately reflects the ageing process [23]. Its measurement is based on changes in the DNA methylation profile that occur throughout life at specific locations along the genome. Although a person’s epigenetic age correlates with their ChA, there are some exceptions. For example, the epigenetic age of long-lived people is significantly younger than their life expectancy [24]. Diet, lifestyle, and environmental exposures can induce epigenetic changes throughout a person’s lifetime, influencing health and disease occurrence. For example, epigenetic processes are disrupted in such diseases as cancer and Alzheimer’s disease [23]. This is extremely important, since scientific evidence indicates that epigenetic changes can affect not only the life of a given person, but also be transmitted from generation to generation [25].
DNA methylation creates a biological record of various molecular processes involved in the development, maintenance and decline of an individual, contributing to age-related diseases [23]. S. Horvath, analysing the data of 13 long-term studies on the study of the DNA methylation profile, found that an increase in epigenetic age relative to ChA increases the risk of mortality, regardless of other known risk factors. It was shown that the ≈5% of people who aged significantly faster than others (the epigenetic age was more than 10 years higher than the ChA) had an almost 50% increase in the risk of death. Such informational value of epigenetic age in predicting life span has raised the question of establishing biomarkers that can help to establish accelerated ageing in time without the use of complex molecular genetic methods. Further research in this direction led to the establishment of a lot of markers that are likely to be correlated with DNA methylation. Thus, several ageing-related epigenetic clocks have recently been proposed to predict BA [25]. Epigenetic clocks may provide a means to establish differential rates of ageing [25–27].
The aim of this study was to establish the relationship between metabolic disorders, overweight and obesity with markers of accelerated ageing BA age in patients with hypertension.
Material and methods
Characteristics of study participants
The study population consisted of 116 patients (the age 35–65 years, women 62.3%) with stage 1–2 grade 1–2 hypertension and low/moderate cardiovascular risk (CVR). The diagnosis and treatment of hypertension was made on the basis of 2020 International Society of Hypertension Global Hypertension Practice Guidelines. All patients received standard antihypertensive therapy [angiotensin-convertase enzyme inhibitor (ACE-I) or angiotensin receptor blocker (ARB), dihydropyridine-calcium channel blocker, thiazide-like diuretic). Twenty healthy volunteers (hereinafter — controls) aged 35–65 years, were also included in the study. All patients and volunteers signed the informed consent to participate in the study. The research protocol was approved at a meeting of the Ethics Commission of the L.T. Mala NIT NAMSU. Physical examination of patients included measurements of height, body weight, waist circumference (WC) and hip circumference (HC), body mass index (BMI) calculation and waist-to-hip ratio (WC/HC) calculation. Body composition was determined by the bioelectrical impedance method using Composition Monitor BF511, Omron; body fat percentage (FAT, %), skeletal muscle percentage (MUS, %), visceral fat level (VIS, %). Office BP was measured in accordance with 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Complete blood test was done in all patients. Lipid metabolism was assessed by total cholesterol (TC), triglycerides, low density lipoprotein cholesterol (LDL-C), very low density lipoprotein cholesterol (VLDL-C) and high density lipoprotein cholesterol (HDL-C). Carbohydrate metabolism was assessed by measuring fasting blood glucose (FPG), glycated haemoglobin (HbA1c) and insulin levels. Insulin resistance (IR) was determined by the homeostasis model assessment of insulin resistance (HOMA-IR) calculation. The state of the prooxidant system was evaluated by the levels of total hydroperoxide (THP) content, and the state of antioxidant protection system — by total antioxidant activity (TAA), THP to TAA ratio (THP/TAA) calculation. Determination of global DNA methylation (GML) by the percentage content of 5-methylcytosine (5-mc) in the DNA of blood mononuclear cells was carried out by the immunoenzymatic method using the set of reagents “5-mc DNA ELISA Kit” (Zymo Research Corp., United States). The sensitivity of the method is 0.5% 5 mC/100 ng of DNA.
Telomere length was determined by real-time PCR. Amplification was performed using SsoAdvanced Universal SYRB Green Supermix (BioRad Laboratories, USA) and primer system (Thermo Fisher Scientific). The relative length of telomeres (RTL-b) was determined by the T/S ratio (T — the number of copies of telomeric repeats, S — the number of copies of the 36B4 gene), followed by normalization of the obtained data to the reference sample, which was included in each experiment. Telomerase activity was determined by real-time PCR using the TRAPeze Kit RT Telomerase Detection Kit (Millipore, United States).
Epigenetic age was calculated using the DNAm PhenoAge epigenetic clock based on nine biological markers (albumin, g/L; creatinine, mg/dL; glucose mg/dL; C-reactive protein (CRP) mg/L, lymphocytes, %; red cell dist width (RCDW), %; mean cell volume (MCV); alkaline phosphatase U/L; leukocytes, x109; age, years).
Continuous variables obtained from the participants in this study were expressed as means ± standard deviations (SDs). Categoric variables were expressed as percentages. Data were compared using Student’s t-test (for parametric continuous variables) and the Mann-Whitney U test (for non-parametric continuous variables) and chi-square test with Yates’s correction (for categoric variables). To assess the interrelationship of indicators, a correlation analysis was performed with the calculation of pairwise correlation coefficients. Also, the associations between variables were assessed using linear multiple regression models. When estimating regression equations, the method of stepwise inclusion of predictors was used, which ranks features in accordance with their contribution to the model. P values < 0.05 were considered statistically significant. All analyses were conducted via the statistical package Statistica V10.0.
Results
A comparative analysis showed that our patients with hypertension are accompanied by significant changes in the main metabolic parameters compared with the control group (Tab. 1). This fact confirmed that even the initial stages of hypertension and low CVR are accompanied by metabolic disorders.
|
Indicators |
Main group (n = 116) |
Control group (n = 20) |
M-W p-level |
|
M ± s |
M ± s |
||
|
Age [years] |
47.96 ± 11.86 |
48.61 ± 6.08 |
0.840 |
|
Weight [kg] |
79.96 ± 14.86 |
67.11 ± 5.00 |
0.002 |
|
BMI [kg/m2] |
27.71 ± 4.54 |
22.71 ± 1.23 |
0.0001 |
|
WC [sm] |
90.67 ± 12.72 |
84.64 ± 5.49 |
0.083 |
|
HC [sm] |
103.02 ± 9.86 |
96.54 ± 3.70 |
0.016 |
|
WC/HC |
0.88 ± 0.13 |
0.88 ± 0.07 |
0.903 |
|
HR [b.p.m.] |
69.78 ± 9.05 |
65.45 ± 4.82 |
0.0387 |
|
SBP [mm Hg] |
125.71 ± 17.46 |
116.52 ± 9.87 |
0.0235 |
|
DBP [mm Hg] |
79.26 ± 10.85 |
72.18 ± 6.47 |
0.0053 |
|
HbA1c (%) |
5.67 ± 0.55 |
5.08 ± 0.25 |
0.0002 |
|
FPG [mmol/L] |
5.57 ± 1.28 |
5.05 ± 0.31 |
0.1284 |
|
Insulin [mMU/L] |
21.57 ± 13.90 |
9.69 ± 1.19 |
0.0018 |
|
HOMA-IR |
5.39 ± 4.04 |
2.18 ± 0.30 |
0.0036 |
|
TC [mmol/L] |
5.61 ± 1.15 |
4.54 ± 0.35 |
0.0007 |
|
TG [mmol/L] |
1.48 ± 0.79 |
0.84 ± 0.15 |
0.0033 |
|
VLDL-C [mmol/L] |
0.68 ± 0.39 |
0.42 ± 0.11 |
0.0130 |
|
HDL-C [mmol/L] |
1.36 ± 0.32 |
1.37 ± 0.17 |
0.9621 |
|
LDL-C [mmol/L] |
3.7 ± 1.03 |
2.79 ± 0.45 |
0.0054 |
|
Uric acid [µmol/L] |
271.52 ± 78.34 |
247.36 ± 45.78 |
0.2605 |
|
TAA [µmol trolox equivalent] |
603.69 ± 25.58 |
538.09 ± 156.75 |
0.1234 |
|
THP [µmol/L] |
80.14 ± 11.39 |
131.37 ± 51.96 |
0.0004 |
|
THP/TAA [un] |
0.13 ± 0.02 |
0.29 ± 0.23 |
0.0112 |
To clarify the influence of age characteristics, a separate analysis of indicators was carried out between the main and control groups in the age ranges up to 45 years and 45–65 years. Differences between the hypertensive and the control group in each age range were the same as for the group as a whole. We had only one exception: fasting glucose level (FPG) in the younger group (up to 45 years), in contrast to older patients, were the same in patients of both control and hypertensive groups, which may indicate an age-dependent progression of carbohydrate disorders.
Deviation of anthropometric characteristics from control values, and signs of impaired carbohydrate and lipid metabolism were observed in patients with hypertension in both age groups and their degree increased with age.
Analysis of weight characteristics by BMI revealed that among patients with hypertension (n = 116), 34 patients (27.59%) were obese, 50 patients (43.1%) were overweight, and only 32 patients (29.31 %) had normal weight. All patients in the control group were of normal weight (BMI less than 25).
The analysis of weight characteristics was carried out in patients of younger and older age groups separately (Fig. 1, Tab. 2,).
|
Indicators |
Up to 45 year (n = 46) |
45–65 year (n = 70) |
p-value |
|
Normal weight [kg] |
21.39 ± 1.32 |
22.83 ± 1.69 |
0.012 |
|
Overweight [kg] |
27.52 ± 1.44 |
27.70 ± 1.40 |
0.674 |
|
Obesity [kg] |
32.75 ± 2.48 |
32.87 ± 3.77 |
0.845 |
Comparison of age groups revealed that with increasing age, there is a decrease in the number of patients with normal weight and an increase in the number of overweight and obese patients.
There were no significant age differences of BMI in patients with overweight and obesity. At the same time, the BMI of patients with normal weight in the younger group was significantly lower compared to the older group.
To assess the impact of obesity, we conducted a comparative analysis of metabolic parameters depending on weight status (Tab. 3).
|
Indicators |
Normal weight (n = 32) |
Overweight (n = 50) |
Obesity (n = 34) |
P1–2 |
P1–3 |
P2–3 |
|
1 |
2 |
3 |
||||
|
BMI [kg/m2] |
22.15 ± 1.68 |
27.56 ± 1.41 |
32.84 ± 3.44 |
0.1802 |
0.0000 |
0.0000 |
|
FAT (%) |
26.76 ± 6.58 |
29.92 ± 7.92 |
37.24 ± 8.58 |
0.0843 |
0.0000 |
0.0002 |
|
MUS (%) |
30.84 ± 4.48 |
30.37 ± 6.73 |
27.16 ± 5.17 |
0.3546 |
0.0045 |
0.0289 |
|
VIS (%) |
6.41 ± 1.76 |
8.47 ± 2.61 |
12.13 ± 11.16 |
0.0000 |
0.0085 |
0.0330 |
|
WC [sm] |
77.33 ± 7.00 |
90.26 ± 8.52 |
104.09 ± 8.12 |
0.0000 |
0.0000 |
0.0000 |
|
HC [sm] |
94.47 ± 5.77 |
103.18 ± 5.89 |
110.79 ± 11.69 |
0.0000 |
0.0000 |
0.0002 |
|
WC/HC |
0.82 ± 0.07 |
0.87 ± 0.07 |
0.96 ± 0.20 |
0.0012 |
0.0004 |
0.0097 |
|
HR [b.p.m.] |
72.36 ± 10.65 |
67.80 ± 6.96 |
71.24 ± 10.25 |
0.0442 |
0.6835 |
0.0859 |
|
SBP [mm Hg] |
119.56 ± 16.18 |
125.92 ± 13.01 |
132.62 ± 16.78 |
0.0206 |
0.0040 |
0.0411 |
|
DBP [mm Hg] |
74.36 ± 9.51 |
80.76 ± 11.46 |
84.38 ± 11.91 |
0.0147 |
0.0010 |
0.1619 |
|
HbA1c (%) |
)5.44 ± 0.60 |
5.71 ± 0.60 |
5.80 ± 0.40 |
0.1199 |
0.0233 |
0.5518 |
|
FPG [mmol/L] |
5.31 ± 0.80 |
5.64 ± 1.73 |
5.72 ± 0.82 |
0.1601 |
0.0418 |
0.8032 |
|
Insulin [mMU/L] |
16.37 ± 13.31 |
20.80 ± 12.01 |
27.63 ± 15.62 |
0.0284 |
0.0036 |
0.0306 |
|
HOMA-IR |
3.93 ± 3.52 |
5.25 ± 3.98 |
7.08 ± 4.27 |
0.0123 |
0.0029 |
0.0594 |
|
TC [mmol/L] |
5.41 ± 1.26 |
5.70 ± 1.08 |
5.69 ± 1.13 |
0.2154 |
0.3443 |
0.9087 |
|
TG [mmol/L] |
1.08 ± 0.65 |
1.58 ± 0.77 |
1.69 ± 0.86 |
0.0010 |
0.0022 |
0.6506 |
|
VLDL-C [mmol/L] |
0.49 ± 0.29 |
0.72 ± 0.34 |
0.82 ± 0.48 |
0.0011 |
0.0013 |
0.2775 |
|
HDL-C [mmol/L] |
1.43 ± 0.35 |
1.35 ± 0.33 |
1.34 ± 0.28 |
0.2402 |
0.2547 |
0.9134 |
|
LDL-C [mmol/L] |
3.49 ± 1.11 |
3.65 ± 1.02 |
3.54 ± 0.93 |
0.4310 |
0.8492 |
0.5777 |
|
Uric acid [µmol/L] |
239.25 ± 85.28 |
278.73 ± 70.08 |
287.76 ± 75.45 |
0.0622 |
0.0170 |
0.6231 |
|
TAA [µmol trolox equivalent] |
508.16 ± 187.11 |
571.05 ± 144.18 |
501.60 ± 136.50 |
0.2358 |
0.9025 |
0.1006 |
|
THP [µmol/L] |
108.00 ± 52.25 |
133.53 ± 41.78 |
153.94 ± 62.39 |
0.0032 |
0.0168 |
0.1661 |
|
THP/TAA [un] |
0.31 ± 0.32 |
0.26 ± 0.12 |
0.37 ± 0.28 |
0.1453 |
0.5462 |
0.0501 |
The measurement of anthropometric parameters revealed the expected increase in WC, HC, WC/HC, Vis% in patients with overweight and obesity, not only in comparison with a patient with normal weight but also among themselves (p < 0.05). BMI and FAT% in obese patients were significantly higher than in normal weight and overweight patients, while there was no difference between normal and overweight patients. Skeletal muscle percentage (MUS, %) was the same in normal weight and overweight groups but was decreased in obese patients compared to these groups.
The measurement of the office BP showed better control of SBP and DBP in normal weight patients (p < 0.05).
In the analysis of carbohydrate metabolism in the obese group, compared with the normal weight group, a significant increase in all indicators was found: HbA1c, FPG, Insulin, HOMA-IR; in the overweight group, only Insulin and HOMA-IR differed significantly. Uric acid in patients of all groups was within the normal range, however, in patients with obesity, uric acid was significantly higher than in patients with normal weight. Patients with overweight and obesity also showed an increase in THP as an indicator of oxidative stress compared with patients of normal weight.
Evaluation of lipid metabolism indicators revealed an increase in triglycerides and a decrease in HDL in the overweight and obese groups compared with the normal weight group. It should be noted that these indicators were obtained in the absence of any lipid-lowering therapy at the initial examination at the moment of inclusion in the study. In the future, patients were prescribed lipid-lowering therapy in accordance with the 2019 European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS) Guidelines for the management of dyslipidaemias, and its results will be evaluated in further studies.
Thus, pathological weight gain associated to the progression of metabolic disorders. Patients with the same degree of hypertension and the same cardiovascular risk (CVR) had different degrees of metabolic disorders. Normal weight patients had minimal changes compared to obese patients.
To assess the rate of ageing measured by telomere length (RTL-b), telomerase activity (TA), global DNA methylation level (GML) and their association with weight status were analysed (Tab. 4). We didn’t find differences in RTL-b in any groups. At the same time overweight and obese patients had higher GML and also TA was lower in obese patients (p < 0.05).
In order to determine association between ageing rates and weight, biological age was calculated using the DNAm PhenoAge epigenetic clock. After calculating the epigenetic age, the rates of ageing were determined for every object. If the patient’s epigenetic age exceeded the chronological age by more than a year, the patient was classified as accelerated ageing, if less than or equal the patient was assigned to the group of healthy ageing. Analyse of association between ageing rate and weight is presented in the Figures 2, 3.
|
Indicators |
Normal weight (n = 32) |
Overweight (n = 50) |
Obesity (n = 34) |
P1–2 |
P1–3 |
P2–3 |
|
1 |
2 |
3 |
||||
|
Age, years |
43.00 ± 11.55 |
46.67 ± 10.96 |
51.52 ± 8.30 |
0.0000 |
0.0010 |
0.0105 |
|
Phenoage, years |
36.35 ± 13.25 |
42.59 ± 11.64 |
48.43 ± 9.62 |
0.0345 |
0.7827 |
0.0110 |
|
RTL-b, relative units |
0.93 ± 0.33 |
0.93 ± 0.30 |
0.89 ± 0.19 |
0.8232 |
0.4217 |
0.7404 |
|
Telomerase activity |
0.97 ± 0.55 |
0.88 ± 0.51 |
0.83 ± 0.47 |
0.5388 |
0.0001 |
0.6836 |
|
GML |
2.77 ± 1.63 |
4.17 ± 1.93 |
4.74 ± 1.67 |
0.0401 |
0.0053 |
0.4250 |
The comparative analysis to determine the factors influencing the rate of ageing was carried out between the accelerated and healthy ageing groups (Tab. 5). The accelerated ageing group had higher levels of Vis%, TG, VLDL-C, uric acid, and all parameters of carbohydrate metabolism (HbA1c, FPG, Insulin, HOMA IR) and lower level of HDL-C.
|
Indicators |
Accelerated ageing |
Healthy ageing |
p-value |
|
BMI [kg/m2] |
29.21 ± 5.01 |
27.38 ± 4.39 |
0.101 |
|
WC [sm] |
96.43 ± 11.92 |
89.40 ± 12.59 |
0.428 |
|
HC [sm] |
104.75 ± 10.38 |
102.65 ± 9.75 |
0.521 |
|
WC/HС |
0.92 ± 0.07 |
0.87 ± 0.14 |
0.260 |
|
Vis (%) |
9.57 ± 3.99 |
8.94 ± 7.00 |
0.034 |
|
Fat (%) |
30.25 ± 8.42 |
31.77 ± 8.81 |
0.432 |
|
Mus (%) |
29.98 ± 5.93 |
29.15 ± 6.12 |
0.105 |
|
HbA1c (%) |
5.91 ± 0.51 |
5.62 ± 0.55 |
0.039 |
|
FPG [mmol/L] |
6.58 ± 2.48 |
5.35 ± 0.65 |
0.000 |
|
Insulin [mMU/L] |
27.38 ± 15.95 |
20.21 ± 13.11 |
0.037 |
|
HOMA-IR |
7.66 ± 5.57 |
4.88 ± 3.45 |
0.004 |
|
TC [mmol/L] |
5.51 ± 1.35 |
5.4 ± 1.10 |
0.560 |
|
TG [mmol/L] |
1.77 ± 0.78 |
1.41 ± 0.78 |
0.020 |
|
VLDL-C [mmol/L] |
0.86 ± 0.49 |
0.65 ± 0.36 |
0.023 |
|
HDL-C [mmol/L] |
1.19 ± 0.24 |
1.40 ± 0.33 |
0.002 |
|
LDL-C [mmol/L] |
3.54 ± 1.21 |
3.58 ± 0.99 |
0.754 |
|
Uric acid [µmol/L] |
328.82 ± 80.89 |
258.92 ± 72.27 |
0.001 |
|
TAA [µmol tr. equival.] |
0.36 ± 0.26 |
0.28 ± 0.22 |
0.322 |
|
THP [µmol/L] |
156.23 ± 68.28 |
124.44 ± 44.70 |
0.109 |
|
THP/TAA |
496.55 ± 141.33 |
549.67 ± 159.95 |
0.206 |
Discussion
The relationship between obesity and ageing has attracted the attention of researchers for last decades. Metabolic dysregulation, oxidative stress, inflammation and immune dysfunction underlie not only obesity but also ageing and appear to be able to influence each other. It is known that metabolic disorders, including hyperglycaemia, insulin resistance (IR) and hyperinsulinemia, dyslipidaemia, induce interconnected processes in the vessel wall and contribute to increased oxidative stress, apoptosis, and vascular permeability, contributing to cardiovascular ageing and increasing the risk of cardiovascular disease (CVD) and other age-related disease [28–31].
Importantly, advanced age is typically accompanied by increased fat deposition around the internal organs (visceral adipose tissue), in which adipocyte precursor cells (preadipocytes) tend to lose their specialized characteristics, undergo dedifferentiation, and acquire a senescent-like pro-inflammatory state; this results in increased production of proinflammatory cytokines and chemokines and induction of visceral adipose tissue inflammageing [32].
Given the etiological similarities between obesity and ageing, it is valid to speculate that obesity might accelerate the rate of ageing and the onset of age-related diseases [33]. Accordingly, obese mice have a shorter lifespan and increased levels of oxidative stress markers compared to their lean counterparts [34]. Likewise, extreme obesity is associated with substantially elevated rates of total mortality, with most of the excess deaths due to diseases typically associated with old age, including CVD [35]. Recently trial showed that obese individuals might lose up to 8 years of life compared to normal-weight individuals [36]. Remarkably, in rats, maternal obesity was found to accelerate the metabolic ageing of the offspring in a sex-dependent manner [37].
The similarities between obesity and age-related fat tissue dysfunction are also evidenced by the fact that multiple energy metabolism and energy-sensing pathways, as well as regulators of oxidative stress and inflammation, play similar roles in both obesity and ageing and their associated pathologies. These pathways represent potential druggable targets for both obesity and ageing [5].
It is known that the visceral type of obesity and the accumulation of senescent cells with an inflammatory phenotype lead to a high level of pro-inflammatory cytokines in the blood, which can interfere with the transmission of insulin signals. IR is often implicated as a cause of adverse ageing phenotypes and age-related conditions. It can even be assumed that counteracting IR can be an effective “anti-ageing” intervention.
In our study, differences in telomere length depending on the presence of excess weight and obesity were not reliably confirmed. Perhaps this is due to the choice of the object of study (patients with low/moderate CVR). At the same time, an increase in telomerase activity in patients with obesity and a significant increase in GML in groups of overweight and obese patients may indicate an earlier start of ageing processes in these patients compared to patients with normal weight.
New therapeutic possibilities have emerged to influence the underlying pathological mechanisms of obesity. The similarity of the pathological processes between obesity and ageing: in cellularity, insulin sensitivity, secretory profiles, and the inflammatory state of adipose tissue [38, 39], allows us to hope for the success of similar interventions to eliminate dysfunctions associated with obesity and age-related pathology.
Conclusion
The results of our study showed that pathological weight gain associated to the progression of metabolic disorders and accelerated ageing. Patients with the same degree of hypertension and CVR had different degrees of metabolic disorders depending on body weight. The group of patients with normal weight not only had fewer metabolic changes compared to patients with obesity, but accelerated ageing was also less frequently observed among them.
Accelerated ageing can contribute to the rapid progression of obesity and other age-related pathologies. Assessment of the relationship between accelerated ageing and obesity may be a determining tool for influencing health improvement and reducing morbidity and mortality rates of the population. Since many of the effects of ageing can be delayed or attenuated by lifestyle changes or currently available medications, research in this direction holds promise for improved risk prediction of various diseases associated with accelerated ageing of the population.
Acknowledgments
None.
Author’s contributions
O.K.: Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
O.Z.: Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
T.B.: Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
A.P.: the acquisition of data for the work; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
All authors read and approved the final manuscript.
Funding
None.
Availability of data and materials
Data and materials used and/or analysed during the current study are available from the corresponding author on reasonable request.
Ethics approval and consent to participate
This study was conducted in accordance with the amended Declaration of Helsinki. The institutional review board approved the study, and all participants provided written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.