Central obesity and glycaemic control in newly diagnosed type 2 diabetes mellitus patients: evidence from Uzbekistan

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In contrast, among non-obese patients (normal weight or overweight), glycaemic disturbances may remain under-recognised. This study aimed to evaluate the association between anthropometric measures and glycaemic control indicators in newly diagnosed, untreated obese and non-obese patients with type 2 diabetes mellitus (T2DM) in Uzbekistan. Methods A cross-sectional study was conducted among 104 untreated, newly diagnosed T2DM patients at the Samarkand branch of the Republican Specialised Endocrinology Hospital, Uzbekistan. Body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and glycated haemoglobin (HbA1c) were recorded. Patients were categorised as obese (BMI ≥ 30 kg/m²) or non-obese (BMI < 30 kg/m²). Independent Student’s t -tests and Mann–Whitney U tests were used to compare variables between groups. Age-adjusted partial Spearman correlations were applied to evaluate associations between anthropometric parameters and glycaemic markers. Results The study included 104 newly diagnosed T2DM patients (mean age 52.1 ± 11.7 years; 61.5% men), with a median BMI of 28.4 [26.53–30.28] kg/m² and a mean WC of 99.5 ± 6.65 cm. Obese (≥ 30.0 kg/m²) patients (n = 30) were significantly younger (47.7 ± 9.32 years) than non-obese patients (n = 74; 53.85 ± 12.05 years; p = 0.014), and FPG levels were significantly higher in obese patients (13.4 ± 2.97 mmol/L) than in non-obese patients (11.89 ± 2.94 mmol/L; p = 0.02). Age-adjusted partial Spearman correlation analysis in all patients showed positive associations between BMI and FPG (ρ = 0.212, p < 0.05), and between WC and both FPG and OGTT, with weak-to-moderate correlations (ρ = 0.251 and 0.23, respectively; p < 0.05). In non-obese patients, WC was weakly to moderately positively correlated with FPG and OGTT (ρ = 0.236 and ρ = 0.297, respectively; p < 0.05), whereas no significant correlations were observed in obese patients. Conclusion Although waist circumference was elevated among non-obese (< 30.0 kg/m²) patients (97.39 ± 5.58 cm), it showed only a weak positive correlation with glycaemic indicators. These findings suggest that central obesity may play a modest but clinically relevant role in the early identification of glycaemic dysregulation among non-obese individuals. newly diagnosed type 2 diabetes mellitus non-obese central obesity glycaemic control fasting plasma glucose Uzbekistan Figures Figure 1 Figure 2 Figure 3 Background Type 2 diabetes mellitus (T2DM) is one of the significant global health concerns, currently affecting more than 537 million adults worldwide [ 1 ]. Factors such as sedentary lifestyle, unhealthy diet, smoking, alcohol consumption, and increasing obesity have been identified as significant contributors to the rising prevalence of T2DM and early insulin resistance [ 2 ]. Importantly, preventable risk factors, particularly central or visceral obesity, have been shown to increase the risk of early hyperglycaemia, notably among individuals with a normal body mass index (BMI) [ 3 ]. In addition, non-modifiable factors such as genetic predisposition, age, and male sex are also recognised contributors to the development of T2DM [ 4 , 5 ]. In Central Asia, including Uzbekistan, the prevalence of T2DM is relatively high, currently estimated at approximately 7.5% and projected to increase to 7.6% by 2050 [ 6 ]. This trend poses a substantial burden on the healthcare system, particularly given the high prevalence of overweight (approximately 50%) and obesity (20%) among adults aged 18–64 years. Furthermore, behavioural risk factors prevalent in Uzbekistan, including unhealthy lifestyles, high-salt diets, and alcohol consumption, further hinder diabetes prevention efforts and worsen glycaemic control. Demographic characteristics, especially population ageing, also play an essential role, with stronger associations reported from the age of 45 years onwards [ 7 ]. Obesity remains a major modifiable risk factor for T2DM, and several studies have demonstrated associations between anthropometric indicators such as waist circumference (WC) and waist-to-hip ratio (WHR) and glycaemic control parameters [ 8 ]. Similarly, BMI, body shape index, and visceral fat index have been reported to be positively associated with fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and glycated haemoglobin (HbA1c) levels in both prediabetic and diabetic individuals [ 9 ]. Evidence further suggests that central obesity is more strongly associated with early hyperglycaemia than BMI alone. In non-obese individuals, particularly among Asian populations, T2DM may develop despite a normal BMI, a phenomenon attributed to disproportionate fat accumulation in the abdominal region [ 10 ]. Studies from East Asia indicate that excess visceral fat is a more important predictor of insulin resistance than elevated BMI. The pathophysiological mechanisms linking central adiposity to early hyperglycaemia are well established. Visceral adipose tissue is metabolically active and produces pro-inflammatory cytokines, such as interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α), which contribute to hepatic insulin resistance and pancreatic β-cell stress [ 11 ]. These inflammatory processes promote the progression from normoglycaemia to prediabetes and eventually overt T2DM, even in individuals without overt obesity. Previous studies have demonstrated strong associations between central obesity, particularly WC, and glycaemic control parameters, including FPG, OGTT, and HbA1c, among prediabetic and diabetic populations [ 12 ]. However, data focusing specifically on newly diagnosed obese and non-obese T2DM patients remains limited, particularly in Uzbekistan. Understanding glycaemic impairment in relation to anthropometric characteristics has important implications for diabetes prevention, especially in low- and middle-income countries with limited healthcare resources [ 13 , 14 ]. Identifying these relationships in newly diagnosed patients may also facilitate early intervention and more effective organisation of treatment strategies, ultimately improving patient care [ 15 ]. Accordingly, the present study aimed to evaluate the association between anthropometric measurements (BMI and WC) and glycaemic control indicators (FPG, OGTT, and HbA1c) among newly diagnosed T2DM patients in Uzbekistan. Methods Study location and study design This collaborative cross-sectional study was conducted at the Institute of Immunology and Human Genomics of the Academy of Sciences of the Republic of Uzbekistan and the Samarkand Branch of the Republican Scientific and Practical Medical Centre for Specialised Endocrinology named after Academician Y.H. Turakulov. Demographic, anthropometric, and clinical data were collected at the Samarkand Branch of the Republican Scientific and Practical Medical Centre for Specialised Endocrinology between 1 January 2025 and 28 February 2025. Study population and data collection A total of 104 patients with newly diagnosed type 2 diabetes mellitus (T2DM) were included in the study. Patients were diagnosed according to national clinical standards, based on World Health Organisation (WHO) and American Diabetes Association (ADA) recommendations. Diagnostic criteria included fasting plasma glucose (FPG) ≥7.0 mmol/L, 2-hour oral glucose tolerance test (OGTT) glucose ≥11.1 mmol/L, glycated haemoglobin (HbA1c) ≥6.5%, or random plasma glucose ≥11.1 mmol/L in the presence of classic symptoms (polydipsia, polyuria, and weight loss) [16]. Data were obtained from medical records using consecutive sampling and included age, sex, body weight, height, WC, HbA1c, OGTT, and FPG. Inclusion criteria were as follows: – untreated, newly diagnosed T2DM patients – age ≥18 years – both sexes Exclusion criteria included: – age <18 years – type 1 diabetes mellitus or gestational diabetes – previously treated or undertreated T2DM – chronic diseases in a decompensated stage that could affect blood glucose levels Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). For descriptive analysis, BMI was categorised according to WHO criteria as underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), obesity class I (30.0–34.9 kg/m²), obesity class II (35.0–39.9 kg/m²), and obesity class III (≥40.0 kg/m²) [17]. Laboratory measurements of HbA1c, OGTT, and FPG were performed using instruments compliant with Uzbekistan's national treatment standards. For comparative analyses, patients were classified according to WHO criteria as ¾ Obese: BMI ≥30.0 kg/m² ¾ Non-obese: BMI <30.0 kg/m² Data analysis Descriptive analyses were initially performed using Microsoft Excel, and inferential statistical analyses were subsequently conducted using JASP software. Continuous variables were first assessed for normality using the Shapiro–Wilk test. Normally distributed data were expressed as mean ± standard deviation (SD) and compared between groups using an independent Student’s t-test. Non-normally distributed variables were presented as median (interquartile range, IQR) and compared using the Mann–Whitney U test. Categorical variables were analysed using the chi-squared test and presented as frequencies and percentages. Given the relatively small sample size and non-normal distribution of some variables, Spearman’s correlation analysis was used. Given significant age differences between obese and non-obese groups, age-adjusted partial Spearman correlation analyses were performed to assess the relationships between anthropometric variables (BMI and WC) and glycaemic control indicators (FPG, OGTT, and HbA1c). Correlation analyses were subsequently conducted separately for obese and non-obese groups. All statistical analyses were performed using JASP software. Statistical significance was defined as a p-value < 0.05. Results Descriptive analysis Table 1 Baseline characteristics of newly diagnosed T2DM(N = 104) Variable Value Age, mean ± SD (years) 52.07 ± 11.67 Sex (Male), n (%) 64 (61.5%) BMI, kg/m², median [IQR] 28.4 [26.53–30.28] WC, cm, mean ± SD 99.5 ± 6.65 BMI Category, n (%) - Normal weight 11 (10.6%) - Overweight 63 (60.9%) - Obesity Class I 18 (17.3%) - Obesity Class II 11 (10.6%) - Obesity Class III 1 (0.9%) HbA1c, %, median [IQR] 9.2 [8.6–9.8] FPG, mmol/L, mean ± SD 12.34 ± 3.02 OGTT, mmol/L, median [IQR] 14.45 [12.75–16.15] A total of 104 newly diagnosed patients with T2DM were included in the study. The mean age was 52.1 ± 11.7 years, and men comprised a higher proportion of the sample (n = 64, 61.5%) than women (n = 40, 38.5%). Regarding anthropometric characteristics, the median body mass index (BMI) was 28.4 [26.53–30.28] kg/m², and the mean WC was 99.5 ± 6.65 cm. Among the study population, 60.9% (n = 63) were overweight, while 28.8% (n = 30) had obesity. Specifically, 17.3% (n = 18) had obesity class I, 10.6% (n = 11) had obesity class II, and approximately 1% (n = 1) had obesity class III. Only 10.6% (n = 11) of patients had a normal BMI. The median HbA1c was 9.2 [8.6–9.8] %, the mean fasting plasma glucose (FPG) level was 12.34 ± 3.02 mmol/L, and the median 2-hour OGTT value was 14.45 [12.75–16.15] mmol/L (Table 1 ). Comparison of obese and non-obese patients Patients were categorised into obese (n = 30) and non-obese (n = 74) groups. A statistically significant age difference was observed between the two groups. Obese patients were younger, with a mean age of 47.7 ± 9.32 years, compared with 53.85 ± 12.05 years in the non-obese group (p = 0.014) (Fig. 1 ). Table 2 Baseline characteristics of obese and non-obese patients with newly diagnosed T2DM Variable Obese n = 30 Non-obese n = 74 p-value Age, years 47.7 ± 9.32 53.85 ± 12.05 0.014** Sex (Male) n (%) 20 (66.7%) 44 (59.5%) 0.494* BMI, kg/m² 32.2 [29.45–35.15] 27.5 [25.3–29.8] < .001*** WC, cm 104.7 ± 6.29 97.39 ± 5.58 < .001** HbA1c, % 9.2 [8.75–9.65] 9.15 [8.54–9.81] 0.246*** FPG, mmol/L 13.4 ± 2.97 11.89 ± 2.94 0.02** OGTT, mmol/L 14.50 [12.78–16.23] 14.35 [12.66–16.04] 0.243 Note: *p-value derived from the Chi-squared test, **p-value derived from the independent Student’s t-test, ***p-value derived from the Mann–Whitney U test. The male-to-female ratio did not differ significantly between groups. Men accounted for 66.7% (n = 20) of the obese group and 59.5% (n = 44) of the non-obese group (p = 0.494) (Table 2 ). As expected, significant differences were observed in anthropometric parameters. The median BMI was higher in the obese group (32.2 [29.45–35.15] kg/m²) than in the non-obese group (27.5 [25.3–29.8] kg/m²; p < 0.001). Similarly, mean WC was significantly greater among obese patients (104.7 ± 6.29 cm) compared with non-obese patients (97.39 ± 5.58 cm; p < 0.001) (Table 2 ). HbA1c levels were slightly higher in the obese group (median 9.2 [8.75–9.65] %) than in the non-obese group (9.15 [8.54–9.8] %); however, this difference was not statistically significant (p = 0.246) (Table 2 ). FPG levels were significantly higher in obese patients (13.4 ± 2.97 mmol/L) than in non-obese patients (11.89 ± 2.94 mmol/L; p = 0.02), as illustrated in Fig. 2 . In contrast, median OGTT values were similar between the obese and non-obese groups (14.50 [12.78–16.23] vs. 14.35 [12.66–16.04] mmol/L, respectively) and did not differ significantly (p = 0.243) (Table 2 ). Correlation between anthropometric measurements and glycaemic control indicators Correlation analysis among all newly diagnosed T2DM patients demonstrated a weak but statistically significant positive association between BMI and FPG (ρ = 0.212, p < 0.05). No significant correlations were observed between BMI and other glycaemic indicators (Table 3 ). WC showed significant positive correlations with both FPG (ρ = 0.251, p < 0.05) and OGTT (ρ = 0.23, p < 0.05) (Table 3 ). Next, partial correlation analysis was performed separately Table 3 Partial correlations between anthropometric measurements and glycaemic variables among the newly diagnosed T2DM Variable WC, cm BMI, kg/m² FPG, mmol/L OGTT, mmol/L HbA1c, % WC, cm — BMI, kg/m² 0.588*** — FPG, mmol/L 0.251* 0.212* — OGTT, mmol/L 0.23* 0.171 0.791*** — HbA1c, % 0.07 0.11 0.567*** 0.529*** — Note. Spearman correlation was used—partial correlations adjusted for age. *p < .05, ** p < .01, *** p < .001. In obese and non-obese patients. The analysis showed that, among non-obese newly diagnosed T2DM patients, weak but statistically significant correlations were observed between WC and FPG (ρ = 0.236, p < 0.05), as well as weak to moderate correlations between WC and OGTT (ρ = 0.297, p < 0.05) (Table 4) (Fig. 3). Table 4. Partial correlations between anthropometric measurements and glycaemic variables among newly diagnosed non-obese patients with T2DM Variable WC, cm BMI, kg/m² FPG, mmol/L OGTT, mmol/L HbA1c, % WC, cm — BMI, kg/m² 0.375** — FPG, mmol/L 0.236* 0.126 — OGTT, mmol/L 0.297* 0.161 0.843*** — HbA1c, % 0.081 0.101 0.564*** 0.503*** — Note. Spearman correlation was used—partial correlations adjusted for age. *p < .05, ** p < .01, *** p < .001. However, in obese patients, no significant correlations were identified between anthropometric indicators and glycaemic control parameters. Discussion In this study of 104 newly diagnosed patients with type 2 diabetes mellitus (T2DM), the mean age at diagnosis was 52.1 ± 11.7 years, indicating an earlier onset than estimates from global pooled analyses [ 18 ]. However, the mean age at diagnosis reported in developed countries, such as Germany (61.0 ± 13.4 years), is notably higher than that observed in our study, suggesting regional variation in the onset of T2DM [ 19 ]. Differences in genetic background, dietary patterns, socioeconomic conditions, and healthcare access may contribute to these variations. Men accounted for 61.2% of cases in the present study, indicating a higher burden of T2DM among males. Sex-specific analysis further showed that men were at greater risk than women, which is consistent with global evidence demonstrating a higher incidence and earlier onset of T2DM among males, often at a lower BMI compared with females [ 20 ]. These findings highlight the importance of considering age and sex when designing strategies for T2DM prevention and management. In this cohort, overweight individuals constituted 60.9% of newly diagnosed T2DM cases, indicating that excess body weight is a major contributor to the early development of T2DM. This proportion is higher than that reported in some low-income settings; for example, studies from South Africa have documented an overweight prevalence of approximately 34% among newly diagnosed T2DM patients [ 21 ]. Such differences may reflect variations in lifestyle, urbanisation, dietary habits, and regional obesity trends. A significant age difference was observed between obese and non-obese patients, with obese individuals being younger (47.7 ± 9.32 years) than non-obese patients (53.85 ± 12.05 years). This finding suggests that obesity may accelerate the onset of T2DM, with younger obese individuals developing glucose dysregulation earlier in life. Similar age-related differences between obese and non-obese patients have been reported previously [ 22 , 23 ]. In contrast, T2DM in older non-obese individuals may be driven by age-related declines in β-cell function or other metabolic alterations independent of adiposity. These observations underscore the importance of preventing obesity at younger ages to reduce the risk of early-onset T2DM. Furthermore, fasting plasma glucose (FPG) levels were lower in non-obese patients (11.89 ± 2.94 mmol/L) than in obese patients (13.4 ± 2.97 mmol/L). This difference may be explained by earlier and more pronounced glycaemic dysregulation in obese individuals, driven by excess adiposity, insulin resistance, and impaired glucose uptake, leading to elevated fasting glucose levels, as previously reported [ 24 ]. Interestingly, although non-obese patients had lower fasting plasma glucose levels, waist circumference remained significantly associated with indicators of glycemic control. This suggests that central obesity may play a significant role in impaired glucose regulation, despite these patients not being obese by BMI. Age-related visceral fat redistribution may, to some extent, help to clarify these findings, especially among older patients who are not obese. These findings are consistent with existing evidence indicating that waist circumference may be a more important predictor of impaired glucose regulation than BMI, and emphasise the need to consider measuring central obesity in patients with newly diagnosed T2DM [ 25 ]. Limitations Limitations of the current study include its cross-sectional design and relatively small sample size. In addition, the study was conducted in a single hospital, which may limit the generalizability of the findings across Uzbekistan. Despite these limitations, we performed the statistical analyses rigorously to ensure the reliability of the results. Although this study is ongoing, baseline characteristics are reported, and immunological and pharmacogenetic analyses are planned for this sample to provide a deeper exploration of glycaemic resistance mechanisms among newly diagnosed T2DM patients. Conclusion In this cross-sectional study of newly diagnosed patients with type 2 diabetes mellitus in Uzbekistan, older adults were more likely to be classified as non-obese according to BMI. Nevertheless, waist circumference remained significantly associated with poor glycaemic control in this group. These findings indicate that central obesity is a key determinant of glucose dysregulation, even among patients who are non-obese by BMI criteria. Incorporating measures of central obesity into routine screening may enhance the detection and management of type 2 diabetes mellitus, particularly in geriatric populations. Abbreviations WHO, World Health Organisation; T2DM, type 2 diabetes mellitus; BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; FPG, fasting plasma glucose; OGTT, oral glucose tolerance test; HbA1c, glycated haemoglobin Declarations Ethics approval and consent to participate The study protocol was reviewed and approved by the Institutional Review Board of the Institute of Immunology and Human Genomics, Academy of Sciences of the Republic of Uzbekistan, in accordance with the Bioethics and Safety Act (IRB No: 2025-0002). The study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to inclusion in the study. Clinical trial registration Not applicable Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This study did not receive any specific funding. Author Contribution ShZ and ON conceptualised the study. ShE collected the data. ON drafted the manuscript. NR, JK and MS reviewed the manuscript. ShZ supervised the study. Acknowledgement The authors thank the medical staff for their assistance with patient recruitment and data collection and acknowledge the organisational and technical support of Negmatova Gulzoda Shukhratovna, Director of the Samarkand Branch of the Republican Scientific and Practical Medical Centre for Specialised Endocrinology named after Academician Y.H. Turakulov. Data Availability The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. References Lin X, Xu Y, Pan X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020;10:14790. 10.1038/s41598-020-71908-9 . Salzberg L. Risk factors and lifestyle interventions. Prim Care. 2022;49(2):201–12. 10.1016/j.pop.2021.11.001 . Zhong P, Tan S, Zhu Z, Liang Y, Huang W, Wang W. Normal-weight central obesity and risk of cardiovascular and microvascular events in adults with prediabetes or diabetes: Chinese and British cohorts. Diabetes Metab Res Rev. 2023;39(8):e3707. 10.1002/dmrr. 3707 . Fletcher B, Gulanick M, Lamendola C. Risk factors for type 2 diabetes mellitus. J Cardiovasc Nurs. 2002;16(2):17–23. 10.1097/00005082-200201000-00003 . Hivert MF, Vassy JL, Meigs JB. Susceptibility to type 2 diabetes mellitus—from genes to prevention. Nat Rev Endocrinol. 2014; 10 (4):198–205. 10.1038/nrendo . 2014.11. International Diabetes Federation. Uzbekistan diabetes data and trends. IDF Diabetes Atlas. 2025. Alieva A, Alimov A, Khaidarova F, Ismailov S, Rakhimova G, Nazhmutdinova D, et al. Assessing the effectiveness of type 2 diabetes screening in the Republic of Uzbekistan. Int J Endocrinol Metab. 2022;20(4):e124036. 10.5812/ijem-124036 . Chikani V, Jeyaseelan L, Saravanan R. Association of glycaemic control and anthropometric measurements among type 2 diabetes mellitus patients. J Fam Med Prim Care. 2021;10(8):2942–9. 10.4103/jfmpc.jfmpc_1941_20 . Kurniawan LB, Apriani LP, Kadir NA, Nurahmi N, Basri MI, Aminuddin A. Visceral fat as the main obesity index that determines the occurrence of adipose tissue insulin resistance. EJIFCC. 2025;36(3):321–7. Jin X, Liu J, Cao Q, Lin J, Wu G, Liu L, et al. Normal-weight central obesity: implications for diabetes mellitus. Front Nutr. 2023;10:1239493. 10.3389/fnut.2023.1239493 . Sethi JK, Hotamisligil GS. Metabolic messengers: tumour necrosis factor. Nat Metab. 2021;3:1302–12. 10.1038/s42255-021-00470-z . Taderegew MM, Emeria MS, Zegeye B. Association of glycaemic control and anthropometric measurement among type 2 diabetes mellitus: a cross-sectional study. Diabetol Int. 2021;12(4):356–63. 10.1007/s13340-021-00490-w . Simmons SS. Biomarkers and anthropometric measures for predicting diagnosed diabetes mellitus in adults in low- and middle-income countries. Heliyon. 2023;9(9):e19494. 10.1016/j.heliyon.2023.e19494 . Alimov AV, Khaidarova FA, Ismailov SI, Rakhimova GN, Nazhmutdinova DK, Shagazatova BK, et al. Cost-effective screening model for type 2 diabetes in the Republic of Uzbekistan. TBEM. 2021;14(3):2. 10.54185/TBEM/vol14_iss3/a2 . Huang H, Zheng X, Wen X, Zhong J, Zhou Y, Xu L. Visceral fat correlates with insulin secretion and sensitivity independent of BMI and subcutaneous fat in Chinese with type 2 diabetes. Front Endocrinol (Lausanne). 2023;14:1144834. 10.3389/fendo.2023.1144834 . American Diabetes Association Professional Practice Committee. Diagnosis and classification of diabetes: standards of care in diabetes—2024. Diabetes Care. 2024;47(Suppl 1):S20–42. 10.2337/dc24-S002 . World Health Organisation. Obesity: preventing and managing the global epidemic. WHO Technical Report Series 894. Geneva: WHO; 2000. Carrillo-Larco RM, Guzmán-Vilca WC, Xu X, Bernabé-Ortiz A. Mean age and body mass index at type 2 diabetes diagnosis: pooled analysis of 56 health surveys. Diabet Med. 2024;41(2):e15174. 10.1111/dme.15174 . Jacobs E, Rathmann W, Tönnies T, Arendt D, Marchowez M, Veith L, et al. Age at diagnosis of type 2 diabetes in Germany: a nationwide analysis. Diabet Med. 2020;37(10):1723–7. 10.1111/dme.14100 . Kautzky-Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia. 2023;66(6):986–1002. 10.1007/s00125-023-05891-x . Mphasha MH, Skaal L, Mothiba TM. Prevalence of overweight and obesity amongst patients with diabetes and their non-diabetic family members in Limpopo province, South Africa. S Afr Fam Pract. 2022;64(1):e1–7. 10.4102/safp.v64i1.5409 . Hillier TA, Pedula KL. Characteristics of an adult population with newly diagnosed type 2 diabetes: relation of obesity and age of onset. Diabetes Care. 2001;24(9):1522–7. 10.2337/diacare.24.9.1522 . Strati M, Moustaki M, Psaltopoulou T, Vryonidou A, Paschou SA. Early onset type 2 diabetes mellitus: an update. Endocrine. 2024;85(3):965–78. 10.1007/s12020-024-03772-w . Chaudhari A, Gujarathi S, Bhatia G. Comparison of blood glucose in obese and non-obese students in a medical college. Int J Res Med Sci. 2020;8(11):3916–9. 10.18203/2320-6012.ijrms20204877 . Sannasi L. Comparison of waist circumference in type 2 diabetes and non-diabetic population. Glob J Med Pharm Biomed Update. 2025;20:4. 10.25259/GJMPBU_1_2025 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Feb, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 12 Jan, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviews received at journal 07 Jan, 2026 Reviews received at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 04 Jan, 2026 Reviewers invited by journal 03 Jan, 2026 Editor invited by journal 24 Dec, 2025 Editor assigned by journal 21 Dec, 2025 Submission checks completed at journal 21 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Eshmuradova","email":"","orcid":"","institution":"Samarkand State Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shokhista","middleName":"","lastName":"Eshmuradova","suffix":""},{"id":565157705,"identity":"1da3aa34-ff54-491e-8c6f-3c905a691a9a","order_by":2,"name":"Jonibek Kadirov","email":"","orcid":"","institution":"Samarkand State Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jonibek","middleName":"","lastName":"Kadirov","suffix":""},{"id":565157709,"identity":"2bba2e9a-1f50-4fce-8c01-b0d8ecd5ec16","order_by":3,"name":"Murodillo Saidov","email":"","orcid":"","institution":"Samarkand State Medical University","correspondingAuthor":false,"prefix":"","firstName":"Murodillo","middleName":"","lastName":"Saidov","suffix":""},{"id":565157721,"identity":"ee2ee3e9-0940-495b-a091-4bec611dd116","order_by":4,"name":"Nodirjon Ruzimurodov","email":"","orcid":"","institution":"Institute of Immunology and Human Genomics of the Academy of Sciences of the Republic of Uzbekistan","correspondingAuthor":false,"prefix":"","firstName":"Nodirjon","middleName":"","lastName":"Ruzimurodov","suffix":""},{"id":565157722,"identity":"a282faab-5f5b-4cce-96a8-63abaad13b03","order_by":5,"name":"Olimjan Nazirkulov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3RsQrCMBCA4SuBdIl2VVp8hopQFVFfpRDQRXfBwU666O6TZG4I2CXg2tEiONfRzRvclBg3h/yBTPeRgwC4XH9ajicCIPJSA7CmLWEAlHePSKjtO0hYEuINX0nfLyq10ooFrRnvjReTiELj1jKR4T5NpS4Vax+5vC4Fx8X8xEjiPM1lVisW6xPvLQVBQqmZnKvsRXQSDsXGgpQcZIaLxcU+CT2hbMgNiZ6z9m7LuwdRMEooGZgXm1/v2WnUCQh+5UOsp4G/9UoT+RD5cd7lcrlc7z0Bg4FGV/sb8i4AAAAASUVORK5CYII=","orcid":"","institution":"Institute of Immunology and Human Genomics of the Academy of Sciences of the Republic of Uzbekistan","correspondingAuthor":true,"prefix":"","firstName":"Olimjan","middleName":"","lastName":"Nazirkulov","suffix":""}],"badges":[],"createdAt":"2025-12-18 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1","display":"","copyAsset":false,"role":"figure","size":13277,"visible":true,"origin":"","legend":"\u003cp\u003eMean age (years) of obese and non-obese patients with newly diagnosed T2DM\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8395890/v1/e5d06c1babf3ff15dbb1151c.png"},{"id":99223472,"identity":"2722ea63-f01f-4f94-8661-e43d2d51d45b","added_by":"auto","created_at":"2025-12-30 10:00:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19481,"visible":true,"origin":"","legend":"\u003cp\u003eMean level of the FPG among the obese and non-obese patients with newly diagnosed T2DM\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8395890/v1/7011fcb9262b372e4e5beb9b.png"},{"id":99318853,"identity":"f373339c-c707-42e5-9458-b104489ef56e","added_by":"auto","created_at":"2025-12-31 16:35:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26856,"visible":true,"origin":"","legend":"\u003cp\u003ePositive correlation between WC (cm) and FPG (mmol/L), OGTT (mmol/L) among the newly diagnosed non-obese T2DM patients\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8395890/v1/42a8090c6d3a31fe9983b3ed.png"},{"id":102235518,"identity":"2b48abd9-2ac3-4a15-ac23-62b7ecdb1e5a","added_by":"auto","created_at":"2026-02-09 16:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":891315,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8395890/v1/7316846d-ca5f-4a1f-abf4-06a843c086e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Central obesity and glycaemic control in newly diagnosed type 2 diabetes mellitus patients: evidence from Uzbekistan","fulltext":[{"header":"Background","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is one of the significant global health concerns, currently affecting more than 537\u0026nbsp;million adults worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Factors such as sedentary lifestyle, unhealthy diet, smoking, alcohol consumption, and increasing obesity have been identified as significant contributors to the rising prevalence of T2DM and early insulin resistance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Importantly, preventable risk factors, particularly central or visceral obesity, have been shown to increase the risk of early hyperglycaemia, notably among individuals with a normal body mass index (BMI) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, non-modifiable factors such as genetic predisposition, age, and male sex are also recognised contributors to the development of T2DM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Central Asia, including Uzbekistan, the prevalence of T2DM is relatively high, currently estimated at approximately 7.5% and projected to increase to 7.6% by 2050 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This trend poses a substantial burden on the healthcare system, particularly given the high prevalence of overweight (approximately 50%) and obesity (20%) among adults aged 18\u0026ndash;64 years. Furthermore, behavioural risk factors prevalent in Uzbekistan, including unhealthy lifestyles, high-salt diets, and alcohol consumption, further hinder diabetes prevention efforts and worsen glycaemic control. Demographic characteristics, especially population ageing, also play an essential role, with stronger associations reported from the age of 45 years onwards [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObesity remains a major modifiable risk factor for T2DM, and several studies have demonstrated associations between anthropometric indicators such as waist circumference (WC) and waist-to-hip ratio (WHR) and glycaemic control parameters [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similarly, BMI, body shape index, and visceral fat index have been reported to be positively associated with fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and glycated haemoglobin (HbA1c) levels in both prediabetic and diabetic individuals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence further suggests that central obesity is more strongly associated with early hyperglycaemia than BMI alone. In non-obese individuals, particularly among Asian populations, T2DM may develop despite a normal BMI, a phenomenon attributed to disproportionate fat accumulation in the abdominal region [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies from East Asia indicate that excess visceral fat is a more important predictor of insulin resistance than elevated BMI.\u003c/p\u003e \u003cp\u003eThe pathophysiological mechanisms linking central adiposity to early hyperglycaemia are well established. Visceral adipose tissue is metabolically active and produces pro-inflammatory cytokines, such as interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α), which contribute to hepatic insulin resistance and pancreatic β-cell stress [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These inflammatory processes promote the progression from normoglycaemia to prediabetes and eventually overt T2DM, even in individuals without overt obesity.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated strong associations between central obesity, particularly WC, and glycaemic control parameters, including FPG, OGTT, and HbA1c, among prediabetic and diabetic populations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, data focusing specifically on newly diagnosed obese and non-obese T2DM patients remains limited, particularly in Uzbekistan.\u003c/p\u003e \u003cp\u003eUnderstanding glycaemic impairment in relation to anthropometric characteristics has important implications for diabetes prevention, especially in low- and middle-income countries with limited healthcare resources [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Identifying these relationships in newly diagnosed patients may also facilitate early intervention and more effective organisation of treatment strategies, ultimately improving patient care [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccordingly, the present study aimed to evaluate the association between anthropometric measurements (BMI and WC) and glycaemic control indicators (FPG, OGTT, and HbA1c) among newly diagnosed T2DM patients in Uzbekistan.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy location and study design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis collaborative cross-sectional study was conducted at the Institute of Immunology and Human Genomics of the Academy of Sciences of the Republic of Uzbekistan and the Samarkand Branch of the Republican Scientific and Practical Medical Centre for Specialised Endocrinology named after Academician Y.H. Turakulov. Demographic, anthropometric, and clinical data were collected at the Samarkand Branch of the Republican Scientific and Practical Medical Centre for Specialised Endocrinology between 1 January 2025 and 28 February 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy population and data collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 104 patients with newly diagnosed type 2 diabetes mellitus (T2DM) were included in the study. Patients were diagnosed according to national clinical standards, based on World Health Organisation (WHO) and American Diabetes Association (ADA) recommendations. Diagnostic criteria included fasting plasma glucose (FPG) \u0026ge;7.0 mmol/L, 2-hour oral glucose tolerance test (OGTT) glucose \u0026ge;11.1 mmol/L, glycated haemoglobin (HbA1c) \u0026ge;6.5%, or random plasma glucose \u0026ge;11.1 mmol/L in the presence of classic symptoms (polydipsia, polyuria, and weight loss) [16].\u003c/p\u003e\n\u003cp\u003eData were obtained from medical records using consecutive sampling and included age, sex, body weight, height, WC, HbA1c, OGTT, and FPG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e were as follows:\u003cbr\u003e\u0026nbsp;\u0026ndash; untreated, newly diagnosed T2DM patients\u003cbr\u003e\u0026nbsp;\u0026ndash; age \u0026ge;18 years\u003cbr\u003e\u0026nbsp;\u0026ndash; both sexes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e included:\u003cbr\u003e\u0026nbsp;\u0026ndash; age \u0026lt;18 years\u003cbr\u003e\u0026nbsp;\u0026ndash; type 1 diabetes mellitus or gestational diabetes\u003cbr\u003e\u0026nbsp;\u0026ndash; previously treated or undertreated T2DM\u003cbr\u003e\u0026nbsp;\u0026ndash; chronic diseases in a decompensated stage that could affect blood glucose levels\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI) was calculated as weight (kg) divided by height squared (m\u0026sup2;). For descriptive analysis, BMI was categorised according to WHO criteria as underweight (\u0026lt;18.5 kg/m\u0026sup2;), normal weight (18.5\u0026ndash;24.9 kg/m\u0026sup2;), overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;), obesity class I (30.0\u0026ndash;34.9 kg/m\u0026sup2;), obesity class II (35.0\u0026ndash;39.9 kg/m\u0026sup2;), and obesity class III (\u0026ge;40.0 kg/m\u0026sup2;) [17]. Laboratory measurements of HbA1c, OGTT, and FPG were performed using instruments compliant with Uzbekistan\u0026apos;s national treatment standards.\u003c/p\u003e\n\u003cp\u003eFor comparative analyses, patients were classified according to WHO criteria as\u003c/p\u003e\n\u003cp\u003e\u0026frac34; \u003cstrong\u003eObese:\u003c/strong\u003e BMI \u0026ge;30.0 kg/m\u0026sup2;\u003c/p\u003e\n\u003cp\u003e\u0026frac34; \u003cstrong\u003eNon-obese:\u003c/strong\u003e BMI \u0026lt;30.0 kg/m\u0026sup2;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive analyses were initially performed using Microsoft Excel, and inferential statistical analyses were subsequently conducted using JASP software. Continuous variables were first assessed for normality using the Shapiro\u0026ndash;Wilk test. Normally distributed data were expressed as mean \u0026plusmn; standard deviation (SD) and compared between groups using an independent Student\u0026rsquo;s t-test. Non-normally distributed variables were presented as median (interquartile range, IQR) and compared using the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test. Categorical variables were analysed using the chi-squared test and presented as frequencies and percentages.\u003c/p\u003e\n\u003cp\u003eGiven the relatively small sample size and non-normal distribution of some variables, Spearman\u0026rsquo;s correlation analysis was used. Given significant age differences between obese and non-obese groups, age-adjusted partial Spearman correlation analyses were performed to assess the relationships between anthropometric variables (BMI and WC) and glycaemic control indicators (FPG, OGTT, and HbA1c). Correlation analyses were subsequently conducted separately for obese and non-obese groups. All statistical analyses were performed using JASP software. Statistical significance was defined as a p-value \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of newly diagnosed T2DM(N\u0026thinsp;=\u0026thinsp;104)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.07\u0026thinsp;\u0026plusmn;\u0026thinsp;11.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.4 [26.53\u0026ndash;30.28]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC, cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI Category, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Normal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Overweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Obesity Class I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Obesity Class II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Obesity Class III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2 [8.6\u0026ndash;9.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG, mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGTT, mmol/L, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.45 [12.75\u0026ndash;16.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 104 newly diagnosed patients with T2DM were included in the study. The mean age was 52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 years, and men comprised a higher proportion of the sample (n\u0026thinsp;=\u0026thinsp;64, 61.5%) than women (n\u0026thinsp;=\u0026thinsp;40, 38.5%). Regarding anthropometric characteristics, the median body mass index (BMI) was 28.4 [26.53\u0026ndash;30.28] kg/m\u0026sup2;, and the mean WC was 99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65 cm. Among the study population, 60.9% (n\u0026thinsp;=\u0026thinsp;63) were overweight, while 28.8% (n\u0026thinsp;=\u0026thinsp;30) had obesity. Specifically, 17.3% (n\u0026thinsp;=\u0026thinsp;18) had obesity class I, 10.6% (n\u0026thinsp;=\u0026thinsp;11) had obesity class II, and approximately 1% (n\u0026thinsp;=\u0026thinsp;1) had obesity class III. Only 10.6% (n\u0026thinsp;=\u0026thinsp;11) of patients had a normal BMI. The median HbA1c was 9.2 [8.6\u0026ndash;9.8] %, the mean fasting plasma glucose (FPG) level was 12.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02 mmol/L, and the median 2-hour OGTT value was 14.45 [12.75\u0026ndash;16.15] mmol/L (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison of obese and non-obese patients\u003c/h2\u003e \u003cp\u003ePatients were categorised into obese (n\u0026thinsp;=\u0026thinsp;30) and non-obese (n\u0026thinsp;=\u0026thinsp;74) groups. A statistically significant age difference was observed between the two groups. Obese patients were younger, with a mean age of 47.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32 years, compared with 53.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05 years in the non-obese group (p\u0026thinsp;=\u0026thinsp;0.014) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of obese and non-obese patients with newly diagnosed T2DM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese n\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-obese n\u0026thinsp;=\u0026thinsp;74\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male) n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.494*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.2 [29.45\u0026ndash;35.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5 [25.3\u0026ndash;29.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2 [8.75\u0026ndash;9.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.15 [8.54\u0026ndash;9.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.246***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGTT, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.50 [12.78\u0026ndash;16.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.35 [12.66\u0026ndash;16.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *p-value derived from the Chi-squared test, **p-value derived from the independent Student\u0026rsquo;s t-test, ***p-value derived from the Mann\u0026ndash;Whitney U test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe male-to-female ratio did not differ significantly between groups. Men accounted for 66.7% (n\u0026thinsp;=\u0026thinsp;20) of the obese group and 59.5% (n\u0026thinsp;=\u0026thinsp;44) of the non-obese group (p\u0026thinsp;=\u0026thinsp;0.494) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs expected, significant differences were observed in anthropometric parameters. The median BMI was higher in the obese group (32.2 [29.45\u0026ndash;35.15] kg/m\u0026sup2;) than in the non-obese group (27.5 [25.3\u0026ndash;29.8] kg/m\u0026sup2;; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, mean WC was significantly greater among obese patients (104.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29 cm) compared with non-obese patients (97.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58 cm; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHbA1c levels were slightly higher in the obese group (median 9.2 [8.75\u0026ndash;9.65] %) than in the non-obese group (9.15 [8.54\u0026ndash;9.8] %); however, this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.246) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFPG levels were significantly higher in obese patients (13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97 mmol/L) than in non-obese patients (11.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94 mmol/L; p\u0026thinsp;=\u0026thinsp;0.02), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, median OGTT values were similar between the obese and non-obese groups (14.50 [12.78\u0026ndash;16.23] vs. 14.35 [12.66\u0026ndash;16.04] mmol/L, respectively) and did not differ significantly (p\u0026thinsp;=\u0026thinsp;0.243) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation between anthropometric measurements and glycaemic control indicators\u003c/h3\u003e\n\u003cp\u003eCorrelation analysis among all newly diagnosed T2DM patients demonstrated a weak but statistically significant positive association between BMI and FPG (ρ\u0026thinsp;=\u0026thinsp;0.212, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant correlations were observed between BMI and other glycaemic indicators (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWC showed significant positive correlations with both FPG (ρ\u0026thinsp;=\u0026thinsp;0.251, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and OGTT (ρ\u0026thinsp;=\u0026thinsp;0.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Next, partial correlation analysis was performed separately\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePartial correlations between anthropometric measurements and glycaemic variables among the newly diagnosed T2DM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWC, cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFPG, mmol/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOGTT, mmol/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHbA1c,\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.588***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.251*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGTT, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.791***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.567***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.529***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote. Spearman correlation was used\u0026mdash;partial correlations adjusted for age. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn obese and non-obese patients. The analysis showed that, among non-obese newly diagnosed T2DM patients, weak but statistically significant correlations were observed between WC and FPG (ρ\u0026thinsp;=\u0026thinsp;0.236, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as weak to moderate correlations between WC and OGTT (ρ\u0026thinsp;=\u0026thinsp;0.297, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;4) (Fig.\u0026nbsp;3).\u003c/p\u003e \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"500\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 500px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4. Partial correlations between anthropometric measurements and glycaemic variables among newly diagnosed non-obese patients with T2DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eWC, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFPG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eOGTT, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eHbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003eWC, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.375**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003eFPG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.236*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOGTT, \u0026nbsp;mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.297*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.843***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003eHbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.564***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.503***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 500px;\"\u003e\n \u003cp\u003eNote. Spearman correlation was used\u0026mdash;partial correlations adjusted for age. *p \u0026lt; .05, ** p \u0026lt; .01, *** p \u0026lt; .001.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003cbr\u003e\u003cp\u003eHowever, in obese patients, no significant correlations were identified between anthropometric indicators and glycaemic control parameters.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study of 104 newly diagnosed patients with type 2 diabetes mellitus (T2DM), the mean age at diagnosis was 52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 years, indicating an earlier onset than estimates from global pooled analyses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the mean age at diagnosis reported in developed countries, such as Germany (61.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 years), is notably higher than that observed in our study, suggesting regional variation in the onset of T2DM [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Differences in genetic background, dietary patterns, socioeconomic conditions, and healthcare access may contribute to these variations.\u003c/p\u003e \u003cp\u003eMen accounted for 61.2% of cases in the present study, indicating a higher burden of T2DM among males. Sex-specific analysis further showed that men were at greater risk than women, which is consistent with global evidence demonstrating a higher incidence and earlier onset of T2DM among males, often at a lower BMI compared with females [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These findings highlight the importance of considering age and sex when designing strategies for T2DM prevention and management.\u003c/p\u003e \u003cp\u003eIn this cohort, overweight individuals constituted 60.9% of newly diagnosed T2DM cases, indicating that excess body weight is a major contributor to the early development of T2DM. This proportion is higher than that reported in some low-income settings; for example, studies from South Africa have documented an overweight prevalence of approximately 34% among newly diagnosed T2DM patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Such differences may reflect variations in lifestyle, urbanisation, dietary habits, and regional obesity trends.\u003c/p\u003e \u003cp\u003eA significant age difference was observed between obese and non-obese patients, with obese individuals being younger (47.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32 years) than non-obese patients (53.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05 years). This finding suggests that obesity may accelerate the onset of T2DM, with younger obese individuals developing glucose dysregulation earlier in life. Similar age-related differences between obese and non-obese patients have been reported previously [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, T2DM in older non-obese individuals may be driven by age-related declines in β-cell function or other metabolic alterations independent of adiposity. These observations underscore the importance of preventing obesity at younger ages to reduce the risk of early-onset T2DM.\u003c/p\u003e \u003cp\u003eFurthermore, fasting plasma glucose (FPG) levels were lower in non-obese patients (11.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94 mmol/L) than in obese patients (13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97 mmol/L). This difference may be explained by earlier and more pronounced glycaemic dysregulation in obese individuals, driven by excess adiposity, insulin resistance, and impaired glucose uptake, leading to elevated fasting glucose levels, as previously reported [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, although non-obese patients had lower fasting plasma glucose levels, waist circumference remained significantly associated with indicators of glycemic control. This suggests that central obesity may play a significant role in impaired glucose regulation, despite these patients not being obese by BMI. Age-related visceral fat redistribution may, to some extent, help to clarify these findings, especially among older patients who are not obese. These findings are consistent with existing evidence indicating that waist circumference may be a more important predictor of impaired glucose regulation than BMI, and emphasise the need to consider measuring central obesity in patients with newly diagnosed T2DM [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eLimitations of the current study include its cross-sectional design and relatively small sample size. In addition, the study was conducted in a single hospital, which may limit the generalizability of the findings across Uzbekistan. Despite these limitations, we performed the statistical analyses rigorously to ensure the reliability of the results. Although this study is ongoing, baseline characteristics are reported, and immunological and pharmacogenetic analyses are planned for this sample to provide a deeper exploration of glycaemic resistance mechanisms among newly diagnosed T2DM patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this cross-sectional study of newly diagnosed patients with type 2 diabetes mellitus in Uzbekistan, older adults were more likely to be classified as non-obese according to BMI. Nevertheless, waist circumference remained significantly associated with poor glycaemic control in this group. These findings indicate that central obesity is a key determinant of glucose dysregulation, even among patients who are non-obese by BMI criteria. Incorporating measures of central obesity into routine screening may enhance the detection and management of type 2 diabetes mellitus, particularly in geriatric populations.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eWHO, World Health Organisation;\u003c/p\u003e \u003cp\u003eT2DM, type 2 diabetes mellitus;\u003c/p\u003e \u003cp\u003eBMI, body mass index;\u003c/p\u003e \u003cp\u003eWC, waist circumference;\u003c/p\u003e \u003cp\u003eWHR, waist-to-hip ratio;\u003c/p\u003e \u003cp\u003eFPG, fasting plasma glucose;\u003c/p\u003e \u003cp\u003eOGTT, oral glucose tolerance test;\u003c/p\u003e \u003cp\u003eHbA1c, glycated haemoglobin\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study protocol was reviewed and approved by the Institutional Review Board of the Institute of Immunology and Human Genomics, Academy of Sciences of the Republic of Uzbekistan, in accordance with the Bioethics and Safety Act (IRB No: 2025-0002). The study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to inclusion in the study.\u003c/p\u003e \u003ch2\u003eClinical trial registration\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study did not receive any specific funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShZ and ON conceptualised the study. ShE collected the data. ON drafted the manuscript. NR, JK and MS reviewed the manuscript. ShZ supervised the study.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the medical staff for their assistance with patient recruitment and data collection and acknowledge the organisational and technical support of Negmatova Gulzoda Shukhratovna, Director of the Samarkand Branch of the Republican Scientific and Practical Medical Centre for Specialised Endocrinology named after Academician Y.H. Turakulov.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLin X, Xu Y, Pan X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020;10:14790. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-71908-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-71908-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalzberg L. Risk factors and lifestyle interventions. Prim Care. 2022;49(2):201\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pop.2021.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.pop.2021.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong P, Tan S, Zhu Z, Liang Y, Huang W, Wang W. Normal-weight central obesity and risk of cardiovascular and microvascular events in adults with prediabetes or diabetes: Chinese and British cohorts. Diabetes Metab Res Rev. 2023;39(8):e3707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/dmrr. 3707\u003c/span\u003e\u003cspan address=\"10.1002/dmrr. 3707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFletcher B, Gulanick M, Lamendola C. Risk factors for type 2 diabetes mellitus. J Cardiovasc Nurs. 2002;16(2):17\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00005082-200201000-00003\u003c/span\u003e\u003cspan address=\"10.1097/00005082-200201000-00003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHivert MF, Vassy JL, Meigs JB. Susceptibility to type 2 diabetes mellitus\u0026mdash;from genes to prevention. Nat Rev Endocrinol. 2014; 10 (4):198\u0026ndash;205.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrendo\u003c/span\u003e\u003cspan address=\"10.1038/nrendo\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2014.11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Diabetes Federation. Uzbekistan diabetes data and trends. IDF Diabetes Atlas. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlieva A, Alimov A, Khaidarova F, Ismailov S, Rakhimova G, Nazhmutdinova D, et al. Assessing the effectiveness of type 2 diabetes screening in the Republic of Uzbekistan. Int J Endocrinol Metab. 2022;20(4):e124036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5812/ijem-124036\u003c/span\u003e\u003cspan address=\"10.5812/ijem-124036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChikani V, Jeyaseelan L, Saravanan R. Association of glycaemic control and anthropometric measurements among type 2 diabetes mellitus patients. J Fam Med Prim Care. 2021;10(8):2942\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/jfmpc.jfmpc_1941_20\u003c/span\u003e\u003cspan address=\"10.4103/jfmpc.jfmpc_1941_20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurniawan LB, Apriani LP, Kadir NA, Nurahmi N, Basri MI, Aminuddin A. Visceral fat as the main obesity index that determines the occurrence of adipose tissue insulin resistance. EJIFCC. 2025;36(3):321\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin X, Liu J, Cao Q, Lin J, Wu G, Liu L, et al. Normal-weight central obesity: implications for diabetes mellitus. Front Nutr. 2023;10:1239493. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2023.1239493\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2023.1239493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSethi JK, Hotamisligil GS. Metabolic messengers: tumour necrosis factor. Nat Metab. 2021;3:1302\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42255-021-00470-z\u003c/span\u003e\u003cspan address=\"10.1038/s42255-021-00470-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaderegew MM, Emeria MS, Zegeye B. Association of glycaemic control and anthropometric measurement among type 2 diabetes mellitus: a cross-sectional study. Diabetol Int. 2021;12(4):356\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13340-021-00490-w\u003c/span\u003e\u003cspan address=\"10.1007/s13340-021-00490-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimmons SS. Biomarkers and anthropometric measures for predicting diagnosed diabetes mellitus in adults in low- and middle-income countries. Heliyon. 2023;9(9):e19494. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2023.e19494\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e19494\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlimov AV, Khaidarova FA, Ismailov SI, Rakhimova GN, Nazhmutdinova DK, Shagazatova BK, et al. Cost-effective screening model for type 2 diabetes in the Republic of Uzbekistan. TBEM. 2021;14(3):2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.54185/TBEM/vol14_iss3/a2\u003c/span\u003e\u003cspan address=\"10.54185/TBEM/vol14_iss3/a2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Zheng X, Wen X, Zhong J, Zhou Y, Xu L. Visceral fat correlates with insulin secretion and sensitivity independent of BMI and subcutaneous fat in Chinese with type 2 diabetes. Front Endocrinol (Lausanne). 2023;14:1144834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2023.1144834\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1144834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Diabetes Association Professional Practice Committee. Diagnosis and classification of diabetes: standards of care in diabetes\u0026mdash;2024. Diabetes Care. 2024;47(Suppl 1):S20\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc24-S002\u003c/span\u003e\u003cspan address=\"10.2337/dc24-S002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organisation. Obesity: preventing and managing the global epidemic. WHO Technical Report Series 894. Geneva: WHO; 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarrillo-Larco RM, Guzm\u0026aacute;n-Vilca WC, Xu X, Bernab\u0026eacute;-Ortiz A. Mean age and body mass index at type 2 diabetes diagnosis: pooled analysis of 56 health surveys. Diabet Med. 2024;41(2):e15174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/dme.15174\u003c/span\u003e\u003cspan address=\"10.1111/dme.15174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobs E, Rathmann W, T\u0026ouml;nnies T, Arendt D, Marchowez M, Veith L, et al. Age at diagnosis of type 2 diabetes in Germany: a nationwide analysis. Diabet Med. 2020;37(10):1723\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/dme.14100\u003c/span\u003e\u003cspan address=\"10.1111/dme.14100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKautzky-Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia. 2023;66(6):986\u0026ndash;1002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00125-023-05891-x\u003c/span\u003e\u003cspan address=\"10.1007/s00125-023-05891-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMphasha MH, Skaal L, Mothiba TM. Prevalence of overweight and obesity amongst patients with diabetes and their non-diabetic family members in Limpopo province, South Africa. S Afr Fam Pract. 2022;64(1):e1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4102/safp.v64i1.5409\u003c/span\u003e\u003cspan address=\"10.4102/safp.v64i1.5409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHillier TA, Pedula KL. Characteristics of an adult population with newly diagnosed type 2 diabetes: relation of obesity and age of onset. Diabetes Care. 2001;24(9):1522\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diacare.24.9.1522\u003c/span\u003e\u003cspan address=\"10.2337/diacare.24.9.1522\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrati M, Moustaki M, Psaltopoulou T, Vryonidou A, Paschou SA. Early onset type 2 diabetes mellitus: an update. Endocrine. 2024;85(3):965\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12020-024-03772-w\u003c/span\u003e\u003cspan address=\"10.1007/s12020-024-03772-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaudhari A, Gujarathi S, Bhatia G. Comparison of blood glucose in obese and non-obese students in a medical college. Int J Res Med Sci. 2020;8(11):3916\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18203/2320-6012.ijrms20204877\u003c/span\u003e\u003cspan address=\"10.18203/2320-6012.ijrms20204877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSannasi L. Comparison of waist circumference in type 2 diabetes and non-diabetic population. Glob J Med Pharm Biomed Update. 2025;20:4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.25259/GJMPBU_1_2025\u003c/span\u003e\u003cspan address=\"10.25259/GJMPBU_1_2025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"newly diagnosed, type 2 diabetes mellitus, non-obese, central obesity, glycaemic control, fasting plasma glucose, Uzbekistan","lastPublishedDoi":"10.21203/rs.3.rs-8395890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8395890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eObesity is a well-known risk factor for early glycaemic dysregulation. In contrast, among non-obese patients (normal weight or overweight), glycaemic disturbances may remain under-recognised. This study aimed to evaluate the association between anthropometric measures and glycaemic control indicators in newly diagnosed, untreated obese and non-obese patients with type 2 diabetes mellitus (T2DM) in Uzbekistan.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among 104 untreated, newly diagnosed T2DM patients at the Samarkand branch of the Republican Specialised Endocrinology Hospital, Uzbekistan. Body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and glycated haemoglobin (HbA1c) were recorded. Patients were categorised as obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) or non-obese (BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u0026sup2;). Independent Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests and Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e tests were used to compare variables between groups. Age-adjusted partial Spearman correlations were applied to evaluate associations between anthropometric parameters and glycaemic markers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 104 newly diagnosed T2DM patients (mean age 52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 years; 61.5% men), with a median BMI of 28.4 [26.53\u0026ndash;30.28] kg/m\u0026sup2; and a mean WC of 99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65 cm. Obese (\u0026ge;\u0026thinsp;30.0 kg/m\u0026sup2;) patients (n\u0026thinsp;=\u0026thinsp;30) were significantly younger (47.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32 years) than non-obese patients (n\u0026thinsp;=\u0026thinsp;74; 53.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05 years; p\u0026thinsp;=\u0026thinsp;0.014), and FPG levels were significantly higher in obese patients (13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97 mmol/L) than in non-obese patients (11.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94 mmol/L; p\u0026thinsp;=\u0026thinsp;0.02). Age-adjusted partial Spearman correlation analysis in all patients showed positive associations between BMI and FPG (ρ\u0026thinsp;=\u0026thinsp;0.212, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and between WC and both FPG and OGTT, with weak-to-moderate correlations (ρ\u0026thinsp;=\u0026thinsp;0.251 and 0.23, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In non-obese patients, WC was weakly to moderately positively correlated with FPG and OGTT (ρ\u0026thinsp;=\u0026thinsp;0.236 and ρ\u0026thinsp;=\u0026thinsp;0.297, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas no significant correlations were observed in obese patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlthough waist circumference was elevated among non-obese (\u0026lt;\u0026thinsp;30.0 kg/m\u0026sup2;) patients (97.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58 cm), it showed only a weak positive correlation with glycaemic indicators. These findings suggest that central obesity may play a modest but clinically relevant role in the early identification of glycaemic dysregulation among non-obese individuals.\u003c/p\u003e","manuscriptTitle":"Central obesity and glycaemic control in newly diagnosed type 2 diabetes mellitus patients: evidence from Uzbekistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 10:00:42","doi":"10.21203/rs.3.rs-8395890/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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