Correlation of Metabolic Age with Chronological Age and Physical Examination Score from BIA in Male and Female Patients with Type 2 Diabetes Mellitus

doi:10.21203/rs.3.rs-5057249/v1

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Correlation of Metabolic Age with Chronological Age and Physical Examination Score from BIA in Male and Female Patients with Type 2 Diabetes Mellitus

https://doi.org/10.21203/rs.3.rs-5057249/v1

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Background:Metabolic age is an important risk marker that can be easily understood by patients in clinical practice. In this study, we aimed to investigate the association of metabolic age with chronological age and physical examination score in male and female patients with type 2 diabetes mellitus.

Material and Method:Male (n:50) and female patients (n:72) with T2DM were included in the study. Anthropometric measurements were obtained with Tanita MC-780MA device, waist/hip and waist/height ratios were calculated, variables were compared between groups and correlation with met age was analyzed. Linear regression analysis was used to determine the factors affecting met age.

Results:The difference between met-age and chronological age was significantly higher in female patients with T2DM (age difference: 6.0∓3.79 in female group, 3.38∓2.80 in male group) (p<0.001). BMI values, degree of obesity and fat mass were significantly higher in women (p<0.001), waist/hip ratios were lower (p<0.001) and waist/height ratios were higher (p=0.001). Physical examination score, muscle mass, lean mass and BMR were higher in men than in women (p<0.001).There was a significant positive association of met age with chronological age and a significant negative association with physical examination score in both sexes (p<0.05). According to the linear regression model, it was found that met age was positively affected by chronological age and waist/height ratio and negatively affected by physical examination score in the female group (R2 =0.983).In the male group, it was observed that it was positively affected by chronological age, obesity degree, body mass index (BMI) and negatively affected by physical examination score (R2 =0.974).

Conclusions:Met age increases with increasing chronological age and decreasing physical examination score in male and female patients with T2DM.

Internal Medicine

Physiology

Endocrinology & Metabolism

Nursing

T2DM

met-age

physical examination score

chronological age

Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disease characterized by hyperglycemia due to insulin secretion deficiency or resistance and impaired carbohydrate, lipid and protein metabolism [1]. Due to the decrease in physical activity (PA) and increase in obesity, type 2 diabetes mellitus (T2DM) is on the rise worldwide [2].Exercise training and PAs are indispensable practices in the prevention and treatment of T2DM that regulate glycemic regulation and improve aerobic capacity, muscle strength, body composition and endothelial function [3].

The risk of sarcopenia in patients with diabetes is 3 times higher than in non-diabetics and is associated with an increased risk of death[4,5]. In T2DM, hyperglycemia causes an increase in fat mass and a decrease in muscle mass, and these measures can be demonstrated by bioimpedance analysis (BIA)[6]. BIA is a non-invasive, inexpensive, safe and easily applied method that measures body composition and metabolic status based on the electrophysiological properties of the human organism [7].Body mass index (BMI), degree of obesity, body fat/muscle ratios, basal metabolic rate (BMR) and metabolic age (met-age) can be obtained by BIA [8-11].Met-age is an index based on BMR showing the basic energy requirement of the body at rest and is related to corrected muscle mass [11]. Since BMR is significantly different between individuals in the same chronological age group [12,13], met-age was started to be used in the foreground as an alternative index in clinical practice and it was suggested that it is a good risk marker that patients can understand more easily [14].It is a predictive value in metabolic syndrome and its greater than chronological age supported the view that increased metabolic syndrome risk and its smaller than chronological age supported the view that metabolic health is good [15].

The physical examination score (PES) is an important parameter determined by the BIA method and reflects the degree of obesity, fat and lean ratios and the percentage of muscle in the body, and is graded inversely proportional to obesity and fat ratio and directly proportional to muscle ratio.

Although studies on met age are quite limited in the literature, there are no studies on met age in T2DM. Our aim in this study was to investigate the association of met age with chronologic age and physical examination score in male and female patients with T2DM. We anticipate that this study will attract the attention of researchers, raise awareness in patients and make important contributions to the literature.

Our study was conducted prospectively in T2DM patients admitted to the Internal Medicine clinic of Kırşehir Training and Research Hospital between January and March 2024 and a total of 122 patients (male: 50, female: 72) were included in the study. Male and female patients over the age of 18 years with T2DM were included in the study. Participants were adequately hydrated and female patients were not in their menstrual cycle. History of malignancy, acute or chronic infection, acute complication states of diabetes (hypoglycemia, diabeticketoacidosis, non-ketotic hyperosmolar coma, lacticacidosis), acute myocardial infarction (AMI), acute cerebro vascular disease, cardiac arrhythmia, presence of pacing and pregnancy were excluded.

The study was approved by the Ethics Committee of Kırşehir Ahi Evran University Faculty of Medicine. All procedures were performed in accordance with the Declaration of Helsinki and informed consents were obtained from all subjects.

Anthropometric and BIA Measurements

The anthrompometric measurements of the participants were performed during the first examination following 8-10 hours of fasting.Age, gender, height measurements and fixed clothing weight (1 kg) were entered into the analyzer.They were asked to stand on the analyzer with bare feet so that the electrodes on the scale touched the soles of the feet. At the same time, they were asked to hold the hand probes with their bare hands in such a way that the electrodes on the hand probes were in contact with the hand feet and to wait in an upright position, motionless and stationary until the results appeared on the screen and measurements were taken.Met-age, PES, BMR (KCAL), BMI, degree of obesity, fat mass, muscle mass, lean mass were measured with Tanita MC-780MA brand device (Tanita Europe BV Hoogoorddreef 56e 1101 BE Amsterdam TheNetherlands). The difference between meta-age and chronological age, waist (cm)/hip ratio (cm) and waist (cm)/height (cm) ratio were also calculated and included in the study.

PES Calculation

The physical examination was evaluated in two sections: fat and muscle sections.The fat section was divided into 4 compartments: obese, fat, normal and lean.The muscle section was evaluated in 3 compartments: low, normal and high according to muscle mass.Tanita automatically calculated the physical examination score out of 100 points according to the percentage of the compartments.

Blood Sampling

Following 8-10 hours of fasting, blood was collected in anti-coagulant-free gel tubes and centrifuged at 2000xg for 10 minutes after 30 minutes of clotting and fasting glucose was studied. Glycosylated hemoglobin (HbA1c) was analyzed from whole blood samples collected in K2EDTA tubes and measured by HPLC (Premier Hb9210; TrinityBiotech, Co.Wicklow, Ireland).

Statistics

Statistical analyses of the study were performed using Statistical Package for Social Sciencesversion 28.0 software for Windows (IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp., USA).The assumption of normality for quantitative variables was tested by Kolmogorov-Smirnov and Shapiro-Wilk tests. Descriptive statistics of the variables are given as Mean∓Standard deviation, Median (Min-Max), and n(%). Gender comparisons were made using Mann-Whitney-U and Independent t test according to the type of variable and fulfillment of assumptions. The relationship between metabolic age and variables was analyzed by correlation analysis. Linear regression analysis was used to determine the factors affecting metabolic age. Stepwise variable selection method was used to determine the regression model that most accurately predicts metabolic age in linear regression analysis. In all statistical analyses, P values below 0.05 were interpreted as statistically significant.

A total of 122 patients participated in the study. Of the participants, 50(41.0%) were male and 72(59.0%) were female. Descriptive statistics and group comparisons of the patients are described in Table 1. According to these results, there was no difference between genders in terms of chronologic age and metabolic age (p>0.05). However, the age difference between the metabolic age and chronologic age of the female group was significantly higher than that of the male group (p<0.001).

BMI values, degree of obesity and fat mass were significantly higher in female (p<0.001), waist/hip ratios were lower (p<0.001) and waist/height ratios were higher (p=0.001). PES, muscle mass, lean mass and BMR were higher in men than in female (p<0.001). The difference between genders in terms of HbA1c and Fasting Glucose values was not statistically significant (p>0.05).

Correlations between metabolic age and other variables in both sexes are given in Table 2. According to these results, metabolic age was positively correlated with chronological age (r:0.887**,p<0.001), BMI (r=0.370**,p<0.001), fat mass (r=0.368**,p=0.001), waist/hip ratio (r=0.298*,p=0.011) and waist/height ratio (r=0.435**,p<0.001) in female.

In male, metabolic age was positively correlated with chronological age (r=0.913**, p<0.001), BMI (r=0.389**, p<0.001), degree of obesity (r=0.316*, p=0.025), fat mass (r=0.346*, p=0.014) and waist/height ratio (r=0.436**, p=0.002).

There was a negative correlation between metabolic age and PES in both genders (Male;r= -0.361*, p=0.010 Female;r=-0.307*, p=0.009). This result is statistically significant.Met age increases as the PES decreases.

There was no correlation between Met-age and BMR in the groups (p>0.05).

The coefficients of the multiple linear regression analysis performed to determine the factors affecting metabolic age are given in Table 3 and Table 4 for both genders.According to these results, metabolic age in female is influenced by chronological age, PES and waist/height ratio. Accordingly, as chronological age and waist/height ratio increase and PES decreases, met-age increases. R2 =0.983 was found for the linear regression model.(Table 3)

The factors affecting metabolic age in male were chronological age, PES, degree of obesity and BMI values. The linear regression model was R2 =0.974. Met-age increases with increasing chronological age, degree of obesity and BMI and decreasing PES (Table 4).

This is the first study to examine met age in T2DM with BIA.Met age was positively correlated with chronological age and negatively correlated with PES in men and women with T2DM and this result is highly significant (p<0.001).Met age progresses as chronological age increases and PES decreases (Table 1).

Rodelo et al. reported that met age was significantly higher in individuals with cardiovascular disease and chronologic age increased met age[14]. There are also some studies showing the diagnostic importance of met age. Alverez et al. showed that met age and age difference were significantly higher in patients with metabolic syndrome compared to the control group[11].Similarly, Mehrdad et al. suggested that met age was higher in male and female patients with metabolic syndrome compared to the control group and that it was a predictive marker[15]. In our study, although chronological age was observed to increase metabolic age in both groups, the difference between chronological age and metabolic age was significantly higher in the female T2DM group. This may have been due to lower physical examination score and waist/hip ratio and higher BMI, waist/height ratio and fat mass in the female T2DM group (Table 1). In terms of gender, although T2DM was diagnosed at an earlier age in male patients, they were reported to have lower BMI than female patients[16,17].Female patients with T2DM were reported to have higher rates of important risk factors such as overweight, excess fat mass, obesity indices including BMI, and high blood pressure compared to male patients [18,19]. In addition, with advancing chronologic age, female T2DM patients have been reported to lose more bone and muscle mass compared to males in the same age group [20,21]. In a large-scale study by Qiwei et al., Waist/Height ratio was shown to be higher in female T2DM patients aged 18-59 years compared to males and to have a strong association with the disease, suggesting that it has a significant effect on the risk of T2DM in the general population[22]. In our study, waist/height ratio was higher in women and positively associated with met age in both sexes, but this association was stronger in women (p<0.001) and linear regression analysis showed that it significantly affected met age (Table 3). As the waist/height ratio increases, the met age also increases and this may have influenced the higher difference between met age and chronological age in female compared to male. Rodelo et al. showed that met age was strongly positively associated with waist/height ratio[14].

BMR decreases with aging due to a decrease in skeletal muscle mass [23-25]. There is a negative correlation between met age and BMR, as BMR increases, met age decreases and vice versa [11]. However, according to our study results, no correlation was observed between BMR and met age in both sexes with T2DM (Table 2). In diabetes mellitus, increased gluconeogenesis, oxidative stress, uncontrolled hyperglycemia and sympathetic activity cause an increase in BMR and it is higher in diabetes compared to non-diabetics [26-28]. Poor metabolic status in patients with diabetes leads to an increase in BMR but not to a decrease in met age, since a low met age indicates good metabolic health.

Exercise and physical activities are an integral part of T2DM and, in addition to glycemic regulation, reduce insulin resistance, increase muscle size and improve body composition [29]. The Consensus Statement of the American College of Sports Medicine reported that aerobic, resistance and combined exercise programs increase insulin sensitivity and skeletal muscle mass, promote weight loss and improve overall health in T2DM [30].Lifestyle interventions including diet and PA have a crucial role in the treatment of T2DM and prevention of its complications[31]. Met age increases with increasing waist/height ratio, BMI and degree of obesity and decreasing PES. This situation reveals the importance of physical activities in diabetes once again. By increasing the physical examination score and decreasing the waist/height ratio with exercises, it should be aimed to reduce met age, awareness should be created in patients and physical activities should be encouraged. In this context, we recommend that the effectiveness of physical activities on met age in patients with T2DM should be investigated and prospective studies should be carried out.

Our study results revealed that PES and chronological age strongly influenced met age in both sexes (p<0.001). Chronological age is a risk factor that cannot be changed, but physical activities can increase muscle mass and decrease fat mass, thereby increasing the physical examination score, decreasing waist/height ratio, degree of obesity, BMI and decreasing metabolic age, resulting in better metabolic health.

Acknowledgements

None.

Authors’ contributions

Collecting data and articles for the review: YEH,UT,GÖ,ÖR

Writing the manuscript: YEH,UT

Final approval: YEH, GÖ

Funding

The authors declared that this study had received no financial support.

Availability of data and materials

Dataset used in this study is available upon reasonable request to the

corresponding author [email protected]

Ethics approval and consent to participate

Consent for publication

Not applicable.

Competing interests

None.

Author details

¹Ahi Evran University, Faculty of Medicine, Department of Internal Medicine,Kırsehir ,Turkey.

² Ahi Evran University , Faculty of Medicine, Department of Anatomy ,Kırsehir ,Turkey

³ Ahi Evran University , Faculty of Medicine, Department of Biostatistics and Medical Information, Kırsehir ,Turkey

Motahari-Tabari N, Ahmad Shirvani M, Shirzad-E-Ahoodashty M, Yousefi-Abdolmaleki E, Teimourzadeh M. The effect of 8 weeks aerobic exercise on insulin resistance in type 2 diabetes: a randomized clinical trial. Glob J Health Sci. 2014;14;7(1):115-21. doi: 10.5539/gjhs.v7n1p115.
Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract. 2011;94(3):311-21. doi: 10.1016/j.diabres.2011.10.029.
Gutch M, Kumar S, Razi SM, Gupta KK, Gupta A. Assessment of insulin sensitivity/resistance. Indian J Endocrinol Metab. 2015 ;19(1):160-4. doi: 10.4103/2230-8210.146874.
Kobayashi Y, Long J, Dan S, Johannsen NM, Talamoa R, Raghuram S, et al. Strength training is more effective than aerobic exercise for improving glycaemic control and body composition in peoplewith normal-weighttype 2 diabetes: a randomised controlled trial. Diabetologia. 2023 ;66(10):1897-1907. doi: 10.1007/s00125-023-05958-9.
Chien YH, Tsai CJ, Wang DC, Chuang PH, Lin HT. Effects of 12-Week ProgressiveS and bag Exercise Training on Glycemic Control and Muscle Strength in Patients with Type 2 Diabetes Mellitus Combined with Possible Sarcopenia. Int J Environ Res Public Health. 2022 15;19(22):15009. doi: 10.3390/ijerph192215009.
Al-Sofiani ME, Ganji SS, Kalyani RR. Body composition changes in diabetes and aging. J Diabetes Complications. 2019 ;33(6):451-459. doi: 10.1016/j.jdiacomp.2019.03.007.
Barbosa-Silva MCG, Barros AJD. Bioelectrical impedance analysis in clinical practice: a new perspective on its use beyond body composition equations. Curr Opin Clin Nutr Metab Care 2005; 8: 311– 317.
Wang JG, Zhang Y, Chen HE, Li Y, Cheng XG, Xu L, et al.. Comparison of two bioelectrical impedance analysis devices with dualenergy X-ray absorptiometry and magneticresonance imaging in the estimation of body composition. J Strength Cond Res. 2013 ;27(1)
Kreissl A, Jorda A, Truschner K, Skacel G, Greber-Platzer S. Clinically relevant body composition methods for obese pediatric patients. BMC Pediatr. 2019. 21;19(1):84. doi: 10.1186/s12887-019-1454-2.
Luy SC, Dampil OA. Comparison of the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry for Measurement of Basal Metabolic Rate among Adult Obese Filipino Patients with Prediabetesor Type 2 Diabetes Mellitus. J ASEAN Fed EndocrSoc. 2018;33(2):152-159. doi: 10.15605/jafes.033.02.07.
Vásquez-Alvarez S, Bustamante-Villagomez SK, Vazquez-Marroquin G, Porchia LM, Pérez-Fuentes R, Torres-Rasgado E, et al. Metabolic Age, an Index Based on Basal Metabolic Rate, Can Predict Individuals That are High Risk of Developing Metabolic Syndrome. High Blood PressCardiovascPrev. 2021;28(3):263-270. doi: 10.1007/s40292-021-00441-1.
Bonfanti N, Fernández JM, Gomez-Delgado F, Pérez-Jiménez F. Efecto de dos diet as hipocalóricas y su combinación conejerciciofísicosobre la tasa metabólica basal y la composición corporal [Effect of two hypocaloric diets and their combination with physical exercise on basal metabolic rate and body composition]. Nutr Hosp. 2014.1;29(3):635-43. Spanish. doi: 10.3305/nh.2014.29.3.7119.
Coello SPO, Urquizo AFP. Determinación del índice de masa corporal y tasa metabólica basal en estudiantes de medicina de la Escuela Superior Politécnica de Chimborazo (ESPOCH). La Ciencia al Servicio de la Salud. 2019;9(2):14–21.
Elguezabal-Rodelo R, Ochoa-Précoma R, Vazquez-Marroquin G, Porchia LM, Montes-Arana I, Torres-Rasgado E, et al. Metabolic age correlates better than chronological age with waist-to-height ratio, a cardiovascular risk index. MedClin (Barc). 2021 . 12;157(9):409-417. English, Spanish. doi: 10.1016/j.medcli.2020.07.026.
MEHRDAD R, POURAGHA H, VESAL M,POURYAGHOUB G, NADERZADEH M. Zahra ALEMOHAMMAD ZB.Metabolic Age:A New Predictor for Metabolic Syndrome.Turk J Endocrinol Metab. 2021;25:78-86
Kautzky-Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia. 2023 . ;66(6):986-1002. doi: 10.1007/s00125-023-05891-x
Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, et al. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia. 2020 ;63(3):453-461. doi: 10.1007/s00125-019-05040-3.
Wright AK, Welsh P, Gill JMR, Kontopantelis E, Emsley R, Buchan I, et al. Age-, sex- and ethnicity-related differences in body weight, blood pressure, HbA1c and lipid levels at the diagnosis of type 2 diabetes relativeto people without diabetes. Diabetologia. 2020;63(8):1542-1553. doi: 10.1007/s00125-020-05169-6.
Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, Giovannucci EL. Comparison of the association of predicted fat mass, body mass index, and other obesity indicators with type 2 diabetes risk: two large prospective studies in US men and women. Eur J Epidemiol. 2018 Nov;33(11):1113-1123. doi: 10.1007/s10654-018-0433-5.
Goossens GH, Jocken JWE, Blaak EE. Sexual dimorphism in cardio metabolic health: the role of adiposetissue, muscle and liver. Nat Rev Endocrinol. 2021 ;17(1):47-66. doi: 10.1038/s41574-020-00431-8.
Rubin R. Postmenopausal Women With a "Normal" BMI Might Be Overweight or Even Obese. JAMA. 2018. 27;319(12):1185-1187. doi: 10.1001/jama.2018.0423.
Ge Q, Li M, Xu Z, Qi Z, Zheng H, Cao Y, et al. Comparison of different obesity indices associated with type 2 diabetes mellitus among different sex and agegroups in Nantong, China: a cross-sectionstudy. BMC Geriatr. 2022 .3;22(1):20. doi: 10.1186/s12877-021-02713-w.
Manini TM. Energy expenditureandaging. Ageing Res Rev. 2010 ;9(1):1-11. doi: 10.1016/j.arr.2009.08.002.
Konarzewski M, Książek A. Determinants of intra-specificvariation in basal metabolic rate. J Comp Physiol B. 2013 ;183(1):27-41. doi: 10.1007/s00360-012-0698-z.
Oh SK, Son DH, Kwon YJ, Lee HS, Lee JW. Association between Basal Metabolic Rate and Hand grip Strength in Older Koreans. Int J Environ Res Public Health. 2019.9;16(22):4377. doi: 10.3390/ijerph16224377.
Gougeon R, Lamarche M, Yale JF, Venuta T. The prediction of resting energy expenditure in type 2 diabetes mellitus is improved by factoring for glycemia. Int J Obes Relat Metab Disord. 2002 ;26(12):1547-52. doi: 10.1038/sj.ijo.0802178.
Piaggi P, Thearle MS, Bogardus C, Krakoff J. Fasting hyperglycemia predict slower rates of weight gain by increased energy expenditure and fat oxidation rate. J Clin Endocrinol Metab. 2015;100(3):1078-87. doi: 10.1210/jc.2014-3582.
Huang KC, Kormas N, Steinbeck K, Loughnan G, Caterson ID. Resting metabolic rate in severely obese diabetic and nondiabetic subjects. Obes Res. 2004 ;12(5):840-5. doi: 10.1038/oby.2004.101.
Sampath Kumar A, Maiya AG, Shastry BA, Vaishali K, Ravishankar N, Hazari A, et al. Exercise and insülin resistance in type 2 diabetes mellitus: A systematic review and meta-analysis. Ann Phys Rehabil Med. 2019 ;62(2):98-103. doi: 10.1016/j.rehab.2018.11.001.
Kanaley JA, Colberg SR, Corcoran MH, Malin SK, Rodriguez NR, Crespo CJ, et al. Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine. Med Sci Sports Exerc. 2022 . 1;54(2):353-368. doi: 10.1249/MSS.
Lavie CJ, Johannsen N, Swift D, Sénéchal M, Earnest C, Church T, et al. Exercise is Medicine – The Importance of Physical Activity, Exercise Training, Cardio respiratory Fitness and Obesity in the Prevention and Treatment of Type 2 Diabetes. Eur Endocrinol. 2014 ;10(1):18-22. doi: 10.17925/EE.2014.1

Table 1. Demographic variables, BIA measures and laboratory parameters of the groups

Variables/Gender

Total, n=122

Male, n=50

Female, n=72

Chronological Age

56.33 11.36

57.0(19-85)

56.6

57.0(36-85)

56.14 12.41

57.0(19-85)

0.827^&

Met Age

58.30 12.16

58.0(19-89)

55.98 10.71

55.0(39-89)

59.92 12.90

60.0(19-83)

0.079^&

Met-age-Chronological age difference

4.92 3.65

4.0(0.0-12.0)

3.38

3.0(0.0-12.0)

6.0 3.79

6.0(0-12)

<0.001^*

PES

62.73 24.63

49.0(34.0-96.0)

74.2 22.40

91.0(34-96)

54.76 23.04

45.0(34-96)

<0.001^*

BMI

30.28 5.20

29.75(20.30-45.80)

27.87 4.24

28.0(20.30-43.6)

31.96 5.17

31.20(22.0-45.80)

<0.001^&

Degree of Obesity

23.58 18.20

18.30(0.94-93.31)

16.42 12.16

13.04(0.94-71.06)

28.56 20.03

26.12(1.14-93.31)

<0.001^*

Fat Mass

26.02 9.75

24.45(7.60-51.80)

19.80 7.74

18.90(7.60-51.80)

30.34 8.64

29.55(14.30-48.0)

<0.001^&

Muscle Mass

54.20 8.48

52.80(36.60-77.80)

60.31

60.9(40.8-77.8)

49.95 5.92

50.45(36.60-62.70)

<0.001^&

Lean Mass

57.12 8.98

55.60(38.60-81.80)

63.61 8.38

64.1(43-81.8)

52.61 6.22

53.15(38.6-66.0)

<0.001^&

Waist/Hip Ratio

0.89 0.05

0.90(0.71-1.0)

0.91

0.92(0.71-1.0)

0.87 0.05

0.87(0.76-1.0)

<0.001^*

Waist/Height Ratio

0.58 0.07

0.57(0.42-0.83)

0.55 0.06

0.55(0.42-0.83)

0.60 0.08

0.59(0.45-0.78)

0.001^&

Basal Metabolic Rate (KCAL)

1.68 0.25

1.65(1.16-2.51)

1.84

1.83(1.23-2.51)

1.58 0.19

1.59(1.16-1.98)

<0.001^&

Fasting glucose

180.20 70.20

170.50(97.0-395.0)

179.80 73.10

153.50(97.0-358.0)

180.48 68.64

176.0(98.0-395.0)

0.958^*

HbA1c

8.39 2.12

7.85(5.70-14.70)

8.44 2.17

7.60(5.80-14.40)

8.35 2.10

8.20(5.70-14.70)

0.817^&

&: Independent t test, *: Mann-Whitney U

BMI:Body Mass Index,KCAL: Kilocalories, HbA1C: Glycosylated hemoglobin,PES: Physical Examination

Table 2.Correlations between metabolic age and other variables across genders

Variables/Gender	Female		Male
Variables/Gender	r	p	r	p
Chronological Age	0.887**	<0.001	0.913**	<0.001
PES	-0.307**	0.009	-0.361*	0.001
BMI	0.370**	<0.001	0.389**	<0.001
Degree of Obesity	0.088	0.461	0.316*	0.025
Fat Mass	0.368**	0.001	0.346*	0.014
Muscle Mass	-0.051	0.670	0.057	0.697
Lean Mass	-0.051	0.672	0.041	0.779
Waist/Hip Ratio	0.298*	0.011	0.233	0.103
Waist/Height Ratio	0.435**	<0.001	0.436**	0.002
Basal Metabolic Rate (KCAL)	-0.052	0.662	0.065	0.653
Fasting glucose	-0.023	0.847	0.035	0.809
HbA1c	0.059	0.620	0.069	0.634

PES: Physical Examination Score, BMI: Body Mass Index, KCAL: Kilocalories, HbA1C: Glycosylated hemoglobin

Table 3.Linear regression results of factors affecting metabolic age in female T2DM group

Variables		95,0% Confidence Intervalfor B		P
Variables		Low	Upper	P
Chronological age	0.923	0.861	0.985	<0.001
Physical Examination Score	-0.082	-0.112	-0.053	<0.001
BMI	0.387	-0.031	0.804	0.069
Degree of Obesity	-0.032	-0.132	0.069	0.529
Fat Mass	-0.017	-0.227	0.193	0.870
Waist/Height Ratio	39.400	5.280	73.519	0.024
Waist/Hips	-4.591	-20.987	11.804	0.578

Table 4.Linear regression results of factors affecting metabolic age in male T2DM group

Variables		95,0% ConfidenceIntervalfor B		p
Variables		Low	Upper	p
Chronological age	0.990	0.928	1.053	<0.001
PES	-0.105	-0.136	-0.074	<0.001
BMI	0.623	0.185	1.061	0.006
Degree of Obesity	0.118	0.022	0.214	0.017
Fat Mass	-0.041	-0.296	0.213	0.744
Waist/Height Ratio	-23.996	-61.224	13.232	0.200
Waist/Hips	7.459	-13.519	28.437	0.477

The authors declare no competing interests.

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Correlation of Metabolic Age with Chronological Age and Physical Examination Score from BIA in Male and Female Patients with Type 2 Diabetes Mellitus

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