Insulin Resistance-Related Cardiometabolic Risk Among Nondiabetic Childbearing Age Females

Abstract

Background:

This study evaluates insulin resistance prevalence in young females without diabetes, assessing risk factors and adiposity indices for early detection of cardiometabolic disorders.

Methods:

A cross-sectional study was conducted, involving 282 females aged 18–35 years from suburban and rural areas of Sri Lanka. Anthropometric measurements [height, weight, waist circumference (WC)] were obtained and biochemical parameters [fasting glucose, insulin, insulin resistance (IR), total cholesterol, high-density lipoproteins, (HDL), low-density lipoproteins, triglycerides] were measured. The anthropometric and biochemical data were compared between the groups of normal weight controls and overweight/obese cases, as well as between females with or without IR.

Results:

The prevalence of IR in controls and cases were 48.6% and 57.1%, respectively. Both groups had mean Homeostasis Model Assessment-IR values greater than the normal cutoff value of 2.5. Females with IR showed higher prevalence of dyslipidemia than those without IR. Compared to the controls (2.81%), the prevalence of metabolic syndrome (MetS) was substantially greater among cases (46.42%). Both groups showed a statistically significant association between IR and MetS, but the association was considerably stronger in cases [r = 0.616, odds ratio (OR) >8] than in controls (r = 0.175, OR >1). Controls exhibited lower HDL levels, hypertriglyceridemia, and elevated IR levels (P < 0.05), and their ORs for acquiring MetS were >2, <1, and >3, respectively. Importantly, overweight/obese cases exhibited a significant association (P < 0.05) with all the MetS risk variables. Visceral adiposity index (VAI) proves to be a precise measurement for identifying IR and cardiovascular disease (CVD) among young females (Z = −3.651), surpassing the accuracy of other indices. Body mass index, body round index, a body shape index, and WC were also reliable measurements to assess IR and the risk of CVD (P < 0.05).

Conclusion:

The study underscores the importance of assessing IR in nondiabetic young females to identify early cardiometabolic risks. VAI emerges as a precise measurement for identifying IR and CVD risk, surpassing the accuracy of other indices.

Introduction

Insulin resistance (IR) is defined as a diminished physiologic response of target tissues, notably the liver, muscle, and adipose tissue, to insulin stimulation. As a result of impaired glucose elimination brought on by IR, beta cells produce more insulin as a coping mechanism, leading to hyperinsulinemia.^1,2 Hyperglycemia, hypertension, abdominal adiposity, an increased risk of obesity, and dyslipidemia are metabolic complications associated with IR that are also hallmarks of the metabolic syndrome (MetS).³ The development of IR may result in MetS, which eventually results in the emergence of cardiometabolic disorders.⁴ It is well known that IR is associated with elevated risks in developing cardiovascular events. However, the development of these cardiometabolic incidences among young females of childbearing age, who have IR with the absence of diabetes mellitus (DM), has not been well addressed.

One of the primary manifestations of IR is excessive body fat, and increase in weight and high daily insulin requirements over extended periods of time are frequently linked to unfavorable changes in cardiovascular disease (CVD) risk factors.⁵ The body mass index (BMI) is a widely used tool to measure weight relative to height. It is used by the World Health Organization (WHO) to classify adults as underweight, normal, overweight, or obese. However, it is important to note that BMI primarily measures excess weight in relationship to height rather than excess body fat.⁶ To identify abdominal obesity, the WHO and several researchers have developed a few more relevant metrics, such as waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR).^7
–9

Furthermore, a body shape index (ABSI) was developed independently of BMI to measure abdominal obesity.¹⁰ Increased adipocytokine production, proinflammatory activity, decreased insulin sensitivity, increased risk of developing diabetes, dyslipidemia, high blood pressure, CVD, and elevated mortality rate are all linked to visceral obesity.¹¹ The Visceral Adiposity Index (VAI) may be beneficial in identifying cardiometabolic risks in individuals with higher sensitivity and specificity than traditional criteria such as WC, BMI, and lipids.¹¹ In 2013, body round index (BRI) was developed by Diana M. Thomas and few studies have proposed that this can be used to identify cardiometabolic risks and DM in communities.¹² Likewise hip index (HI) was developed as an alternative to hip circumference (HC), the common measure of gluteofemoral adiposity that is unrelated to BMI, much like the ABSI.¹³

Since visceral fat is connected to cardiometabolic disorders, including IR, hypertension, and dyslipidemia,¹⁴ we evaluated all of these indices to evaluate their relationship with IR-induced cardiometabolic risks, which are further linked with abdominal and gluteofemoral adiposity.^15,16

It is generally known that people with IR and diabetes are at higher risk for unfavorable cardiovascular events and total mortality.^3,17,18 In addition, research has suggested that early onset IR is linked to an early and considerable propensity to CVD risk events later in life.^3,19,20 However, neither Sri Lankan populace nor other nations' communities have paid much attention to the modern trends of IR in females of childbearing age who are nondiabetic. In addition, we considered the newly introduced anthropometric risk indices to evaluate their relationship with IR and its impact on cardiometabolic concerns. With the objective to assess the connection of measures of IR with obesity and the prevalence and development of cardiometabolic risk factors triggered by IR status, we examined young female participants without diabetes who were between the ages of 18 and 35 years.

Methods

In the time span from September 2020 to September 2022, a cross-sectional study was conducted, involving female participants recruited from various rural, suburban, and urban areas. The study encompassed both normal-weight individuals and those who were overweight or obese. A total of 282 female subjects aged between 18 and 35 years were included, comprising 142 normal controls and 140 overweight/obese cases, with the selection based on Asia–Pacific BMI criteria. The sample size was calculated applying the guidelines provided in Kelsey et al.'s Methods in Observational Epidemiology, Second Edition.²¹ The Ethics Review Committee of the Faculty of Medical Sciences at the University of Sri Jayewardenepura, Sri Lanka, granted approval for this study.

To ensure the validity of the study, certain criteria were set for the exclusion of participants. Females with malignant neoplasia, pregnant women, individuals taking steroid hormones, lithium, amiodarone, and anticonvulsants, as well as those with DM were not included. The collection of relevant demographic and clinical data was done through a questionnaire. The study adhered to the guidelines specified by the Declaration of Helsinki, and all volunteers were enrolled after obtaining informed written consent from each participant.

Following international recommendations, anthropometric measures were meticulously collected using calibrated equipment.²² Height, WC, and HC were measured using a nonstretchable, plastic flexible tape, ensuring precision up to the nearest 0.1 cm. For body weight determination, a digital weighing scale was utilized while participants were attired in light clothing, with readings recorded to the nearest 0.1 kg. For blood pressure assessment, participants were in a seated position and had rested for at least 5 min. The measurement was taken using the left arm at heart level, using an automated sphygmomanometer (Omron HEM-7111) operated by the researcher. Two readings were taken, with a 5 min interval, and the mean values were used for further analysis.

Blood samples were gathered from participants following aseptic protocols to evaluate fasting blood sugar (FBS), fasting insulin, and lipid profile on two separate days. On the first designated day, participants were instructed to fast for 8–10 hr, and 3 mL of blood was drawn to analyze FBS and fasting insulin levels. On another selected day, participants were required to fast for 12–14 hr, and 3 mL of venous blood was collected for lipid profile analysis. To maintain sample integrity, all collected samples were transported to the laboratory within 2 hr of each day's collection. After transport, the samples underwent centrifugation at 3500 rpm for 10 min to separate the serum. The serum was then carefully divided into Eppendorf tubes and stored in a −80°C freezer until they were ready for analysis.^23,24

The FBS and lipid profile samples were processed within 3 days of collection using the KONELAB-20XT analyzer, which was equipped with quality controls and calibrators from KONELAB-20XT BioLabo SAS, France. For the serum insulin test, a quantitative sandwich enzyme-linked immunosorbent assay method was used, utilizing the R&D Systems^® kit on the ThermoFisher Multiskan FC machine (Bio-Techne^® brand, United Kingdom). To evaluate the IR among participants, the Homeostasis Model Assessment-Insulin Resistance (HOMA-IR) was utilized.

Obese women were identified as those with a BMI above 23 kg/m², while normal women were characterized by a BMI below this threshold.²⁵ HOMA-IR was calculated using the following formula: HOMA-IR = [fasting glucose (mg/dL) × fasting insulin (lU/mL)]/405. The HOMA-IR score of ≥2.5 was used as the cutoff for defining IR.²⁶ MetS was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria.²⁷ As we excluded all the participants with diabetes, we used HOMA-IR to define the MetS.²⁸ Women were classified as obese if their WHR was ≥0.85 and their WC was ≥80 cm, whereas normal individuals were those whose WHR was <0.85 and WC was <80 cm.²⁹ A WHtR cutoff of 0.5 was used as it has been suggested to be applicable to people of all racial and ethnic backgrounds, in both children and adults.³⁰

Subjects who scored higher than the cutoff point (0.076) for the ABSI were classified as obese.^31,32 For ages under 30, the ideal VAI cutoff value was 2.52.³³ The HI ≥0.64 was deemed the low-risk category when evaluating gluteofemoral adiposity.³⁴ Hypertension was defined as systolic blood pressure >130 mmHg and/or a diastolic blood pressure >85 mmHg. Serum total cholesterol levels >240 mg/dL was used to define hypercholestrolemia, and HDL <40 mg/dL, triglycerides >200 mg/dL, and LDL >190 mg/dL were considered as dyslipidaemia.^35,36 Based on a number of risk factors, an anthropometric risk indicator (ARI) was used to determine each person's mortality risk. This conclusion was reached by adding the log hazard ratios of BMI, ABSI, and HI for each risk factor. ARI of 0 (zero) denotes population-average risk, while the positive ARI denotes above-average risk, and negative ARI denotes below-average risk.¹³

The SPSS version 26.0 (SPSS Inc., Chicago, IL, USA) software was used for the statistical analysis. The test's variables were represented as mean ± standard deviation. Independent sample t-test was used to compare mean values of biochemical parameters between normal and overweight/obese groups. The chi-squared test was used to assess any significant association between categorical data of each group of normal and overweight/obese with selected NCD risk factors. A P value <0.05 was considered statistically significant.

Results

The mean values of biochemical parameters in both groups are described in Table 1.

Table 1.

The Mean Values (±Standard Deviation) of Biochemical Parameters of Normal and Overweight/Obese Groups and the Significant Association Between Both Groups. Blood Pressure, Anthropometric Parameters (Excluding A Body Shape Index and Hip Index), Insulin Resistance, and Lipid Profile Parameters Showed Significant Associations Between the Two Groups

Parameter	Normal(N = 142)	Overweight/obese (N = 140)	P
Age (years)	26.21 ± 4.26	28.21 ± 3.95	0.093
SBP (mmHg)	112.94 ± 10.33	121.05 ± 11.37	0.002^a
DBP (mmHg)	69.19 ± 9.11	75.82 ± 7.61	0.001^a
Height (cm)	157.89 ± 6.08	156.66 ± 5.00	0.160
Weight (kg)	51.05 ± 5.52	65.86 ± 7.38	0.010^a
BMI (kg/m²)	20.58 ± 1.67	26.87 ± 2.82	0.002^a
WC (cm)	69.27 ± 5.36	85.74 ± 7.78	0.001^a
HC (cm)	87.31 ± 7.29	99.74 ± 5.93	0.001^a
WHR	0.78 ± 0.05	0.84 ± 0.12	0.003^a
WHtr	0.44 ± 0.03	0.55 ± 0.04	0.001^a
ABSI	0.7469 ± 0.005	0.7774 ± 0.004	0.305
BRI	2.42 ± 0.55	4.49 ± 1.03	0.001^a
VAI	1.80 ± 0.80	2.56 ± 0.87	0.001^a
HI	101.59 ± 8.51	102.89 ± 4.31	0.171
Fasting serum glucose (mg/dL)	86.06 ± 8.43	84.80 ± 10.35	0.261
Fasting serum insulin (mIU/L)	12.32 ± 5.44	16.84 ± 6.75	0.001^a
HOMA-IR	2.68 ± 1.30	3.56 ± 1.57	0.001^a
HDL (mg/dL	47.31 ± 7.34	45.32 ± 7.04	0.021^a
LDL (mg/dL)	133.27 ± 42.51	157.03 ± 39.00	0.005^a
TG (mg/dL)	126.07 ± 34.95	165.96 ± 33.35	0.001^a
TC (mg/dL)	181.92 ± 44.94	207.55 ± 40.04	0.002^a

P < 0.05.

ABSI, a body shape index; BMI, body mass index; BRI, body round index; DBP, diastolic blood pressure; HC, hip circumference; HDL, high-density lipoproteins; HI, hip index HOMA-IR, homeostasis model assessment insulin resistance; LDL, low-density lipoproteins; SBP, systolic blood pressure; TC, total cholesterols; TG, triglycerides; VAI, visceral adiposity index; WC, waist circumference; WHR, waist-to-hip ratio; WHtr, waist-to-height ratio.

Both study groups demonstrated a higher prevalence of IR, however, the overweight/obese female participants displayed significantly more IR individuals than the participants who were of normal weight (Table 2).

Table 2.

Prevalence of Insulin Resistance Among Normal Weight and Overweight/Obese Females

IR status	Normal controls		Overweight/obese Cases
IR status	No. of individuals	Valid %	No. of individuals	Valid %
IR females	69	48.6	80	57.1
Non-IR females	73	51.4	60	42.9
Total	142	100	140	100

IR, insulin resistance.

Among study participants, we identified ∼54% of young females in childbearing age without diabetes who have IR. When they were classified as normal weight controls or overweight/obese cases based on their BMI values, the prevalence of IR was 48.6% and 57.1%, respectively. Importantly, both groups had mean HOMA-IR values greater than the normal cutoff value of 2.5 (normal weight controls = .68 ± 1.30, overweight/obese cases = 3.56 ± 1.57). IR was found to be significantly associated with various cardiometabolic risk factors, including BMI, ABSI, VAI, BRI, WC, HC, HDL, LDL, triglycerides, and total cholesterol levels.

When the relationship of IR to cardiometabolic risk factors, including nine categories of obesity (BMI, WC, HC, WHR, WHtR, ABSI, HI, BRI, VAI), blood pressure, and lipid profile parameters, was analyzed, it was found that multiple risk variables showed significant associations with IR (Tables 3 and 4).

Table 3.

Examining the Relationship Between Insulin Resistance and Cardiometabolic Risk Factors Using Anthropometric Measurements and Anthropometric-Combined Parameters

IR versus cardiometabolic risk factors
	BMI	WC	HC	WHR	WHtR	ABSI	HI	BRI	VAI
Z	–2.097	–2.405	–2.33	–1.633	–1.494	–2.082	–1.106	–2.739	–3.651
P	0.037^a	0.016^a	0.020^a	0.103^a	0.135	0.036^a	0.269	0.021^a	0.001^a

P < 0.05.

Table 4.

Examining the Relationship Between Insulin Resistance and Cardiometabolic Risk Factors Using Biochemical Parameters

IR versus cardiometabolic risk factors
	SBP	DBP	HDL	LDL	TG	TC
Z	–1.16	–1.114	–3.232	–3.95	–4.117	–3.935
P	0.246	0.265	0.001^a	0.001^a	0.001^a	0.001^a

P < 0.05.

The prevalence of cardiometabolic risk factors, based on the presence or absence of IR, was determined. Different obesity assessing indices and dyslipidemic variables exhibited a significant association with those risk factors in the presence of IR. The prevalence of obese subjects with IR was significantly higher compared to the prevalence of obese subjects with non-IR when considering the risk factors BMI, ABSI, VAI, BRI, WC, and WHR. The prevalence of dyslipidemic subjects with IR was significantly higher compared to the prevalence of dyslipidemic subjects without IR when considering low HDL, high LDL, high total cholesterol, and high triglycerides (Table 5).

Table 5.

Insulin Resistance-Based Stratification of the Prevalence (%) of Cardiometabolic Risk Factors Among Study Participants

	IR+ (N = 149), n (%)	IR– (N = 133), n (%)	P (IR+ vs. IR–)
Obesity
BMI (>23 kg/m²)	82 (55.03)	58 (43.60)	0.044^a
ABSI (>0.0762)	79 (54.10)	59 (44.36)	0.021^a
VAI (>2.25)	69 (46.30)	39 (29.32)	0.006^a
BRI (>5.16)	64 (42.95)	54 (40.60)	0.032^a
WC (>80 cm)	68 (45.63)	41 (30.82)	0.039^a
WHR (>0.85)	70 (46.97)	59 (44.36)	0.043^a
WHtR (>0.5)	70 (46.97)	68 (51.12)	0.159
HI (>0.64)	66 (44.29)	66 (49.62)	0.263
Hypertension	23 (15.43)	22 (16.54)	0.494
Dyslipidemia
Low HDL	112 (75.16)	88 (66.16)	0.048^a
High LDL	109 (73.15)	78 (58.64)	0.025^a
High TG	77 (51.67)	51 (38.34)	0.042^a
High TC	78 (52.34)	41 (30.82)	0.001^a

P < 0.05.

The study highlights that alteration of lipid profile parameters with the influence of IR occurs not only among overweight/obese cases but also among normal weight controls. All four lipid profile parameters (HDL, LDL, triglycerides, total cholesterol) were found to be abnormal among the cases. Moreover, abnormal HDL, LDL, and triglyceride levels were also revealed among normal weight controls. The prevalence of MetS occurrences was significantly higher among overweight/obese cases (46.42%) compared to the normal weight controls (2.81%). The association between IR and MetS was significant among both groups, but it was much stronger among cases [r = 0.616, odds ratio (OR) >8] than in controls who exhibited a weaker association (r = 0.175, OR >1). The normal weight controls exhibited lower HDL levels, hypertriglyceridemia, and elevated HOMA-IR levels (P < 0.05), and their ORs for acquiring MetS were >2, <1, and >3, respectively.

Importantly, overweight/obese cases exhibited a significant association (P < 0.05) with all the MetS risk variables: hypertension (r = 0.235, OR >2), low HDL (r = −0.365, OR >4), hypertriglyceridemia (r = 0.566, OR >3), HOMA-IR (r = 0.796, OR >8), and central obesity (r = 0.696, OR >5).

Apart from the above lipid profile, abnormalities based on IR were computed among the two study groups: normal weight controls and overweight/obese cases. Normal weight controls exhibited significant associations between IR and hypertriglyceridemia (r = 0.442, P = 0.001), reduced HDL levels (r = −0.354, P = 0.020), and elevated LDL levels (r = 0.357, P = 0.011). In the overweight/obese cases, there were significant associations between IR and all lipid profile parameters, including lower HDL (r = -0.568, P = 0.001), hypertriglyceridemia (r = 0.619, P = 0.001), elevated LDL (r = 0.362, P = 0.020), and elevated total cholesterol (r = 0.265, P = 0.010).

In the current study, both the normal weight controls and overweight/obese cases groups exhibited strong correlations between the HOMA-IR, lipid profile, and MetS with insulin resistant. The individuals with MetS had considerably higher levels of IR. Between the IR and MetS, the normal controls had a P-value of 0.037 and a rho of 0.175, whereas the overweight/obese individuals had values of 0.001 and 0.616. Furthermore, with P values <0.05 at a 95% confidence range, all lipid profile characteristics in both groups were substantially linked with IR. In addition, the odds ratio between IR and MetS was >1 in normal controls and >8 in individuals who were overweight or obese.

ARI was calculated for the HOMA-IR and other cardiometabolic risk factors among study participants using the log hazard ratios of BMI, ABSI, and HI of each risk variable (Table 6).

Table 6.

Anthropometric Risk Indicator of Study Participants for A Given Risk Factor

Risk factor	Anthropometric risk indicator
HOMA-IR	1.93
Hypertension	1.26
WC	2.62
Low HDL	2.33
High LDL	–1.59
High TG	2.53
High TC	–0.36
Dyslipidemia	2.03
Hypercholesterolemia	1.34

Discussion

Pathogenic mechanisms and associated risks for CVD can begin even before adulthood, and obesity linked to an abnormal lipid profile during puberty has been strongly connected to IR. As mentioned in the literature, several variables, including obesity, abnormal lipid profiles, and IR, play significant roles in the development of cardiometabolic risks.³ However, in the current study, we discovered that women of normal weight with IR, who do not have DM, also face a much higher risk of developing cardiometabolic disorders, similar to women who are overweight or obese.

The study emphasizes the importance of assessing adiposity in females without DM to identify IR at early stages, as obesity assessed through anthropometric indices (BMI, ABSI, VAI, BRI, WC, HC) showed significant associations with HOMA-IR. A study conducted by Parch et.al.⁴ also identified a modest continuous linear association between HOMA-IR and different anthropometrics (BMI, WC) among young females, which supports our findings. In addition, during the analysis, the presence or absence of IR based on cardiometabolic risk factors was assessed, and BMI, ABSI, VAI, BRI, WC, and WHR were effective indices in identifying IR among our study population [P < 0.05 (IR+ vs. IR–)].

VAI was recommended by Amato et al. as a useful tool for detecting IR,¹¹ and studies with DM patients have also supported the use of BRI and ABSI applications in identifying visceral adiposity independently from BMI.³⁷ Chang et al., on the contrary, noted that neither ABSI nor BRI are appropriate for diagnosing DM in their investigation.¹² WHtR and HI were not significant between the two groups with or without IR in the current study.

The prevalence of dyslipidemia among young females with IR was significantly higher compared to females without IR (P < 0.05). Females with IR demonstrated lower HDL levels, along with elevated LDL, triglycerides, and total cholesterol levels. This finding is in line with previous findings from different studies.^4,38 According to a study carried out in Taiwan,³⁹ the overall incidence of overweight and obesity at the initial check-up was 17.6% and 14.5%, respectively. In comparison to individuals with initial BMI <23.0 kg/m², obese individuals with initial BMI >25 kg/m² showed a considerable multivariate-adjusted RR (Relative Risk) of 2.7 for high blood pressure, 14.8 for T2DM, and 3.2 for hypertriglyceridemia. RR for hypertriglyceridemia was higher in men than in women, although RR for diabetes was more significant in women.

The study further confirmed that individuals with baseline BMI ≥23 kg/m² had considerably increased chances of high blood pressure, whereas baseline BMI ≥24 kg/m² subjects had considerably higher chances of type 2 diabetes, and baseline BMI ≥25 kg/m² respondents had considerably higher hazards of hypertriglyceridemia. Based on these findings, it is evident that our study participants also have a greater possibility of IR-related DM, dyslipidemia, elevated blood pressure, and central obesity as their BMI rises.

The pathogenesis of several metabolic diseases with IR in normal and obese persons, as well as the interference of IR toward the MetS, had been discussed in the Bruneck's study.⁴⁰ There were 888 individuals in all, and 65.9% of them had IR. In patients with MetS, 95.2% of subjects had IR. According to their findings, the vast majority of people who have several metabolic disorders are insulin resistant, and IR can occur in the general population even when there are no significant metabolic abnormalities present.

Similarly, the occurrence of dyslipidemia and hypercholesterolemia in the context of IR was investigated in the current study. Low-HDL 74.32%, High-LDL 72.29%, Hypertriglyceridemia 50%, and Hypercholesterolemia 48.61% were the results of the current study, where the prevalence of IR among MetS participants was 89.85%. Therefore, the results of the Bruneck study, which showed that ones with IR have a higher chance of developing MetS and CVD among risk groups, are in congruence with our study findings.

Further, this study presents ARIs for HOMA-IR and cardiometabolic risk factors among study participants. All cardiometabolic risk factors, except for LDL and total cholesterol levels, such as HOMA-IR, hypertension, WC, HDL, dyslipidemia, and hypertriglyceridemia, showed positive ARI values. This indicates that study participants were at an increased risk of developing cardiometabolic disorders. The results of our study are supported by Krakauer et al.'s and Krakauer examination, which found a positive association between all MetS risk variables and ARI. They suggested using these metrics in clinical settings to identify potential mortality hazards such as MetS, IR, obesity, CVD risks, and so on.⁴¹ In addition, other studies have also supported the use of ARI to predict morbidity, such as diabetes and CVD.^42,43

This study's primary limitations arise from the cross-sectional design, which makes it impossible to determine long-term trends or causal relationships. In addition, the well-researched HOMA-IR method was used in place of the gold standard test for identifying IR—the hyperinsulinemic euglycemic clamp—because it proved to be expensive, time-consuming, and inappropriate for this field of study. Furthermore, anthropometric measurements may have limitations because it is a relatively insensitive method that cannot identify changes in nutritional status over short periods of time. It may also have procedural errors due to observer bias and issues with standards reference, such as local versus international standards.

The findings of this study highlight the importance of assessing IR in nondiabetic females to identify cardiometabolic risks at early stages, as it may lead to increased pathophysiology among this young population. In addition, the study indicates a significant association between IR and various anthropometric indices, suggesting that these indices can be valuable tools for identifying IR in young females without DM. It emphasizes how crucial it is to measure adiposity in nondiabetic females to detect IR early, as it may result in elevated cardiometabolic risks. The results also demonstrate that both normal weight controls and overweight/obese individuals with IR are at a higher risk of developing cardiometabolic disorders, including dyslipidemia and MetS. This underscores the need for early identification and intervention to mitigate the risks associated with IR.

Conclusion

In conclusion, the findings underscore the importance of addressing IR early on to prevent adverse cardiometabolic outcomes. Independent of DM and obesity, young females with IR demonstrated an elevated risk for acquiring cardiometabolic conditions. Further, this study provides valuable insights into the relationship between various anthropometric indices and IR-induced cardiometabolic risks in young nondiabetic females. Notably, VAI proves to be a precise measurement for identifying IR and CVD among young females, surpassing the accuracy of other indices. BMI, BRI, ABSI, and WC are also reliable measurements to assess IR and the risk of CVD. However, HI, WHR, and WHtR do not exhibit any conclusive associations with IR.

Therefore, it is crucial to consider and effectively manage IR along with obesity in young females to reduce the risk of developing future metabolic and cardiovascular disorders. Proper management of both IR and obesity can play a significant role in preventing the onset of cardiometabolic health issues in this population.

Footnotes

Authors' Contributions

Conceptualization: N.H., U.W, and R.P.; Methodology: N.H., U.W, and R.P.; Formal analysis and investigation: N.H.; Writing—original draft preparation: N.H.; Writing—review and editing: U.W. and R.P.; Funding acquisition: U.W.; Resources: N.H., U.W., and R.P.; Supervision: U.W. and R.P.

Author Disclosure Statement

No conflicting financial interests exist.

Funding Information

This study was funded by the University of Sri Jayewardenepura (University Grant—ASP/01/RE/MED/2018/51).

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