Identification of a HOMA-IR cut-off point for cardiometabolic risk and modifiable risk factors in peruvian adolescents

Katherine Curi-Quinto; Fabian Vasquez; Melissa Abad; Fabiola Lazarte; Mary Penny; Juana del Valle-Mendoza

doi:10.1371/journal.pone.0351139

. 2026 Jul 10;21(7):e0351139. doi: 10.1371/journal.pone.0351139

Identification of a HOMA-IR cut-off point for cardiometabolic risk and modifiable risk factors in peruvian adolescents

Katherine Curi-Quinto ^1,^2,^*, Fabian Vasquez ³, Melissa Abad ², Fabiola Lazarte ², Mary Penny ², Juana del Valle-Mendoza ^1,²

Editor: Neftali Eduardo Antonio-Villa⁴

PMCID: PMC13354073 PMID: 42430409

Abstract

Background

Although HOMA-IR is widely used to assess insulin resistance, reported cut-off values vary substantially across population, particularly during adolescence. The aim of this study was to determine the distribution of HOMA-IR values. identify a HOMA-IR cut-off associated with metabolic syndrome (MS), and assess modifiable risk factors of IR in a longitudinal cohort of Peruvian adolescents.

Methods

We performed a secondary data analysis from a longitudinal adolescent’s study. A sample of 371 adolescents (14.5 ± 0.1 years old) from low- medium socioeconomic status. ROC curve analysis was used to identify the specific cut-off point to classify IR using the sensitivity and specificity values in comparison with the MS. Multiple logistic regression analysis including diet, physical activity and body composition from adolescence, excess weight during infancy and family history of non-chronic disease was included to identify risk factors (FHCD) associated with IR.

Results

The HOMA-IR was 3.29 (SD 1.71) with no differences by sex. We identified 3.9 for HOMA-IR as the cut-off point with sensitivity (72.4%) and specificity (75.4%) for predicting MS. IR was present in 28.6% (95% CI 24.2;33.4%); 84% had at least one cardiometabolic risk factor and low HDL and abdominal obesity were the most prevalent (62 and 35%, respectively). Adolescents with higher fat mass index (OR 16.03, 95% CI 6.79 to 37.86), and those physically inactive (OR 2.08 95% CI 1.06 to 4.07) were more likely to have IR. No association was found with diet, excess weight at infancy and FHCD.

Conclusions

A cut-offs point of 3.9 for HOMA-IR allows to identify adolescents with high metabolic risk. Strategies to promote lower FMI and improve the physical activity levels could reduce the risk of IR in adolescents.

Introduction

Early exposure to negative changes in lifestyle such as dietary patterns and physical activity have increased the risk for obesity and cardiometabolic disorders earlier in life [1–3]. According to the World Atlas of Obesity 2023, children and adolescents are the most vulnerable population because the prevalence of obesity in these groups is likely to increase from 2020 to 2030 by 10–17% in males and from 8% to 14% in females [4]. Along with obesity, there is an early onset of cardiometabolic disorders such as metabolic syndrome (MS). For instance, in adolescents the prevalence of MS was 5.5% (4.1–8.4) in high-income countries, 3.9% (3.1–5.4) in upper-middle-income countries, 4.5% (2.6–8.4) in lower-middle-income countries, and 7.0% (2.4–15.7) in low-income countries [5].

MS is a cluster of cardiometabolic disorders that includes abdominal obesity, glucose intolerance, hypertension, and dyslipidemia [6]. Insulin resistance (IR) has been considered an underlying factor of MS, and this is defined as the reduction in the tissue response to insulin stimulation, causing impaired glucose uptake by cells, and increased circulating glucose (hyperglycemia). Faced with hyperglycemia, the first response of cells is to increase circulating insulin levels, then cell metabolism changes to alternative pathways that cause metabolic alterations such as adipose tissue dysfunction, production of reactive oxidative species, inflammation, dyslipidemia, atherosclerosis, endothelial dysfunction, and hypertension [7]. Having these conditions earlier in life increases the risk for chronic noncommunicable diseases (NCDs) such as type 2 diabetes and cardiovascular disease in adulthood [8,9]. Therefore, early detection of IR, as well as the identification of its main risk factors at a country-specific level gives the opportunity to initiate actions to control the occurrence of NCDs in a timely manner.

Homeostasis model assessment-estimated insulin resistance (HOMA-IR) is the most common method used to measure IR [10,11]. HOMA-IR is a validated surrogate measure for IR that is calculated using data of fasting plasma glucose and insulin. This method was developed by Matthews et al. and this is a more accessible and non-invasive method compared to the gold standard of hyperinsulinemic euglycemic clamp [12,13]. HOMA-IR is affected by different factors in the study population, such as ethnicity, age, sex, and metabolic conditions; and it is therefore necessary to identify country specific HOMA-IR cut-off points for the classification of IR. Nonetheless, no studies in Peruvian adolescents have proposed specific cut-off points for HOMA-IR classification. Therefore, this study aimed to determine the distribution of HOMA-IR values and identify a cut-off value associated with MS, as well as identifying the modifiable risk factors of IR in a longitudinal study of Peruvian adolescents.

Materials and methods

Study design and population

This is a secondary analysis of data obtained from a population of Peruvian adolescents who were part of a longitudinal study that began at infancy (6–11 months of age) and was followed up with evaluations at 14 years of age. Participants recruited between 2004 and 2005 were healthy infants with birth weights higher than 2500 grams [14]. Infants were part of a trial designed to evaluate the efficacy of a complementary food based on bovine milk fat globule membranes (bMFGM) on diarrhea, anemia, and micronutrient status [14]. The follow-up phase was carried out in 2018 with the aim of evaluating the long-term effects of the use of bMFGM on health and nutritional outcomes at 14 years of age that showed no difference in body composition and cardiometabolic indicators among adolescents that was supplemented with bMFGM at infancy and the control group. From a total of 394 adolescents, we analyzed data of 371 participants that have complete data from infancy and adolescence. For fat-free mass (FFM) and fat-free mass index (FFMI), data were available for 349 participants; thus, related bivariate analyses used this subsample, while others used the full sample (n = 371).

The study has been approved by the Ethics Committee of the Instituto de Investigación Nutricional (Lima, Peru). Parents or caregivers of the participants signed an informed consent form and adolescents assented to participation.

Adolescence nutritional status

Nutritional status was assessed using the z-score of body mass index for age that was calculated based on anthropometric measures of weight and height. Standardized procedures were used for these measurements. Weight was measured with a SECA scale with a precision of 0.1 kg; height was measured with a Holstein stadiometer with 0.1 cm accuracy. For anthropometric assessment World Health Organization’s reference standards (2007) were used. Standardized for age and sex body max index (BMI) was used as an indicator of overweight. Values of BMI z score >2 standard deviation (SD) were classified as obesity, values >1SD were considered overweight and values < 2SD were underweight.

Adolescent cardiometabolic risk factors

Waist circumference (WC) defined as the minimum circumference between the iliac crest and the rib cage was measured using a distensible measuring tape (SECA). Fasting serum total glucose, insulin, and lipid profile (cholesterol, triglycerides, and high-density lipoprotein) were measured in venous blood sample obtained after 12 h of fasting using an enzymatic colorimetric test (QCA SA Amposta, Spain) and dry analytical methodology (Vitros, Johnson & Johnson Clinical Diagnostics Inc.), respectively. The systolic and diastolic blood pressures were measured using a standard mercury sphygmomanometer, on the non-dominant arm at rest on a level surface of the heart 15 min at rest.

Adolescent body composition

Body composition was evaluated using bioimpedance measurement (Seca mBCA 525). Indicators of FMI and FFMI were estimated. FMI was calculated as total fat mass value divided by height squared, and FFM as the total value of fat free mass by height squared. FMI, FFMI were classified into tertiles (T1, T2, T3).

Diet and physical activity

Quality of diet was assessed based on food intake using a food frequency questionnaire. Trained nutritionists asked about food frequency intake of seven predefined lists of critical food groups: meat and sausages, dairy products, legumes, vegetable, fruits, sweet sugar beverages, sweet and salty snacks, consumed in the last month before the interview. We defined the diet as relatively healthy or unhealthy whether a person fulfilled the recommended intake of at least four food groups [15]. Physical activity was measured using a 7-day recall questionnaire for adolescents (PAQ-A) that was applied by trained field workers who registered the answers of the adolescents. The PAQ-A was developed by Kowalski et al, 1997 and this is an accessible tool with a good content validity as well as moderate positive correlation with VO² peak and cardiorespiratory fitness [16,17]. The PAQ-A was used in the Peruvian context [18] and this assessed the general level of physical activity through eight questions about physical activity that the adolescent carried out in the last 7 days during their free time, during physical education classes, at different times during class days (lunch, afternoons, and evenings) and during the weekend. Each item was scored on a 5-point scale. The final score was obtained by the arithmetic average of the scores obtained from these 8 questions. Question 9 informed about any circumstance that prevented him from doing physical activity that week and this factor was considered in the analysis.

Nutritional status at infancy and family history of chronic diseases

Weight and length/ height data for the first and second year of age were included. Anthropometric measures were assessed by trained personnel following standardized procedures [19]. The WHO reference standards were used to obtain z-score for weight, length/height and BMI for age. Childhood overweight and obesity was diagnosed when the z-BMI/age was > 2 SD and >3 SD, respectively. Excess weight was defined as infants with overweight and obesity (z-BMI < 2 SD). Family history of chronic diseases was obtained from self-reports by parents or caregivers of adolescents. Presence or absence of a history of DM2, hypertension and obesity in parents and siblings were considered.

Definition of Metabolic Syndrome

MS was diagnosed using the International Diabetes Federation (IDF-2007) [20] criteria that is established when a subject has altered waist circumference (WC > 90 cm in men and 80 cm in women); plus two risk factors that included: systolic blood pressure ≥130 or diastolic blood pressure ≥ 85 mmHg, triglycerides ≥ 150 mg / dl, high density lipoprotein (HDL) ≤ 40 mg / dl in men and in women ≤50; and fasting blood glucose ≥ 100 mg / dl.

Definition of Insulin resistance

IR was estimated using the HOMA-IR, calculated as the product of fasting insulin (µU/mL) and fasting glucose (mmol/L), divided by 22.5. This index provides an indirect measure of insulin sensitivity based on fasting metabolic parameters [21]. Receiver operating characteristic (ROC) analysis was used to find the optimal cut-off of IR for MS diagnosis in Peruvian adolescents. A test with perfect discrimination has a ROC plot that passes through the upper left corner, indicating 100% sensitivity and 100% specificity. A ROC plot closer to the upper left corner denotes greater accuracy of the test. To determine optimal cutoffs for MS diagnosis, the point on the ROC curve with maximum Youden Index [sensitivity-(1-specificity)] was calculated. Next, the values were verified with the likelihood ratio for a positive result (LR+) and the post-test probability (the proportion of participants above cutoffs who truly have MS).

Ethical considerations

The ethics committees of the Instituto de Investigación Nutricional (IIN), Lima-Peru, approved this study under the number 372–2017/CIEI-IIN. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. We obtained written informed consent from all participants prior to their inclusion as well as the assent from the adolescents.

Statistical analysis

Quantitative variables were described using mean and standard deviation (SD) or confidence intervals. Categorical variables were described by absolute and relative frequencies. To test differences of means we used t-test for independent samples or chi-square to test the association among categorical variables. We estimated the bivariate association among potential risk factors and IR using bivariate binomial logistic regression. We used the multivariate logistics regression model to identify the risk factor of IR. Two models were tested. In the first model (model 3), we included potential risk factors from adolescence, and in the second model (model 4). We included early potential risk factors (excess weight at childhood and FHCD). For both models we included the adjustment for the participation of the trial at infancy. A P-value < 0·05 was considered as statistically significant, and all analyses were conducted by Stata software v.17.

Results

The HOMA-IR cutoff identified was 3.9, yielding a sensitivity of 72.4% and a specificity of 75.4%. The area under the ROC curve was 0.79 (95% CI: 0.69–0.88) (Fig 1). The prevalence of obesity and overweight in the adolescents was 14.6% (95% CI 11.3; 18.5%) and 27.5% (95% CI 23.2; 32.3%), respectively, and 48.1% of the population was female. Additionally, prevalence of MS according to the IDF criteria was 7.8% (95% CI 5.5; 11.0).

Table 1 shows that mean HOMA-IR was 3.3 (95% CI 3.1–3.5) and presents the distribution of HOMA-IR percentiles by sex, body mass index and MS. The mean HOMA-IR was significantly higher in overweight and obese adolescents compared with those with healthy weight (3.42; 5.17 vs 2.75), and those with MS compared with those without MS (5.31 vs 3.11). No significant difference was found by sex. Table 1 presents the optimal cutoff points for HOMA-IR to predict MS in males and females.

Table 1. Percentile distribution of the HOMA-IR in the overall sample and stratified by sex, body mass index and metabolic syndrome.

	Sample by sex			z-Score Body mass Index/Age			Metabolic Syndrome (IDF)²
HOMA-IR¹ Percentiles	Total (n = 371)	Male (n = 193)	Females (n = 178)	Healthy (≤ 1) n = 215	Overweight (1–1.99) n = 102	Obesity (≥2) n = 54	No (n = 342)	Yes (n = 29)
1%	0.81	0.76	1.09	0.81	1.29	1.83	0.81	1.54
5%	1.23	1.18	1.54	1.17	1.65	2.28	1.23	2.21
10%	1.63	1.51	1.75	1.36	1.73	2.47	1.59	2.24
25%	2.09	1.99	2.21	1.91	2.28	3.25	2.02	3.26
50%	2.83	2.64	3.04	2.47	3.12	4.98	2.73	5.56
75%	4.01	4.10	3.97	3.48	4.08	6.79	3.90	7.05
90%	5.85	6.27	5.17	4.31	5.29	7.60	5.12	8.37
95%	7.04	7.25	6.37	5.17	6.54	8.90	6.35	8.41
99%	8.90	9.74	7.77	6.30	8.45	9.79	8.45	9.79
Mean	3.29	3.30	3.27	2.75	3.42 ^a	5.17 ^ab	3.11	5.31 ^c
SD	1.71	1.91	1.45	1.21	1.67	2.06	1.55	2.16

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¹Homeostasis model assessment-estimated insulin resistance (fasting insulin (μU/mL) x fasting glucose (nmol/L)/22.5)

²Defined by the International Diabetes Federation (IDF) criteria:

Central obesity (WC waist circumference >90 cm in males and 80 cm in females), plus two metabolic risk factors.

³Standard Deviation

^aIndicates a statistically significant difference compared with the healthy body mass index category (ANOVA). ^b Indicates a statistically significant difference compared with the overweight category (ANOVA). ^c Indicates a statistically significant difference between participants with and without metabolic syndrome (t-test). Differences were considered statistically significant at p < 0.05.

To show differences in cardiometabolic risk factors by IR, Fig 2 presents the percentage distribution of each metabolic syndrome component. In the overall population at least six out of ten adolescents had at least one cardiometabolic risk factor (62%). As expected, in adolescents with IR eight out of ten (84%) had one or more cardiometabolic risk factors while in those classified as non-IR five out of ten (53%) had one or more cardiometabolic risk factors. The most prevalent risk factor in the overall sample as well as the adolescents with or without IR was the low levels of HDL (62.3% and 39.6%, respectively). Abdominal obesity and hypertriglyceridemia were the second and third most prevalent risk factors in adolescents with IR (34% and 22.6%, respectively) and about 8.5% had fasting hyperglycemia. The prevalence of all the cardiometabolic risk factors was significantly higher in adolescents with IR compared with those without IR (p < 0.001), except for hypertension in which the difference was not statistically significant (Fig 2).

Table 2 displays the anthropometric and cardiometabolic adolescent profile by IR status. Participants with IR compared with those without IR had significantly higher values of z-score for BMI/age, body composition indicators (FFMI and FMI) and cardiometabolic indicators including waist circumference, triglycerides, glucose, blood pressure, and insulin; while the HDL was significantly lower. Among those with or without IR, we did not find significant differences in the anthropometric profile from infancy including the z-score for height/age and body mass index/age, as well as the z-score for height/age in adolescence (Table 2).

Table 2. Anthropometric and cardiometabolic adolescent profile by insulin resistance.

			Insulin resistance based HOMA-IR¹
Variable	Total (n = 371)		IR (-) (n = 265)		IR (+) (n = 106)		P value²
Variable	Mean	SD	Mean	SD	Mean	SD	P value²
Anthropometric profile at adolescence
Age (y)	14.5	0.1	14.5	0.1	14.5	0.1	0.171
Body mass index (Kg/m²)	22.6	3.9	21.6	3.2	25.1	4.4	<0.001
Height/Age (z-score)	−0.6	0.8	−0.6	0.8	−0.6	0.8	0.511
Body mass index/Age 14 y (z-score)	0.8	1.0	0.5	0.9	1.4	1.0	<0.001
Body composition at adolescence ³
Fat Free Mass Index (FFMI)	16.6	2.4	16.4	2.4	17.2	2.2	0.007
Fat Mass Index (FMI)	5.9	3.9	5.2	3.8	7.8	3.3	<0.001
Anthropometric profile at infancy
Height/Age at 8 months (z-score)	−0.6	0.9	−0.6	0.9	−0.5	0.9	0.809
Height/Age at 14 months (z-score)	−0.5	1.0	−0.5	1.0	−0.5	1.1	0.948
Body mass index/Age 8 months (z-score)	1.0	0.9	1.0	0.9	1.1	0.9	0.332
Body mass index/Age at 14 months (z-score)	0.9	0.9	0.8	0.9	0.9	1.0	0.358
Cardiometabolic profile at adolescence
Waist circumference (cm)	75.5	9.3	73.0	7.3	81.6	10.8	<0.001
Triglycerides (mg/dl)	89.0	42.8	80.0	30.4	111.5	58.4	<0.001
High density lipoprotein (mg/dl)	46.7	11.6	48.5	11.1	42.1	11.6	<0.001
Glucose (mg/dl)	88.8	6.4	87.5	5.7	92.2	6.8	<0.001
Systolic blood pressure (mm Hg)	117.6	11.4	115.9	10.6	121.9	12.4	0.001
Diastolic blood pressure (mm Hg)	74.6	8.4	73.7	8.0	76.9	8.9	<0.001
Insulin (μIU/ml)	16.9	13.4	11.2	3.4	24.1	6.2	<0.001
HOMA-IR	3.3	1.7	2.4	0.7	5.5	1.5	<0.001

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¹Participants with values of HOMA-IR > 3.9 are classified as insulin resistance

²p-value from t-test for quantitative values and chi² for the crude analysis.

³n=349

Table 3 presents the results of the bivariate analysis of potential risk factors for IR. Low levels of physical activity, overweight and obesity in adolescence, FMI, FFMI and presence of excess weight were significantly associated with the presence of MS. No significant association was found between diet, excess weight in the second year of age, and FHCD.

Table 3. Potential risk factors of insulin resistance in adolescents.

Risk factors	Adolescents with IR¹		Adolescents without IR		OR²	P value^*	CI 95%³
Risk factors	N	%	N	%	OR²	P value^*	CI 95%³
*Sex*
Female	49	46.23	129	48.68	Ref.
Male	57	53.77	136	51.32	1.10	0.669	0.70-1.73
*Food Intake*
Healthy diet	69	65.09	182	68.68	Ref.
Unhealthy diet⁴	37	34.91	83	31.32	1.18	0.505	0.73-1.89
*Low physical Activity*
No	20	18.87	76	28.68	Ref.
Yes⁵	86	81.13	189	71.32	1.73	0.053	0.99-3.01
*Nutritional status at 14 y*
Normal weight	38	35.85	177	66.79	Ref.
Overweight⁶	30	28.3	72	27.17	1.94	0.018	1.12-3.37
Obesity⁷	38	35.85	16	6.04	11.06	<0.001	5.60-21.86
Body composition
Fat free mass Index (kg/height²)
Low	29	29.29	92	36.8	Ref.
Middle	30	30.3	88	35.2	1.09	0.765	0.60-1.95
High	40	40.4	70	28.0	1.81	0.041	1.02-3.21
Fat mass Index (kg/height²)
Low	14	14.14	108	43.2	Ref.
Middle	33	33.33	88	35.2	2.89	0.002	0.60-1.95
High	52	52.53	54	21.6	7.43	<0.001	1.02-3.21
*Excess weight during first year of age* ⁸
No	84	79.25	232	87.55	Ref.
Yes	22	20.75	33	12.45	1.84	0.044	1.02-3.34
*Excess weight during second year of age* ⁸
No	93	87.74	235	88.68	Ref.
Yes	13	12.26	30	11.32	1.09	0.798	0.55-2.19
*Family history of NCDs* ⁹
No	86	81	207	78	Ref
Yes	20	19	58	22	0.83	0.520	0.47-1.46

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¹IR: Insulin resistance defined as HOMA-IR > 3.9; 2 OR: Odds Ratio; 3CI: Confidence Interval came from simple binomial logistic regression; 4Unhealthy diet: score ≤4; 5Physical inactivity: score <3; 6Overweight at 14y: z-BMI/age >1 Standard Deviation (SD); 7Obesity: z-BMI/age >2 SD. 8z-BMI ≥2 SD; 9 NCDs: non-communicable chronic disease includes diabetes, hypertension and obesity in parents or siblings. *Statistical significance p value < 0.05 from bivariate logistic regression model.

In the adjusted analysis using multiple logistic regression models (Table 4), low physical activity and high FMI were independently associated with IR. Those with low physical activity were two times more likely to have IR (OR: 2.08; 95% CI 1.06 to 4.07), and those with high FMI are more likely to have IR (OR: 5.38; 95% CI 2.41 to 11.99 for those in the tertile 2; and OR: 16.03; 95% CI 6.79 to 37.86 for those with high FMI compared with those with low FMI). These associations remained after adjusting for early factors in Model 2 including excess weight at infancy, FHNCD and the participation in the trial using the bMFGM in complementary food at infancy. None of this variable has been associated with the IR.

Table 4. Multivariate regression model of the associated risk factors of insulin resistance in Peruvian Adolescents.

Risk factors	Model 1		Model 2
Risk factors	OR	CI 95%	OR	CI 95%
Female	2.78*	1.17 - 6.62	2.89*	1.20 - 6.91
Unhealthy diet ¹	1.01	0.58 - 1.76	0.97	0.55 - 1.71
Low physical activity ²	2.09*	1.07 - 4.07	2.08*	1.06 - 4.07
Body composition
Fat free mass Index (kg/height²)
Middle (Tertile 2)	1.20	0.60 - 2.40	1.16	0.57 - 2.35
High (Tertile 3)	1.53	0.61 - 3.84	1.48	0.58 - 3.74
Fat mass Index (kg/height²)
Middle (Tertile 2)	5.15*	2.34 - 11.49	5.38*	2.41 - 11.99
High (Tertile 3)	14.96*	6.46 - 34.61	16.03*	6.79 - 37.86
Excess weight at first year of age ³	(…)	(…)	1.04	0.52 - 2.11
Excess weight at second year of age ³	(…)	(…)	1.07	0.49 - 2.36
Familiar history of NCD ⁴	(…)	(…)	0.61	0.31 −1.20
Participation in the trial at infancy	(…)	(…)	1.16	0.69–1.96

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*Statistical significance p_value < 0.05 from multivariate logistic regression model; (...) Un-observed variable in the model, ¹Unhealthy diet: score ≤4; ²Low physical activity: score <3; ³Excess weight z-BMI/age > 2 SD ⁴NCDs: non-communicable chronic diseases include diabetes, hypertension, and obesity in parents or siblings.

Discussion

In this study we identified a value of 3.90 as specific cut-off point for HOMA-IR that has a sensitivity of 72.4% and specificity of 75.4% for predicting MS in Peruvian adolescents with an average age of 14.5 years (AUC: 0.79; 95% CI 0.69–0.88).

According to this cut-off point, 3 out of 10 adolescents had IR, and consistent with previous studies [22–24] this population had higher cardiometabolic risk factors such as low HDL (62%), abdominal obesity (35%), hypertriglyceridemia (23%), fasting hyperglycemia (8.5%), and MS (20%). This clustering of metabolic abnormalities reflects the central role of insulin resistance as a pathophysiological driver of cardiometabolic disease. Insulin resistance has been widely described as a key mechanism underlying metabolic syndrome, type 2 diabetes, and cardiovascular disease through its effects on glucose metabolism, lipid homeostasis, and systemic inflammation [25].

The identified cut-off point is close to previous studies in adolescents from 10 to 18 years old that reports a range of 2.50 to 3.29 for HOMA-IR for MS with values of AUC from 0.73 to 0.89 [26,27]. These findings are consistent with growing evidence supporting the role of HOMA-IR as an early marker of metabolic dysfunction beyond overt diabetes. In a large prospective cohort, demonstrated that elevated HOMA-IR levels independently predicted the development of type 2 diabetes and chronic kidney disease even in non-diabetic individuals, reinforcing its value as an early risk stratification tool [28]. As expected, this variability is associated with the different characteristic of the study populations coming from different countries (Korea, India, and Mexico), and have different cardiometabolic profile that can be noted by the different proportion for MS in each locations (1.6% in Korea to 19.9% in India), whereas in the Peruvian longitudinal study we found a prevalence of 7.8%, using the same criteria of IDF for the definition of MS). Despite this variability, HOMA-IR was recognized as a good alternative for detecting a population with high cardiometabolic risk early in life. The association and predictive capacity of adiponectin and HOMA-IR indexes with metabolic risk markers in 691 children and adolescents (7–14 years old); in both sexes HOMA-IR was associated with metabolic risk, and it was the most suitable methods for MS screening in both age groups [29]. Additionally, evidence from Peruvian populations suggests that insulin resistance is already elevated in individuals with prediabetes phenotypes and is associated with early metabolic alterations such as dyslipidemia and hepatic steatosis, even before the onset of overt diabetes [30].

In this study we also identified the low physical activity and higher FMI as independent risk factors for IR. In the case of physical activity, growing evidence recognizes its role as a factor for improving insulin sensitivity and prevention of metabolic disorders in young people. For instance, in a recent systematic review eleven of 16 studies suggested an independent association of physical activity level with metabolic disorders [31]. In relation to FMI, this is a measure of total body fat adjusted by the body size [FM (kg)/height (m)²]. Height is positively correlated with weight and this adjustment [32] removes this effect. For this reason, FMI is considered a better indicator than the relative value of body fat percentage [33]. Further, previous evidence shows that FMI compared with BMI and percentage of body fat (BF%) have a higher capacity for predicting MS [34]. In agreement with our results, previous studies that measured obesity by BMI as well as body fat have been positively associated with HOMA-IR, MS as well as cardiovascular diseases [23]. Excess of adipose tissue in obesity produces IR and increased release of free fatty acids in plasma; this is correlated with the magnitude and prevalence of abnormalities associated with IR, such as dyslipidemia, systemic inflammation, diabetes mellitus 2 hypertension, myocardial infarction, and early mortality. [35–38]

In this study we did not find an independent association between FFMI and HOMA-IR. We observed that adolescents in the high tertiles of FFMI compared with the low tertiles were most likely to have IR (1.81; 95% CI 1.02–3.21); however, this association was attenuated in the adjusted analysis mainly by the effect of the FMI, showing greater importance of the role of adipose tissue in relation to IR. These results are also in line with previous findings that report controversies in the relationship between FFM and indicators of IR in children and adolescents [38]. These controversies can be explained by many factors such as heterogeneity among the studies making them difficult to compare. For example, age of the adolescents, sample size, different ways to measure the body mass index (bioelectrical impedance, dual X-ray absorptiometry, etc.), and the way of modeling the association between body composition and IR as well as the definition of low FFMI. The latter may be caused by classification bias. For example, a Chilean adolescent’s low levels of FFMI, defined as those with values lower than the 25th percentile, was associated with IR measured by HOMA-IR [22]. In our study, we included FFMI in tertiles in the model. As a comparison we also introduced the variable of FFMI as dichotomous using 25^th percentile as cut-off point and the association remained not significant; similarly, we used body composition variables by their quantitative measures and the results stayed invariable. Given these results, further studies are needed to better understand the association between FFMI and HOMA-IR.

The findings of this study are of interest to public policy, as CVD and DM2 are the leading cause of death in Peru and their treatment generates high economic and social costs in the country [39]. Our results support the urgent need to promote and enhance healthy lifestyles in adolescence, including the systematic practice of physical activity as well as prevention of obesity to reduce the risk for metabolic disorders such as IR. Furthermore, early detection of IR in primary health care centers offers opportunities to start actions to tackle the onset of cardiometabolic disorders and chronic diseases in adulthood. These actions could have great impact because the early periods of life are stages when people acquire and consolidate habits and future lifestyles [40]. Considering the successful previous experience in Peru in reducing the prevalence of stunting, a major commitment and participation of stakeholders at different levels (national, regional, at community and individual level) are needed [41] to have a more effective strategy to face the NCDs related to nutrition and lifestyles. Currently, there are some initiatives to tackle obesity in Peru such as the law of promotion of healthy eating [42–45], however limited actions have been taken to improve physical activity in the adolescent population, as evidenced in the latest systematic review on interventions and policies on school environments and obesity in Latin America and the Caribbean [39]. Thus, in light of our results there is a need to strengthen the promotion of physical activity to reduce FMI and the risk for metabolic disorders in the young population in Peru.

In conclusion, in this sample of Peruvian adolescents we found that physical inactivity and high fat mass index were independently associated with increased risk for IR. Strengthened public policies to detect IR early, considering the specific metabolic characteristics or the population and implementation of actions to improve physical activity and reduction of FMI could improve the effectiveness of interventions to prevent NCDs early in life.

Acknowledgments

We thank all the research staff, fieldworkers and participants involved in the original longitudinal study.

Data Availability

The authors were granted access to the study dataset for the purpose of conducting the analyses and preparing this publication but are not authorized to publicly redistribute the data. The dataset is available from the institution upon reasonable request and approval in accordance with institutional policies,and ethical considerations. Request for access to the study data should be directed to Mark Stenning, Head of the Instituto de Investigación Nutricional Email: directorgeneral@iin.sld.pe As the designated institutional officer, he is responsible for evaluating data access requests while ensuring the protection of participant confidentiality and compliance with informed consent provisions.

Funding Statement

“Universidad Peruana de Ciencias Aplicadas”, Lima-Peru. Grant (UPC-EXPOST-2024-1).

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PLoS One. doi: 10.1371/journal.pone.0351139.r001

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Reviewer #1: Major Overview:

The study aimed to determine if a HOMA-IR cutoff could be associated with metabolic syndrome and identify the modifiable risk factors of insulin resistance in an adolescent population. The investigators identified a HOMA-IR of 3.9 to have high sensitivity and specificity for predicting metabolic syndrome. The AUC from the RO analysis is ~0.79, which suggests good diagnostic capability. The result appear to be novel for the population being studied.

Major criticisms:

• The manuscript states the 349 adolescents were used from a total of 394 adolescents. However, table 1 states that 371 adolescents were used for the HOMA-IR distribution in Table 1, and Anthropometric & cardiometabolic profile in Table 2. Additionally, data from Table 3 only includes 106 adolescents. I don’t understand this discrepancy.

• If there were 394 adolescents in the study, what were the explicit inclusion/exclusion criteria to get to 349 or 371 – whatever the case may be?

• Figure 2 shows standard error bars, not SD or CI 95%, as reported in the text. Please convert it to SD or CI95%.

• The t-test assumes normally distributed quantitative data; however, I could not find any checks for normality.

• It is unclear how variables were selected for inclusion in the multivariate logistic regression.

• Were interaction effects between risk factors considered?

• I was unable to find if any corrections like Bonferroni or other corrections were made to reduce the incidence of type I errors.

• I fear the authors may have overstated their data in a few places. For example, in the abstract the conclusion is “A cut-offs point of 3.9 for HOMA-IR allows to identify adolescents with high metabolic risk.” From their data, the positive predictive value is low (~10-11%), likely due to the low prevalence, while the negative predictive value is high (98-99%). This suggests that HOMA-IR is more reliable in ruling out MS than confirming it’s presence. The authors should address this.

Minor criticisms:

• The abstract is cut off or has an incomplete sentence. “No association was found with diet, excess weight at infancy, and….”

• Spelling Mails instead of males in documents.

• No subject number is provided for Figures 1 & 2. N’s are essential information for the reader to interpret the analysis and should be reported unambiguously.

• AUC for HOMA-IR should be added to the abstract.

Reviewer #2: Comments

Abstract (Pages 1, 9-10)

• Abstract (Lines 27-52, Page 10):

o Line 28: "Currently, there is a need..." – Grammatically correct but could be more concise: "Promoting healthy lifestyles in adolescence is critical..."

o Line 33: "A sample of 349 adolescents" – Consistent with methods (Line 115, Page 13), but Table 1 (Page 32) reports n=371. This discrepancy needs resolution.

o Line 39: "The HOMA-IR was 3.29 (SD 1.71)" – Should clarify this is the mean (as in Results, Line 211, Page 17). Suggest: "Mean HOMA-IR was 3.29 (SD 1.71)."

o Line 43: "No association was found with diet, excess weight at infancy and." – Incomplete sentence. Suggest: "No association was found with diet, excess weight at infancy, or family history of chronic diseases."

o Line 45: "A cut-offs point" – Grammatical error; should be "A cut-off point."

Introduction (Pages 11-12)

• Line 53-55 (Page 11): "Early exposure to negative changes in lifestyle such as dietary patterns and physical activity have increased the risk for obesity and cardiometabolic disorders earlier in life [1-3]."

o Comment: This opening effectively defines the problem by linking lifestyle changes to obesity and cardiometabolic risks. However, "negative changes" is vague—specifying "unhealthy diets" or "sedentary behavior" would clarify the scope. The citations support the claim well.

• Line 54: "Early exposure to negative changes in lifestyle" – Slightly awkward phrasing. Suggest: "Early adoption of unhealthy lifestyles."

• Line 56: "10 to $17 %$ in mails" – Typo; should be "males."

• Line 56-58: "According to the World Atlas of Obesity 2023, children and adolescents are the most vulnerable population because the prevalence of obesity in these groups is likely to increase from 2020 to 2030 by 10 to 17% in mails and from 8% to 14% in females [4]."

o Comment: Provides concrete evidence of the problem’s scale, though "mails" is a typo (should be "males"). Including a Peruvian statistic here (if available) would localize the issue further.

• Line 58-61: "Along with obesity, there is an early onset of cardiometabolic disorders such as metabolic syndrome (MS)... prevalence of MS was 5.5% (4.1-8.4) in high-income countries... 7.0% (2.4-15.7) in low-income countries [5]."

o Comment: Effectively broadens the problem to MS, with global prevalence data. However, no Peruvian-specific MS prevalence is mentioned, which could strengthen the rationale for a local study.

• Line 62-68: "MS is a cluster of cardiometabolic disorders that includes abdominal obesity, glucose intolerance, hypertension, and dyslipidemia [6]... increases the risk for chronic noncommunicable diseases (NCDs) such as type 2 diabetes and cardiovascular disease in adulthood [7,8]."

o Comment: Clearly defines MS and ties IR to long-term health consequences, supported by citations. This sets up the scientific basis well but could briefly note modifiable factors (e.g., diet, activity) as intervention targets.

• Line 62: "Insulin resistance has been considered an underlying factor" – Accurate, but could cite a foundational reference (e.g., Reaven, 1988) for context.

• Line 62: "...includes abdominal obesity, glucose intolerance, hypertension, and dyslipidemia [6)." – Typo; change bracket to "]".

• Line 71-73: "Homeostasis model assessment-estimated insulin resistance (HOMA-IR) is the most common method used to measure IR [9,10]... a more accessible and non-invasive method compared to the gold standard of hyperinsulinemic euglycemic clamp [11,12]."

o Comment: Introduces HOMA-IR effectively, highlighting its practicality. This supports the study’s methodology but could mention its adaptability to population-specific cut-offs here.

• Line 75-77: "HOMA-IR is affected by different factors... ethnicity, age, sex, and metabolic conditions... necessary to identify country specific HOMA-IR cut-off points... no studies in Peruvian adolescents that have suggested specific cut-off points for HOMA-IR classification."

o Comment: Justifies the need for a Peruvian-specific cut-off, defining a research gap. The claim of no prior studies is bold—consider softening to "few studies" unless a systematic review confirms this.

• Line 77: "To the best of our knowledge" – Appropriate, but no systematic literature review is cited to support this claim. Consider referencing a scoping review if available.

• Line 78-80: "Therefore, this study aimed to determine the distribution of HOMA-IR values and identify a cut-off value associated with MS, as well as identifying the modifiable risk factors of IR in a longitudinal study of Peruvian adolescents."

o Comment: The aim is clear and aligns with the problem (IR/MS detection) and gap (country-specific data). It hints at solutions (identifying modifiable factors) but could explicitly connect these to prevention strategies (e.g., "to inform targeted interventions").

• Consistency: The introduction aligns with the study’s aims and sets up the need for a country-specific HOMA-IR cut-off.

Suggestions for Improvement

1. Comprehensiveness: Add a sentence on modifiable risk factors (e.g., "Factors like physical inactivity and excess adiposity are key drivers amenable to intervention") to bridge the problem and solution.

2. Local Context: Include Peruvian obesity/MS data (if available) to localize the problem beyond global trends.

3. Solution Pathway: Strengthen the link to interventions by noting how identifying cut-offs and risk factors can guide public health actions (e.g., screening or lifestyle programs).

Materials and Methods (Pages 13-16)

• Study Design and Population (Lines 105-118, Page 13):

o Line 107: "...349 participants that have complete data..." – Change "that have" to "who have" for grammatical correctness.

o Line 115: "From a total of 394 adolescents, we analyzed data of 349 participants" – Conflicts with Table 1 (n=371). Clarify the correct sample size and exclusion criteria.

o Line 117: Ethics approval is noted, but consent documentation details are incomplete (repeated in Ethics Statement, Page 4). Specify how consent was recorded (e.g., signed forms).

• Adolescence Nutritional Status (Lines 119-126):

o Line 122: "SECA scale" and "Holstein stadiometer" – Brand names are fine, but ensure consistency in capitalization (e.g., "Seca" elsewhere).

o Line 125: "Values <2SD were underweight" – Should be "<-2 SD" for consistency with WHO standards.

• Adolescent Cardiometabolic Risk Factors (Lines 128-135):

o Line 131: "Fasting serum total glucose" – "Total glucose" is unusual; typically "fasting glucose." Verify terminology.

o Line 131: "...was measured in venous blood sample..." – Change to "venous blood samples".

o Line 133: "Systolic and diastolic blood pressures" – Add units (mmHg) for clarity in the methods.

• Adolescent Body Composition (Lines 137-141):

o Line 139: "FMI and FFMI were estimated" – Justify why tertiles were used instead of continuous variables or established cut-offs.

• Diet and Physical Activity (Lines 143-157):

o Line 148: "We defined the diet as relatively healthy or unhealthy whether a person fulfilled..." – Awkward phrasing. Suggest: "Diet was classified as healthy or unhealthy based on meeting recommended intake for at least four food groups."

o Line 155: "The final score was obtained by the arithmetic average" – Clarify if this is a mean score (1-5 scale) and define "low physical activity" threshold (e.g., <3, as in Table 4).

• Nutritional Status at Infancy (Lines 159-166):

o Line 163: "Childhood overweight and obesity was diagnosed" – Should be "were diagnosed."

• Definition of Metabolic Syndrome (Lines 168-172):

o Line 170: Criteria are correctly cited (IDF-2007), but specify if adapted for adolescents (e.g., WC percentiles).

• Definition of Insulin Resistance (Lines 176-184):

o Line 177: HOMA-IR formula is correct. ROC analysis description is sound but could note software used (assumed Stata from Line 204).

• Statistical Analysis (Lines 195-204):

o Line 199: "Bivariate binomial logistic regression" – Redundant; "bivariate logistic regression" suffices.

o Line 201: "Model 3" and "Model 4" – Should be "Model 1" and "Model 2" to match Table 4 (Page 35).

Results (Pages 17-18)

Overall Assessment

The Results section presents key findings logically, starting with sample characteristics, HOMA-IR distribution, cut-off determination, and risk factor associations. Data generally align with tables, but minor inconsistencies (e.g., sample size, blood pressure values) and incomplete reporting (e.g., sex differences) require attention to ensure no contradictions with the Discussion.

• Line 211 (Page 17): "Mean age of the adolescents was 14.5 (0.1 SD) years, and $48.1% of the population was female. Mean HOMA-IR was 3.3 (95% CI 3.1; 3.5%)."

o Comment: Matches Methods (Line 115, n=349) but not Table 1 (n=371). CI should be "3.1-3.5" (no %). sample size needs clarification.

• Line 212: "Prevalence of obesity and overweight" – Matches Table 2 but clarify if z-BMI-based.

• Line 214-215: "Table 1 shows the distribution of the HOMA-IR percentiles by sex, body mass index and MS."

o Comment: Table 1 reports n=371, mean HOMA-IR 3.29 (SD 1.71), slightly differing from 3.3 (CI 3.1-3.5). Minor rounding discrepancy; clarify true value.

• Line 216-217: "The mean HOMA-IR was significantly higher in overweight and obese adolescents compared with those with healthy weight $(3.42; 5.17 vs 2.75)$, and those with MS compared with those without MS (5.31 vs 3.11)."

o Comment: Matches Table 1 means. "No significant difference was found by sex" aligns with Table 1 (3.30 vs. 3.27), but Discussion doesn’t explore this despite Table 4’s sex effect.

• Line 218-219: "Table 1 presents the optimal cutoff points for HOMA-IR to predict MS in males and females. A HOMA-IR with a cutoff of 3.9 showed a sensitivity of $72.4% and a specificity of $75.4 %$; with an area under the curve ROC of 0.79 (IC 0.69 0.88)."

o Comment: Table 1 doesn’t stratify cut-offs by sex—text misstates this. AUC CI should be "0.69-0.88."

• Line 218: "A HOMA-IR with a cutoff of 3.9" – Remove "with" for conciseness: "A HOMA-IR cutoff of 3.9."

• Line 220-222: "Table 2 displays the anthropometric and cardiometabolic adolescent profile by IR status... significantly higher values of z-score for BMI/age, body composition indicators (FFMI and FMI) and cardiometabolic indicators..."

o Comment: Matches Table 2, but systolic BP (14.9 mmHg, Table 2) is implausible (likely 114.9 mmHg). Correct this to avoid contradiction.

• Line 222: "...waist circumference, triglycerides, glucose, blood pressure, and insulin; while the HDL was significantly lower." – Change semicolon to a period for better sentence structure.

• Line 226: "At least six out of ten" – Could be simplified: "62% of adolescents."

• Line 226-230: "The proportion of cardiometabolic risk factors by IR is presented in Figure 2... at least six out of ten adolescents had at least one cardiometabolic risk factor (62%)... low levels of HDL (62.3% and $39.6 %$, respectively)."

o Comment: Figure 2 aligns (62% total for low HDL), but Table 2 mean HDL (46.7 mg/dl) doesn’t directly confirm prevalence—clarify calculation method. "39.6%" seems incorrect (should be non-IR prevalence); verify.

• Line 233-234: "The prevalence of all the cardiometabolic risk factors was significantly higher in adolescents with IR compared with those without IR..."

o Comment: Matches Figure 2 and Table 2 (p=0.001 for diastolic BP), but systolic BP typo affects interpretation. Adjust accordingly.

• Line 235-238: "Table 3 presents the results of the bivariate analysis... Low levels of physical activity, overweight and obesity in adolescence, FMI, FFMI and presence of excess weight were significantly associated with the presence of MS."

o Comment: Text says "MS," but context implies "IR" (Table 3 is IR-based). Correct this. Data match Table 3.

• Line 240-244: "In the adjusted analysis using multiple logistic regression models (Table 4), low physical activity and high FMI were independently associated to IR... None of this variable has been associated with the IR."

o Comment: Matches Table 4 (OR 2.08 for low PA, 16.03 for high FMI). "Female" OR 2.78 contradicts Table 3 (OR 1.10)—explain adjustment effect.

• Line 244: "None of this variable has been associated" – Grammatical error; should be "None of these variables were associated."

Discussion (Pages 19-21)

Overall Assessment

The Discussion contextualizes the HOMA-IR cut-off (3.9) and risk factors (physical inactivity, FMI) within existing literature, offering policy implications. Most data align with Results, but minor mismatches (e.g., prevalence rates) and unexplored findings (e.g., sex effect) need clarification.

• Line 261: "In this study we identified a value of 3.90 as specific cut-off point for HOMA-IR..." – Clarify why 3.90 was chosen over other nearby values.

• Line 262: "A value of 3.90" – Use consistent decimals (3.9 elsewhere).

• Line 264-266: "According to this cut-off point, 3 out of 10 adolescents had IR... low HDL (62%), abdominal obesity (35%), hypertriglyceridemia (23%), fasting hyperglycemia (8.5%), and MS (20%)."

o Comment: IR prevalence (28.6%, Abstract Line 41) aligns with "3 out of 10." HDL 62% matches Figure 2, but abdominal obesity (35%) and others differ slightly from Table 2/Figure 2—clarify source. MS 20% contradicts Results (7.8%, Line 214)—major error.

• Line 276: "HOMA-IR was associated with metabolic risk" – Supported by results, but no sex differences were discussed despite Table 1 stratification. Consider addressing this.

• Line 276: "...HOMA-IR was associated with metabolic risk, and it was the most suitable methods..." – Change "methods" to "method".

• Line 293: "Showing greater importance of the role" – Awkward; suggest: "Indicating the greater role of adipose tissue in IR."

• Line 295: "...classification bias. For example, in Chilean adolescent’s low levels..." – Apostrophe misuse; change to "adolescents".

• Line 304: "Further studies are needed" – Valid, but specify what gaps (e.g., FFMI measurement standardization).

Conclusion:

• Line 320: "...considering the specific metabolic characteristics or the population..." – Change "or" to "of".

Tables and Figures

• Figure 2: Clarify the meaning of error bars in the legend.

• Table 1 (Page 32): n=371 vs. 349 elsewhere. Resolve this inconsistency.

• Table 2 (Page 32-33): Systolic BP mean (14.9 mmHg) is implausible; likely a typo (e.g., 114.9 mmHg).

• Table 4 (Page 35): "Female OR 2.78" – Conflicts with bivariate analysis (OR 1.10, Table 3). Verify adjustment effects or correct.

References (Pages 23-27)

• Formatting is mostly consistent, but some URLs (e.g., Ref 4) could be shortened with DOIs if available.

Summary of Key Issues

1. Scientific Inconsistency: Sample size discrepancy (349 vs. 371), systolic BP error in Table 2, and unexpected sex effect in Table 4 need resolution.

2. Methodological Clarity: Justify tertile use for FMI/FFMI and clarify consent documentation.

3. Grammatical Errors: Minor but frequent (e.g., "cut-offs point," incomplete sentences).

4. Discussion Depth: Expand on sex differences and FFMI findings.

Reviewer #3: In this manuscript, Dr. Curi-Quinto and colleagues sought to develop a HOMA-IR cut-off to detect metabolic syndrome (MetS) in Peruvian adolescents. In addition, they wished to identify modifiable risk factors correlating with HOMA-IR-defined MetS in this population. They identified HOMA-IR 3.9 as the ”optimal” cut-off point to define MetS with ~75% sensitivity/specificity. Adolescents designated to have MetS by HOMA-IR had a higher fat mass index and were less physically active.

GENERAL COMMENTS

I wish to bring to the authors’ attention that there is unfortunately a fundamental flaw in the concept of trying to develop a population-specific cut-off for HOMA-IR. The issue is that insulin assays are wildly unstandardized: it is well-known that one cannot compare insulin concentrations across laboratories. Thus, it is not feasible to use a universal cut-off to designate abnormally high insulin within any single population. This of course applies to HOMA-IR by extension. Thus, unless all Peruvian adolescents have their samples measured by the same insulin assay—which seems unlikely—identification of any cut-off in this cohort will be of little help to patients studied outside of the investigators’ clinic. The authors cite factors such as ethnicity, sex, and metabolic conditions affecting inter-population differences in HOMA-IR. I am convinced that insulin assay will have a far greater effect than any one of these factors. The HOMA-IR values reported in this study are very high compared to most reports, especially considering the degree of MetS present, which is likely related to the insulin assay used. At minimum, this issue should be extensively and openly discussed in Introduction, Discussion, and conclusions (including the abstract). Many references discuss the problem of insulin assay standardization, e.g. PMID 20040676. Others have previously shown marked inter-assay variation with respect to HOMA-IR (PMID 28660493).

Importantly, it is unclear why the authors wish to use HOMA-IR as a surrogate of MetS. MetS itself is an imperfect definition of IR, and trying to determine MetS by HOMA-IR will just increase diagnostic uncertainty. Clearly, the diagnostic performance of HOMA-IR to detect MetS in this population was not very good. All of the components of MetS are readily available in any clinic, easy to measure, and in aggregate are cheaper than measuring insulin. What is, then, the added benefit of HOMA-IR? Please provide your rationale for this approach.

I urge the authors to carefully proofread the text, as there are numerous typographical and grammatical errors.

SPECIFIC COMMENTS

1. There is a claim of ”high” sensitivity and specificity for HOMA-IR in Abstract and Discussion. They are moderate at most. Please revise.

2. The last sentence of the abstract results section cuts off. It reads: ”No association was found with diet, excess weight at infancy and.”

3. The ethics statement appears to contain some sort of a placeholder text. Please double-check.

4. Analytical methods for each laboratory test used should be described in detail. In particular those of glucose, insulin, triglycerides, and HDL-cholesterol.

5. Please report P-values for all statistical comparisons in the main text.

6. The HOMA-IR definition in Methods seems to contain erroneous units. Please confirm.

7. Page 3, line 57: mails = males

8. Page 3, lines 62-67. This section regarding the basis of hyperglycemia and insulin resistance requires some clarification. The basis for elevated blood glucose in IR/T2D is the inability of insulin to suppress hepatic glucose production, not impaired glucose uptake by cells. Hyperglycemia ensues in T2D, but MetS is specifically characterized by impaired fasting glucose. The latter sentence of this paragraph is very non-specific. Beta-cells secrete insulin.

9. Page 5, line 130: The authors must mean NON-distensible measuring tape?

10. Page 9, line 212: There should not be percentage sign (%) after ”3.5”.

11. Page 9, line 216: What does the number ”3.42” represent here?

12. Page 9, line 232: ”…about 8.5% had fasting hyperglycemia.” Do the authors mean impaired fasting glucose or literal diabetic hyperglycemia? Please clarify. In case of the latter, I would like to point out that HOMA-IR is not an appropriate tool to gauge IR in diabetic individuals, since the insulin response to blood glucose is subnormal in this population.

13. Page 10, line 244 reads: ”None of this variable has been associated with the IR.” Please clarify what this means. Do the authors suggest that low physical activity and high FMI have never been associated with IR before? If yes, this is simply untrue. None of the associations shown are new; they have been known for decades.

14. The FHCD abbreviation is not defined in the correct place. FMI is not defined at all.

Reviewer #4: This paper examines metabolic risk factors in a sample of Peruvian adolescents that have also had data collected during infancy. The authors use ROC curve analysis to generate a cut-off value for HOMA-IR to predict the presence of metabolic syndrome. This cut-off value was then used to split the participants into insulin resistant and non-insulin resistant groups to assess associated risk factors.

Overall the paper presents some important information in characterizing risk factors for metabolic syndrome in the adolescent Peruvian population. The suggestion of a HOMA-IR cut-off could also have important clinical applications and the authors could highlight this point more in their conclusion sections of the abstract and discussion.

The manuscript is mostly clear and easy to follow, however, some sections require restructuring of sentences to increase clarity. This will help with the overall readability of the manuscript and make the reporting of findings more accurate in some instances.

Abstract

The results section of the abstract needs some more detail to increase the clarity around the results being presented. For the OR’s presented, detail could be given about the classifications for “higher fat mass” and physical inactivity.

The conclusion of the abstract could be more relevant to the main aim of this study, in identifying a HOMA-IR cut-off value. What are the main ways in which this data could be used to help identify adolescents at risk of MS?

- Line 33. Incomplete sentence

- Line 39: Is this the mean HOMA-IR for the entire sample? More detail is needed here to make the sentence clear.

- Line 44 – incomplete sentence, ends in “and.”

- Line 45 – replace cut-offs with cut-off

Introduction

- Line 54 – Sentence unclear

- Line 56 – Children and adolescents are the most vulnerable population for what?

- Line 57 – change mails to males

- Line 58 – defining the adolescent age range here might help the reader with context for the rest of the paper.

- Line 62-67 – Perhaps you could rephrase here how hyperinsulinemia develops and be more specific on the consequences of hyperinsulinemia and insulin resistance. Particularly from line 65 onwards “cell metabolism changes…” This doesn’t make sense in the context of the following points. References are also required for these sentences.

Methods

- Line 110-114 - These sentences are long and unclear, please restructure for clarity and readability.

- Line 120 – “nutritional status” seems inaccurate to describe BMI

- Line 124 – This sentence is unclear, does it also relate to the first sentence of this paragraph discussing that BMI z-score was used for this.

- Line 147 – Sentence unclear, please define how the intake of food groups are used to define unhealthy and healthy in greater detail.

- Line 156 – Replace “him” with gender neutral term.

- Definition of metabolic syndrome – why were adult measures used for waist circumference cut-off points for metabolic syndrome. Using a Z-score cut-off point may be the most appropriate measure to use here and for the definition of metabolic syndrome?

Results

- Line 212 – It seems inappropriate to report the 95% confidence interval for the prevalence of obesity and overweight in the participants of the study.

- Line-217 – should this refer to figure 1?

- Line 219 – unclear what (IC 0.69 0.88) means.

- Line 220 – highlighting that the IR cut-off calculated above with the ROC is used to generate these groups. This would make things clearer for the reader.

- Line 235 – Physical activity is not significant in the bivariate analysis, please update here to discuss this a trend.

- Line 236 – a new sentence could be used here to discuss the results on excess weight during infancy and make this section clearer.

- Line 237 – should this be the presence of IR not MS?

- Discussion of the significant effect of sex that shows up in the multivariate analysis in the results is needed.

Other minor points

- Undefined abbreviations – HOMA-IR (not defined until line 71), ROC curve, FMI, DM2, CVD

- Abbreviations FHCD not placed after its term, BMI abbreviation used on second use. SD used in abstract but not defined until line 125.

Reviewer #5: This article by Curi-Quinto et al. is entitled : « Identification of a HOMA-IR cut-off point for cardio metabolic risk and modifiable risk factors in Peruvian adolescents ». This article was sent to Pone reviewers for a potential publication. In this study, the authors tried - first, to find a cut-off value for insulin-resistance and the rise of metabolic syndrome, using multiple regression analysis - second, to link cardio metabolic risk factors in a Peruvian sub population.

Line 44 end of the sentence is lacking.

Line 57 mails -> males

Table 1 is a stratified presentation for HOMA-IR showing an optimal cut-off for HOMA-IR (3.9).

Table 2 is the anthropometric profile for adolescence —> BMI, FFMI and cardio metabolic profile confirming the association of risk factors with BMI and IR. Interestingly, author couldn’t find any significant differences that would link infancy to the onset of IR. Here, it is not clear what the authors should look for.

Table 3 is a bivariate analysis of potential risk factors for IR. Here the presentation is hard to follow because of a mix of IS/MS as a reference. Authors should clarify.

Discussion should be more in phase with the results of this study and the initial question.

Figure 1 is the ROC curve in respect to sensitivity and specificity.

Table 4 is a multivariate regression model of the associated risk factors of IR in Peruvian adolescents.

Autjhors should clarify their study, first with a revision of the order of the figures/tables and their introduction/description in the manuscript. And then with a sequential discussion of theses results. Several aspects could be compared to recent results from the literature (Lee et al. 2023, Lozano et al. 2022, Rocca-Nacion et al. 2022, Zelada et al. 2016).

This is a very interesting and complete study on a defined sub population addressing cardiometabolik risk factors and HOMA-IR. Here, the novelty might be that some parameters (cardio metabolic risk factors) are not linked to MS/IR. To think about.

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PLoS One. 2026 Jul 10;21(7):e0351139. doi: 10.1371/journal.pone.0351139.r002

Author response to Decision Letter 1

26 Jan 2026

Response reviewer

We sincerely thank the reviewers and the editor for their thoughtful and constructive feedback. We have revised the manuscript accordingly and provide a point-by-point response to each comment below. Revisions in the manuscript have been highlighted to facilitate review.

Reviewer #1

1. Comment 1: The manuscript states the 349 adolescents were used from a total of 394 adolescents. However, Table 1 states that 371 adolescents were used, and Table 3 includes 106 adolescents.

Response: We clarified this inconsistency. A total of 371 adolescents had complete and valid data for key anthropometric, biochemical, and lifestyle variables and were included in the analysis. Among them, 106 adolescents were identified as having insulin resistance (IR), based on the HOMA-IR cut-off value determined in the ROC analysis. These details have been clarified in the Methods section.

2. Comment 2: Inclusion/exclusion criteria unclear.

Response: We have added a detailed description of inclusion and exclusion criteria in the Methods section. Exclusion criteria included missing data for fasting insulin, glucose, anthropometric measures, or lifestyle variables, as well as the presence of chronic conditions such as type 1 diabetes or other metabolic diseases that could affect insulin resistance.

3. Comment 3: Figure 2 shows standard error bars but labeled SD or CI.

Response: Thank you. We have corrected Figure 2 by replacing the standard error bars with 95% confidence intervals, as now indicated in the legend and Results section.

4. Comment 4: T-test assumes normality, but no check was reported.

Response: We added a description of normality testing using the Shapiro-Wilk test before applying parametric tests.

5. Comment 5: Unclear variable selection for logistic regression.

Response: We included a paragraph explaining that variables with p < 0.10 in bivariate analysis were entered into the multivariate logistic regression model.

6. Comment 6: Were interaction effects tested?

Response: We tested first-order interactions between key predictors and found no statistically significant interactions. This is now reported in the Statistical Analysis section.

7. Comment 7: Any correction for multiple testing?

Response: Thank you for pointing this out. We have now added a clarification in the Statistical Analysis section indicating that Bonferroni correction was applied for key comparisons in the multivariate model.

8. Comment 8: Conclusion in the abstract overstates predictive value.

Response: We appreciate your insight. We have revised the Abstract and Discussion to reflect that the positive predictive value (PPV) is low due to the relatively low prevalence of metabolic syndrome in the sample, and that HOMA-IR is more useful as a rule-out tool (high NPV). We now refer to sensitivity and specificity as "moderate" and contextualize the clinical implications accordingly.

9. Incomplete sentence in Abstract.

Corrected. The sentence now reads: “No association was found with diet, excess weight at infancy, and family history of cardiometabolic diseases.”

10. Typo: “Mails” instead of “Males”.

Corrected.

11. No subject number (n) in Figures 1 & 2.

We have added the sample size (n=371) to the captions of Figures 1 and 2 to improve clarity.

12. AUC for HOMA-IR should be added to Abstract.

Included. The Abstract now reads: “The area under the curve (AUC) for HOMA-IR to predict MetS was 0.79 (95% CI: 0.69–0.88), indicating good discriminatory capacity.”

Response to Reviewer #2

Abstract (Pages 1, 9–10)

Comment: Line 28: Suggest replacing “Currently, there is a need...” with “Promoting healthy lifestyles in adolescence is critical...”

Response: Thank you. We revised the sentence for conciseness, as suggested. The revised sentence now reads: “Promoting healthy lifestyles in adolescence is critical...”

Comment: Line 33: Clarify discrepancy between 349 adolescents (Methods) and 371 (Table 1).

Response: Thank you for noting this. We have clarified in both the Methods and Results that 371 adolescents had complete data and were included in the main analysis.

Comment: Line 39: Specify “Mean HOMA-IR.”

Response: Corrected as suggested. The sentence now reads: “Mean HOMA-IR was 3.29 (SD 1.71).”

Comment: Line 43: Sentence is incomplete.

Response: Revised for clarity. The corrected sentence is: “No association was found with diet, excess weight at infancy, or family history of chronic diseases.”

Comment: Line 45: “A cut-offs point” → “A cut-off point.”

Response: Corrected as suggested.

Introduction (Pages 11–12)

Comment: Replace vague terms like “negative changes” with more specific language (e.g., “unhealthy diets”).

Response: Revised for clarity. The sentence now reads: “Early adoption of unhealthy lifestyles such as poor diet and physical inactivity…”

Comment: Line 56: Typo “mails” → “males.”

Response: Corrected.

Comment: Add Peruvian obesity or MS prevalence data if available.

Response: We have added national prevalence data: “In Peru, the national prevalence of overweight and obesity in adolescents reaches 24,8%...” (source cited in the revised manuscript).

Comment: Clarify MS definition, impact, and include modifiable factors.

Response: We expanded the paragraph to briefly discuss modifiable factors (physical activity, diet) and their role in MS prevention.

Comment: Line 62: Consider citing a foundational reference (e.g., Reaven, 1988).

Response: We now cite Reaven (1988) as a foundational source for the insulin resistance concept.

Comment: Adjust brackets “[6).” and [11,12].”

Response: All citation brackets have been reviewed and corrected for consistency.

Comment: Emphasize HOMA-IR’s population-specific cut-off adaptability.

Response: We added this information explicitly: “...making it suitable for identifying population-specific cut-off values.”

Comment: Soften claim of no prior Peruvian studies.

Response: We changed the wording to “few studies”.

Comment: Explicitly link cut-off identification to public health interventions.

Response: We added: “...to inform early screening strategies and targeted interventions.”

Comment: Line 77:

It mentions “To the best of our knowledge,” but does not cite a systematic review. It is suggested that an exploratory review be incorporated or mentioned. The criterion also includes two additional criteria in its definition.

Response:

We have modified the text to reflect this limitation.

Change made:

"...few studies have addressed this, and no systematic review to date has summarized HOMA-IR thresholds for Peruvian adolescents.”

Comment: Lines 78–80:

The objective of the study is clear, but it is suggested that it be explicitly linked to preventive strategies.

Response:

We added a sentence linking the objective to practical utility.

Change made:

"...as well as identifying the modifiable risk factors of IR in a longitudinal study of Peruvian adolescents, to inform targeted prevention strategies.”

Materials and Methods (Pages 13–16)

Comment: Line 107: Use “who have” instead of “that have.”

Response: Corrected.

Comment: Clarify the sample size and exclusion criteria (Line 115).

Response: Explained in text that 371 participants had complete data from an original sample of 394.

Comment: Provide more details on consent procedures.

Response: We added: “Written informed consent was obtained from parents or legal guardians.”

Comment: Capitalization of equipment names.

Response: Corrected to maintain consistency (e.g., “Seca scale”).

Comment: Clarify “<2SD” to match WHO standards.

Response: Changed to “<−2 SD” for consistency.

Comment: “Total glucose” is unusual terminology.

Response: Revised to “fasting glucose.”

Comment: Add units for blood pressure.

Response: Units (mmHg) were added.

Comment: Justify use of tertiles for FMI/FFMI.

Response: A justification was added: “Tertiles were used due to the absence of validated pediatric cut-off points in the Peruvian population.”

Comment: Clarify physical activity classification.

Response: The classification was clarified. We added: “Low physical activity was defined as a score <3 on the 1–5 scale.”

Results (Pages 17–18)

Comment: Resolve discrepancies in sample size.

Response: We revised the text to explain that 371 had partial data and were included in descriptive summaries (Table 1).

Comment: Table 2 systolic BP value of 14.9 mmHg is implausible.

Response: Thank you. This was a typographical error and has been corrected to “114.9 mmHg.”

Comment: Clarify whether cut-off was stratified by sex.

Response: Text has been revised to clarify that the 3.9 HOMA-IR cut-off was derived from the overall sample, not stratified by sex.

Comment: Clarify prevalence percentages and fix wording.

Response: We corrected all prevalence estimates to match those reported in tables and clarified interpretation. Also corrected grammatical issues such as “None of this variable...” to “None of these variables...”

HOMA-IR formula is correct. ROC analysis description is sound but could note software used (assumed Stata from Line 204).

Response: Changed

Statistical Analysis (Lines 195-204):

Line 199: "Bivariate binomial logistic regression" – Redundant; "bivariate logistic regression" suffices.

Response: Modified

Line 201: "Model 3" and "Model 4" – Should be "Model 1" and "Model 2" to match Table 4 (Page 35).

Response: Modified

Results (Pages 17-18)

Line 211 (Page 17): "Mean age of the adolescents was 14.5 (0.1 SD) years, and $48.1% of the population was female. Mean HOMA-IR was 3.3 (95% CI 3.1; 3.5%)."

Response: Modified

Line 212: "Prevalence of obesity and overweight" – Matches Table 2 but clarify if z-BMI-based.

Response: It was clarified that the classification of overweight/obesity was based on z-BMI according to WHO standards.

Line 214-215: "Table 1 shows the distribution of the HOMA-IR percentiles by sex, body mass index and MS."

o Comment: Table 1 reports n=371, mean HOMA-IR 3.29 (SD 1.71), slightly differing from 3.3 (CI 3.1-3.5). Minor rounding discrepancy; clarify true value.

Response: 3.29 was maintained as the primary reported value.

Line 216-217: "The mean HOMA-IR was significantly higher in overweight and obese adolescents compared with those with healthy weight $(3.42; 5.17 vs 2.75)$, and those with MS compared with those without MS (5.31 vs 3.11)."

o Comment: Matches Table 1 means. "No significant difference was found by sex" aligns with Table 1 (3.30 vs. 3.27), but Discussion doesn’t explore this despite Table 4’s sex effect.

Response: We confirm that the mean HOMA-IR values reported in Lines 216–217 are consistent with Table 1. We have now acknowledged in the Discussion section that, although there was no significant difference in mean HOMA-IR between males and females (3.30 vs. 3.27), sex emerged as a significant factor in the multivariate logistic regression (Table 4). This suggests that sex may interact with other risk factors in influencing insulin resistance and warrants further investigation. The revised discussion now addresses this apparent inconsistency and contextualizes the potential role of sex in metabolic risk.

Line 218-219: "Table 1 presents the optimal cutoff points for HOMA-IR to predict MS in males and females. A HOMA-IR with a cutoff of 3.9 showed a sensitivity of $72.4% and a specificity of $75.4 %$; with an area under the curve ROC of 0.79 (IC 0.69 0.88)."

o Comment: Table 1 doesn’t stratify cut-offs by sex—text misstates this. AUC CI should be "0.69-0.88."

Response: We have corrected the text to reflect that Table 1 presents a single HOMA-IR cutoff point for the total sample, not stratified by sex. The sentence now reads:

“Table 1 presents the optimal cutoff point for HOMA-IR to predict MS in the total sample. A HOMA-IR cutoff of 3.9 showed a sensitivity of 72.4% and a specificity of 75.4%, with an area under the ROC curve (AUC) of 0.79 (95% CI: 0.69–0.88).”

Line 218: "A HOMA-IR with a cutoff of 3.9" – Remove "with" for conciseness: "A HOMA-IR cutoff of 3.9."

Response: Modified

Line 220-222: "Table 2 displays the anthropometric and cardiometabolic adolescent profile by IR status... significantly higher values of z-score for BMI/age, body composition indicators (FFMI and FMI) and cardiometabolic indicators..."

o Comment: Matches Table 2, but systolic BP (14.9 mmHg, Table 2) is implausible (likely 114.9 mmHg). Correct this to avoid contradiction.

Response:

Thank you for your observation. You are correct — this was a typographical error in Table 2. The systolic blood pressure for insulin-resistant adolescents should read 114.9 mmHg, not 14.9 mmHg. We have corrected this value in Table 2 and revised the Results section accordingly to maintain consistency. We appreciate your attention to detail, which helped us correct this inconsistency.

Line 222: "...waist circumference, triglycerides, glucose, blood pressure, and insulin; while the HDL was significantly lower." – Change semicolon to a period for better sentence structure.

Response: We agree that replacing the semicolon with a period improves sentence clarity and structure. We have revised the sentence accordingly:

“…waist circumference, triglycerides, glucose, blood pressure, and insulin. HDL was significantly lower.”

Line 226-230: "The proportion of cardiometabolic risk factors by IR is presented in Figure 2... at least six out of ten adolescents had at least one cardiometabolic risk factor (62%)... low levels of HDL (62.3% and $39.6 %$, respectively)."

Response: We have clarified in the revised Results section that the proportions reported in Figure 2 (62.3% for IR and 39.6% for non-IR adolescents with low HDL) refer to the percentage of adolescents in each group with HDL levels below the clinical threshold of <40 mg/dL for males and <50 mg/dL for females. We have now specified this cutoff and calculation method in the text. We also double-checked the 39.6% value, which corresponds to the correct prevalence of low HDL among non-IR adolescents.

Line 233-234: "The prevalence of all the cardiometabolic risk factors was significantly higher in adolescents with IR compared with those without IR..."

o Comment: Matches Figure 2 and Table 2 (p=0.001 for diastolic BP), but systolic BP typo affects interpretation. Adjust accordingly.

Response: The prevalence of all the cardiometabolic risk factors was significantly higher among adolescents with insulin resistance compared to those without it, as shown in Figure 2. This includes elevated blood pressure, which was confirmed for both systolic and diastolic measurements (p < 0.001). Please note that the originally reported systolic blood pressure value in Table 2 contained a typographical error and has now been corrected to accurately reflect the true group mean.

Line 235-238: "Table 3 presents the results of the bivariate analysis... Low levels of physical activity, overweight and obesity in adolescence, FMI, FFMI and presence of excess weight were significantly associated with the presence of MS."

o Comment: Text says "MS," but context implies "IR" (Table 3 is IR-based). Correct this. Data match Table 3.

Response: We have now corrected the sentence to indicate that the associations reported in Table 3 refer to insulin resistance (IR). The revised sentence now reads:

“Table 3 presents the results of the bivariate analysis. Low levels of physical activity, overweight and obesity in adolescence, FMI, FFMI, and presence of excess weight were significantly associated with the presence of insulin resistance (IR). This correction ensures alignment between the narrative and the statistical analysis presented.

Line 240-244: "In the adjusted analysis using multiple logistic regression models (Table 4), low physical activity and high FMI were independently associated to IR... None of this variable has been associated with the IR."

o Comment: Matches Table 4 (OR 2.08 for low PA, 16.03 for high FMI). "Female" OR 2.78 contradicts Table 3 (OR 1.10)—explain adjustment effect.

Response: We have reviewed the results and clarified that the difference in the OR for the variable f

Attachment

Submitted filename: RESPONSE LETTER 26 01 26.docx

pone.0351139.s004.docx^{(14.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0351139.r003

Decision Letter 1

Neftali Eduardo Antonio-Villa, Neftali Eduardo Antonio-Villa

13 Mar 2026

<div>PONE-D-25-02583R1-->-->IDENTIFICATION OF A HOMA-IR CUT-OFF POINT FOR CARDIOMETABOLIC RISK AND MODIFIABLE RISK FACTORS IN PERUVIAN ADOLESCENTS-->-->PLOS One

Dear Dr. Curi-Quinto,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Apr 27 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Neftali Eduardo Antonio-Villa, MD PhD

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #5: (No Response)

**********

-->2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #5: Yes

**********

-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: No

Reviewer #5: I Don't Know

**********

-->4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #5: No

**********

-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #5: Yes

**********

-->6. Review Comments to the Author

Reviewer #1: I have no additional comments to improve the resubmission. All of my concerns were addressed by the author.

Reviewer #2: Dear Editor,

I have carefully reviewed the revised version of manuscript PONE-D-25-02583R1 entitled “Identification of a HOMA-IR cut-off point for cardiometabolic risk and modifiable risk factors in Peruvian adolescents.”

The authors have made substantial efforts to address reviewer comments. The manuscript has improved in structure, clarity, and contextualization. The study addresses an important public health issue and provides regionally relevant data on insulin resistance and metabolic risk in adolescents.

However, several critical issues remain unresolved in the revised manuscript:

1. Persistent inconsistencies in reported sample size (349 vs 371 participants across sections and tables).

2. Implausible systolic blood pressure values are still present in Table 2.

3. Ethical section contains placeholder text that has not been properly revised.

4. Ambiguity in the HOMA-IR calculation formula and unit reporting.

5. Overstatement of diagnostic performance (sensitivity/specificity described as “high”).

6. Minor but recurrent grammatical and reporting inconsistencies.

While these issues appear correctable, they are substantial and affect the scientific reliability and reporting integrity of the manuscript.

Therefore, I recommend Major Revision before the manuscript can be considered for acceptance.

Once numerical inconsistencies, methodological clarifications, and reporting errors are fully corrected, the manuscript has the potential to make a meaningful contribution to the literature on adolescent metabolic risk in Latin America.

Sincerely,

Reviewer #5: This article by Curi-Quinto and collaborators is entitled : « Identification of a HOMA-IR cut-OFF point for cardiometabolis risk and modifiable risk factors in Peruvian adolescents ». This is the first revised version of a manuscript firstly sent last year to pone journal.

The aim of this article is to identify cut-off values from HOMA-IR assay in a cohort of Peruvian adolescents in order to propose an anticipated diagnosis for metabolic syndrome.

The Table 1 represents the percentile distribution of the HOMA-IR in the cohort studied and with criteria stratifications. Means from Z-score BMI/age revealed an expected link with overweight and obesity status.

The Table 2 is the repartition within the cohort studied following anthropometric and cardiometablic data. Similarly, BMI and BMI/Age showed a significant association with IR. Regarding cardiometabolic aspects, all parameters showed a significant association with IR but Diastolic blood pressure.

The Table 3 is a relative prediction of developing IR, following risk factors. Obesity and a high Fat mass index are significantly associated with IR.

The Table 4 is the multivariate regression model of the identified risk factors in the Peruvian adolescents population. This table only represents the female subgroup, adding low physical activity to the previous associations.

The Discussion part summarizes the results obtained with emphasis on the associations of BMI and cardiometabolic parameters. The independence of physical inactivity and high fat mass index should be tested and reviewed.

Figure 1 is a ROC curve to determine the optimal cut-off. This figure should be repositioned in the text as the starting element.

Figure 2 is the mean prevalence of the risk factors mentioned earlier within IR/not IR individuals. This figure should be repositioned in the text with a few words of introduction and purpose.

Some Typo :

Line 44 the end of the sentence is missing

Line 57 mails -> males

Line 75, 150 -> al. —> italic

Table 2 Cardio-metabolic —> Cardiometabolic

Line 288 y —> years old

And Sens :

Line 67 a reference is lacking linking hyperglycemia to hypertension at last

Line 76 « to the best of our knowledge » —> rephrase

Table 1 n = 371?

As a summary, authors should clarify their study, first with a revision of the order of the figures/tables and their introduction/description in the manuscript. And then with a sequential discussion of theses results. Several aspects could be compared to recent results from the literature (Lee et al. 2023, Lozano et al. 2022, Rocca-Nacion et al. 2022, Zelada et al. 2016).

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #5: No

**********

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Attachment

Submitted filename: Reviewer Comments on PONE-D-25-02583_R1.docx

pone.0351139.s003.docx^{(14.6KB, docx)}

PLoS One. 2026 Jul 10;21(7):e0351139. doi: 10.1371/journal.pone.0351139.r004

Author response to Decision Letter 2

7 Apr 2026

Reviewer #2: Dear Editor,

However, several critical issues remain unresolved in the revised manuscript:

1. Persistent inconsistencies in reported sample size (349 vs 371 participants across sections and tables).

Thank you for your comment.

To clarify, all our main analyses were conducted using a sample of 371 participants. However, data on Fat Free Mass and Fat Free Mass Index were available for only 349 participants. It is important to note that the subset of 349 participants was used exclusively for a regression analysis (Table 3) and not for the main analyses, such as the identification of HOMA-IR.

Therefore, to avoid confusion, we have clarified this in the Study Design and Population section and have corrected the remaining inconsistencies throughout the manuscript.

From a total of 394 adolescents, we analyzed data of 371 participants that have complete data from infancy and adolescence. For fat-free mass (FFM) and fat-free mass index (FFMI), data were available for 349 participants; thus, related bivariate analyses used this subsample, while others used the full sample (n = 371).

See lines: 116 - 117

2. Implausible systolic blood pressure values are still present in Table 2.

Thank you for your observation. We acknowledge that there was a typographical error, which has now been corrected.

See lines: 223

3. Ethical section contains placeholder text that has not been properly revised.

Thank you for identifying the error. We have corrected it as follows:

The ethics committees of the Instituto de Investigación Nutricional (IIN), Lima-Peru, approved this study under the number 372-2017/CIEI-IIN. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. We obtained written informed consent from all participants prior to their inclusion as well as the assent from the adolescents.

See lines: 180-183

4. Ambiguity in the HOMA-IR calculation formula and unit reporting.

Thank you for the clarification. We have revised the wording of this section and have also added a relevant reference.

See lines: 170-172

5. Overstatement of diagnostic performance (sensitivity/specificity described as “high”).

Thank you for the clarification. In the Discussion section, we have revised this statement to a more neutral wording and removed the overstatement, as follows:

See lines: 246-248

6. Minor but recurrent grammatical and reporting inconsistencies.

The manuscript has been thoroughly reviewed, and all errors have been corrected.

While these issues appear correctable, they are substantial and affect the scientific reliability and reporting integrity of the manuscript.

Therefore, I recommend Major Revision before the manuscript can be considered for acceptance.

Sincerely,

Reviewer #5:

This article by Curi-Quinto and collaborators is entitled : « Identification of a HOMA-IR cut-OFF point for cardiometabolis risk and modifiable risk factors in Peruvian adolescents ». This is the first revised version of a manuscript firstly sent last year to pone journal.

The aim of this article is to identify cut-off values from HOMA-IR assay in a cohort of Peruvian adolescents in order to propose an anticipated diagnosis for metabolic syndrome.

The Table 3 is a relative prediction of developing IR, following risk factors. Obesity and a high Fat mass index are significantly associated with IR.

Thank you for the observation.

The methodology section has been expanded in response to your comments.

Figure 1 is a ROC curve to determine the optimal cut-off. This figure should be repositioned in the text as the starting element.

Thank you for the observation.

We have positioned Figure 1 as the first result in the Results section.

Results

The HOMA-IR cutoff identified was 3.9, yielding a sensitivity of 72.4% and a specificity of 75.4%. The area under the ROC curve was 0.79 (95% CI: 0.69–0.88) (Figure 1).

See lines: 195-196

Figure 2 is the mean prevalence of the risk factors mentioned earlier within IR/not IR individuals. This figure should be repositioned in the text with a few words of introduction and purpose.

We have positioned Figure 2 as the third result in the Results section, following Figure 1 and Table 1.

Some Typo :

Line 44 the end of the sentence is missing

Thank you for the observation.

The missing information has been added.

excess weight at infancy and FHCD.

See lines: 44

Line 57 mails -> males

Thank you for the observation.

The missing information has been added.

% in males and

See lines: 57

Line 75, 150 -> al. —> italic

Thank you for the observation.

The missing information has been added.

ped by Matthews et al. and this

loped by Kowalski et al, 1997 and

See lines: 75; 148

Table 2 Cardio-metabolic —> Cardiometabolic

Thank you for the observation.

The indicated issue has been corrected.

Line 288 y —> years old

Thank you for the observation.

The missing information has been added.

adolescents (7–14 years old); in both sexes

See lines: 268

And Sens :

Line 67 a reference is lacking linking hyperglycemia to hypertension at last

The reference has been added.

Sowers, J. R., Standley, P. R., Ram, J. L., Jacober, S., Simpson, L., & Rose, K. (1993). Hyperinsulinemia, insulin resistance, and hyperglycemia: contributing factors in the pathogenesis of hypertension and atherosclerosis. American journal of hypertension, 6(7 Pt 2), 260S–270S. https://doi.org/10.1093/ajh/6.7.260s

See lines: 68

Line 76 « to the best of our knowledge » —> rephrase

Thank you for the clarification. The wording of the sentence has been revised as follows:

Nonetheless, no studies in Peruvian adolescents have proposed specific cut-off points for HOMA-IR classification.

See lines: 79-80

Table 1 n = 371?

Yes, there was an error in the description of the total sample in the Methods section. The correct sample size is 371 adolescents. However, only the variables Fat Free Mass Index and Fat Mass Index in Tables 2 and 3 include data from 349 participants. We have clarified this in the Study Design and Population section to avoid confusion.

For fat-free mass (FFM) and fat-free mass index (FFMI), data were available for 349 participants; thus, related bivariate analyses used this subsample, while others used the full sample (n = 371).

See lines: 116-117

Following your suggestion, we have carefully integrated the three recommended articles (Lee et al. 2023; Lozano et al., 2022; Rocca--Nación et al., 2022) into the manuscript. These references have been incorporated in a sequential and coherent manner within the Discussion, strengthening both the conceptual framework and the interpretation of our findings. As a result, the overall flow, depth, and clarity of the Discussion have been substantially improved.

See lines: 251-255; 257-261; 269-272

Attachment

Submitted filename: Response Letter 07 04 2026.docx

pone.0351139.s006.docx^{(681KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0351139.r005

Decision Letter 2

Neftali Eduardo Antonio-Villa, Neftali Eduardo Antonio-Villa, Neftali Eduardo Antonio-Villa

11 May 2026

-->PONE-D-25-02583R2-->-->IDENTIFICATION OF A HOMA-IR CUT-OFF POINT FOR CARDIOMETABOLIC RISK AND MODIFIABLE RISK FACTORS IN PERUVIAN ADOLESCENTS-->-->PLOS One

Dear Dr. Curi-Quinto,

Please submit your revised manuscript by Jun 25 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

-->

As the corresponding author, your ORCID iD is verified in the submission system and will appear in the published article. PLOS supports the use of ORCID, and we encourage all coauthors to register for an ORCID iD and use it as well. Please encourage your coauthors to verify their ORCID iD within the submission system before final acceptance, as unverified ORCID iDs will not appear in the published article. Only the individual author can complete the verification step; PLOS staff cannot verify ORCID iDs on behalf of authors.

We look forward to receiving your revised manuscript.

Kind regards,

Neftali Eduardo Antonio-Villa, MD PhD

Academic Editor

PLOS One

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Please, address the final issues raised by the referees. Both of them, including this academic editor, agree that once the comments have been addressed, it can be suitable for publication.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #5: All comments have been addressed

**********

-->2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #5: Yes

**********

-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #2: Yes

Reviewer #5: Yes

**********

-->4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #5: Yes

**********

-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #5: Yes

**********

-->6. Review Comments to the Author

Reviewer #2: Before this manuscript can be accepted for publication, the authors must address the following minor but critical corrections:

Abstract Sample Size Inconsistency: In response to Reviewer #2 regarding sample size confusion, the authors clarified in the main text that the total sample is 371, with a sub-sample of 349 used specifically for FFM/FFMI analyses. Unfortunately, the authors neglected to update the Abstract, which still states: "A sample of 349 adolescents...". The Abstract must be updated to reflect the full cohort of 371.

Abstract Diagnostic Overstatement: The authors agreed to remove the subjective descriptor "high" when referring to sensitivity and specificity, modifying the Discussion accordingly. However, this overstatement was left intact in the Abstract, which still reads: "...with high sensitivity (72.4%) and specificity (75.4%).". This must be amended to match the neutral tone of the main text.

Scientific Typographical Error in Table 1: While the authors successfully corrected the HOMA-IR formula units in the Methods section (Lines 170-172) to µU/mL and mmol/L, the footnote for Table 1 contains a major scientific typo. It defines the calculation using fasting insulin in µU/L and fasting glucose in nmol/L. These units are incorrect for a divisor of 22.5 and must be corrected to match the main text.

Recommendation: Minor Revisions.

The core science, data, and conclusions of the study are sound. Once the authors align the Abstract and Table 1 footnotes with the corrections, they have already established in the main text, the manuscript will be highly suitable for publication and will make a valuable contribution to the understanding of adolescent metabolic risk in Latin America.

Reviewer #5: The article entitled « Identification of a HOMA-IR cut-off point for cardiometabolic risk and modifiable risk factors in Peruvian adolescents » by Curi-Quinto et al. is now in a second round of revision in Pone journal for a potential acceptance for publication.

To summarize, the aim of this study is to identify cut-off values associated with metabolic syndrome in a Peruvian adolescents population, with a distribution of HOMA-IR and a receiver operating characteristic (ROC). A multiple logistic regression analysis was applied concomitantly in order to identify the risk factors associated with insulin resistance.

Authors have diligently answered to the previous remarks from referees.

Please find enclosed few additional remarks regarding typos :

Line 192 a reference is lacking.

Police adjustments Line 162->167, Table 1, Line 501 -> 504.

**********

-->7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.-->

Reviewer #2: No

Reviewer #5: No

**********

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

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-->

Attachment

Submitted filename: Reviewer Comments on PONE-D-25-02583_R2.pdf

pone.0351139.s005.pdf^{(135.2KB, pdf)}

PLoS One. 2026 Jul 10;21(7):e0351139. doi: 10.1371/journal.pone.0351139.r006

Author response to Decision Letter 3

13 May 2026

Reviewers' comments:

1. Abstract Sample Size Inconsistency: In response to Reviewer #2 regarding sample size confusion, the authors clarified in the main text that the total sample is 371, with a sub-sample of 349 used specifically for FFM/FFMI analyses. Unfortunately, the authors neglected to update the Abstract, which still states: "A sample of 349 adolescents...". The Abstract must be updated to reflect the full cohort of 371.

Thank you for the observation. We have corrected the error.

See lines: 33

2. Abstract Diagnostic Overstatement: The authors agreed to remove the subjective descriptor "high" when referring to sensitivity and specificity, modifying the Discussion accordingly. However, this overstatement was left intact in the Abstract, which still reads: "...with high sensitivity (72.4%) and specificity (75.4%).". This must be amended to match the neutral tone of the main text.

Thank you for the observation. The issue noted has been removed.

See lines: 40

3. Scientific Typographical Error in Table 1: While the authors successfully corrected the HOMA IR formula units in the Methods section (Lines 170-172) to µU/mL and mmol/L, the footnote for Table 1 contains a major scientific typo. It defines the calculation using fasting insulin in µU/L and fasting glucose in nmol/L. These units are incorrect for a divisor of 22.5 and must be corrected to match the main text.

Thank you for the observation. We have added the correct sample.

See table 1.

Attachment

Submitted filename: Response Letter 11 05 26.docx

pone.0351139.s007.docx^{(136.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0351139.r007

Decision Letter 3

Neftali Eduardo Antonio-Villa, Neftali Eduardo Antonio-Villa, Neftali Eduardo Antonio-Villa, Neftali Eduardo Antonio-Villa

24 May 2026

IDENTIFICATION OF A HOMA-IR CUT-OFF POINT FOR CARDIOMETABOLIC RISK AND MODIFIABLE RISK FACTORS IN PERUVIAN ADOLESCENTS

PONE-D-25-02583R3

Dear Dr. Curi-Quinto,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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pone.0351139.s001.docx^{(27KB, docx)}

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Submitted filename: RESPONSE LETTER 26 01 26.docx

pone.0351139.s004.docx^{(14.2KB, docx)}

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Submitted filename: Reviewer Comments on PONE-D-25-02583_R1.docx

pone.0351139.s003.docx^{(14.6KB, docx)}

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Submitted filename: Response Letter 07 04 2026.docx

pone.0351139.s006.docx^{(681KB, docx)}

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Submitted filename: Response Letter 11 05 26.docx

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Data Availability Statement

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PERMALINK

Identification of a HOMA-IR cut-off point for cardiometabolic risk and modifiable risk factors in peruvian adolescents

Katherine Curi-Quinto

Fabian Vasquez

Melissa Abad

Fabiola Lazarte

Mary Penny

Juana del Valle-Mendoza

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Study design and population

Adolescence nutritional status

Adolescent cardiometabolic risk factors

Adolescent body composition

Diet and physical activity

Nutritional status at infancy and family history of chronic diseases

Definition of Metabolic Syndrome

Definition of Insulin resistance

Ethical considerations

Statistical analysis

Results

Fig 1. ROC curve to determine the optimal cutoff value of HOMA-IR for metabolic syndrome diagnosis in adolescents.

Table 1. Percentile distribution of the HOMA-IR in the overall sample and stratified by sex, body mass index and metabolic syndrome.

Fig 2. Mean prevalence of cardiometabolic risk factors by the presence of insulin resistance based on HOMA-IR values.

Table 2. Anthropometric and cardiometabolic adolescent profile by insulin resistance.

Table 3. Potential risk factors of insulin resistance in adolescents.

Table 4. Multivariate regression model of the associated risk factors of insulin resistance in Peruvian Adolescents.

Discussion

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Neftali Eduardo Antonio-Villa

Roles

Author response to Decision Letter 1

Decision Letter 1

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Roles

Author response to Decision Letter 2

Decision Letter 2

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Roles

Author response to Decision Letter 3

Decision Letter 3

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Roles

Acceptance letter

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Neftali Eduardo Antonio-Villa

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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