Associated factors, concordance, and trends of obesity: body mass index, abdominal waist, and waist- to-height ratio between 2019 and 2022. Analysis of nine national surveys

doi:10.21203/rs.3.rs-3745026/v1

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Associated factors, concordance, and trends of obesity: body mass index, abdominal waist, and waist- to-height ratio between 2019 and 2022. Analysis of nine national surveys

https://doi.org/10.21203/rs.3.rs-3745026/v1

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Introduction: Obesity is a global epidemic affecting millions of people worldwide. Its diagnosis and treatment are crucial for the prevention of associated chronic diseases.

Objectives: To assess the prevalence of obesity according to different diagnostic criteria, analyze the concordance between various diagnostic methods, and identify associated factors.

Methods: Peru's Demographic and Family Health Survey (ENDES) from 2019 to 2022 was utilized. The cut-off points for defining obesity were ≥ 30 for body mass index (BMI), the ATPIII criteria for waist circumference (WC-ATPIII), and 0.56 for the waist-to-height ratio (WHtR). Concordance analyses were performed to compare diagnostic methods and regression analyses were conducted to identify associated factors.

Results: The prevalence of obesity according to BMI, WC-ATPIII, and WHtR was 25.65%, 42.04%, and 46.49%, respectively. The concordance between the three criteria was evaluated through the Kappa index. The concordance between obesity by BMI and WC-ATPIII was 0.5141. The concordance between BMI and WHtR was 0.5099. Finally, the concordance between WC-ATPIII and WHtR was 0.7514. Men showed a lower prevalence of obesity compared to women. The obesity trend increased overall during the study period, with marked differences in prevalence according to the obesity measure used.

Conclusions: The findings reveal differences in obesity prevalence according to the diagnostic method employed and underscore the need to consider multiple approaches to assess obesity. The results significantly affect public health and provide a foundation for future interventions and policies to combat obesity in Peru.

Obesity

Body Mass Index

Waist Circumference

Waist-Height Ratio

Public Health (Source: MeSH NLM)

For over two decades now, the World Health Organization (WHO) has labeled being significantly obese a worldwide dilemma. This label has motivated health groups and administrations to work together on plans dealing with this issue from medical, political, financial, and societal views, as reference number one discusses ⁽¹⁾.

Obesity issues have spread around the planet, and the count keeps rising. As indicated by WHO, more than 1.9 billion grown-ups were heavier in 2016, of which over 650 million were seriously obese ⁽¹⁾, and this example is as yet expanding ⁽²⁾. In Latin America, Peru isn't invulnerable to this developing pattern. While obesity rates in Peru are contrasted with different nations, it has recently seen a massive increment. An investigation conducted in 2022 showed that nearly 20.5% of Peruvian grown-ups were seriously obese, with more noteworthy rates in urban zones and among ladies ⁽³⁾. This developing recurrence underscores the requirement for powerful open well-being mediations and target strategies to handle obesity in Peru and worldwide.

At present, several strategies can be employed to diagnose obesity. The gold standard, or the most precise methodology for quantifying obesity, is body substance resonance, which is magnetic or computed tomography, which thoroughly evaluates fat and lean mass throughout the body ^(4,5). On the other hand, because of their high expense and necessity for specialized gear, these techniques are impractical for utilization in most clinical or investigative surroundings. Additional signs, like Body Mass Listing (BMI), waist circumference (WC), and waist-to-height percentage (WHtR), are also extensively applied owing to their simplicity and reduced cost ^(6,7).

Nevertheless, differences that exist among the various signs used must be acknowledged. BMI, though extensively recognized and applied, has been criticized for not distinguishing between fat mass and muscle mass, which can lead to misclassifications in some groups of individuals, like athletes ⁽⁸⁾. Meanwhile, WC and WHtR offer a more direct assessment of abdominal fat, which has been more intimately connected to metabolic risk. Investigations into specific indicators have discovered that these signals may forecast obesity-related health risks more accurately than BMI, including cardiovascular diseases and type 2 diabetes ⁽⁶⁾. However, the lack of agreement on cut-off points and inconsistency in measurement strategies can lead to disparities in obesity categorization among these methods, underscoring the necessity for reasonable assessment and potentially the utilization of numerous indicators in clinical practice ^(7,9,10)

Considering the contrasts between these signs and starting from the premise that the predominance of obesity ought to be steady, paying little mind to the marker utilized, this considers four objectives. First, we seek to determine the prevalence of obesity in Peru between 2014 and 2022 according to BMI, WC, and WHtR. Second, the aim is to establish agreement between these three obesity markers since it is expected that, if they all adequately measure obesity, they should yield consistent results. The third place is to know the trend of nutritional states, ranging from low weight to type III obesity, in the period above. Finally, the fourth objective is to identify and analyze the factors associated with obesity measured by these three markers to understand the variations better and guide effective and personalized intervention strategies.

Design and Study Population

The present study is of an analytical cross-sectional type. A secondary analysis of the database from the Demographic and Family Health Survey (ENDES), provided by the National Institute of Statistics and Informatics (INEI) for the years 2014 to 2022, was conducted ⁽¹¹⁾.

This survey gathers sociodemographic and health data, including anthropometric measures such as BMI, WC, and WHtR. The STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology) were followed in conducting this study ⁽¹²⁾.

Population, Sample, and Selection Criteria

ENDES is a national survey conducted on Peruvians aged 15 to 49, carried out in urban and rural areas of 24 departments of the country – Peru. It features probabilistic, two-stage, stratified, and independent sampling for rural and urban areas.

The selection criteria included adults over 20 years of age and anyone who did not have data to evaluate anthropometric parameters. To ensure only plausible BMI measurements are included in our analyses, we adopted a cutoff based on previous studies⁽¹³⁾: BMI ≥ 10 kg/m² and < 60 kg/m². For WC, we set boundaries at a minimum of 50 cm and a maximum of 150 cm.

Definition of Variables

Obesity was defined according to one or more of the following parameters: 1) If BMI is equal to or greater than 30 Kg/m^{2 (14)}; 2) WHtR ≥ 0.59; 3) WC ≥ 102 cm in men and abdominal waist in women ≥ 88 cm, according to the Cholesterol Education Program Adult Treatment Panel III (WC-ATPIII) criteria ⁽¹⁵⁾.

Additionally, analyses were conducted to denote the prevalence of obesity according to the International Diabetes Federation (IDF) criteria for waist circumference (WC ≥ 94 cm in men and WC ≥ 80 cm in women) ⁽¹⁶⁾ and for obesity as determined by the waist-to-height ratio (WHtR) with a cut-off point of ≥ 0.5 ⁽⁹⁾.

The WHO classification was used to establish the cut-off points that indicate nutritional status according to BMI. The cut-off points for adults were: Underweight (BMI < 18.5 kg/m2), normal weight (BMI between 18.5 and 24.9 kg/m2), overweight (BMI between 25 and 29.9 kg/m2), type I obesity (BMI between 30 and 34.9 kg/m2), type II obesity (BMI between 35 and 39.9 kg/m2) and type III obesity (BMI ≥ 40 kg/m2) (13).

The individual's weight (in kilograms) was divided by their height (in meters squared) to calculate BMI. Weight was measured to an accuracy of 0.1 kg using an electronic scale, with the individual wearing light clothing and without shoes. Height was measured to the nearest 0.1 cm using a wooden stadiometer, following international standards and Peruvian guidelines. WC was measured with tape, recording the abdominal diameter between the last rib's edge and the iliac crest. On the other hand, WHtR was calculated as the ratio between WC and previously measured height ⁽¹⁷⁾.

The independent variables used were sex (female and male), age group (20–35, 36–59, 60–69, and 70 years to more), marital status (with a partner and without a partner), region (Metropolitan Lima, rest of the coast, highlands, and jungle), education (no level/primary and secondary/higher), wealth index (poorest, poor, middle, rich, and richest), area of residence (urban vs. rural), physical disability (no vs. yes), health insurance (no vs yes), consumption of fruits and vegetables greater than five portions per day (yes and no), presence of Arterial Hypertension (HTN) (yes and no), presence of type 2 diabetes mellitus (T2DM) (yes and no), Altitude (0 to 499, 500 to 1499, 1500 to 2999, 3000 or more), Smoker Status and Alcohol consumption ⁽¹⁸⁾.

HTN was defined as those who had SBP ≥ 140 mmHg or DBP ≥ 90 mmHg or who had a prior self-reported diagnosis of hypertension ⁽¹⁹⁾. In the case of T2DM, this was solely by self-report.

Smoker Status was obtained from the self-report of the interviewee, classified as never smoked, ex-smoker, or currently smoker ⁽²⁰⁾. Alcohol consumption was also defined by self-report and was classified as Never or did not use in the last 12 months; consumption was not excessive (≥ one occasion in the last 30 days, but < 5 drinks in the previous 12 months). Men or < 4 drinks in women) and excessive drinking (≥ 1 occasion in the last 30 days and ≥ five drinks in men or ≥ four drinks in women) ⁽²¹⁾.

Statistical Analysis

Data analysis was carried out using R software version 4.0.5. Descriptive variables were presented in terms of absolute and relative frequencies. Associated factors were evaluated in a bivariate analysis, and crude prevalence ratios (RPc) and adjusted prevalence ratios (RPa) were calculated with their respective 95% confidence intervals (CI 95%). For this, generalized linear models with robust variance estimation were used, and a Poisson distribution with logarithmic link functions was assumed. Additionally, concordance analyses were performed to assess consistency between different sets of diagnostic criteria for obesity.

Likewise, a concordance analysis was executed to evaluate consistency between different obesity diagnostic criteria, using the Kappa coefficient. This statistical measure quantifies the degree of agreement between two observers or measurement methods beyond the agreement expected by chance. A Kappa coefficient of 1 indicates perfect concordance and one that any observed concordance is purely coincidental. In the results of this analysis, there is additional insight into the consistency between the three already mentioned diagnostic criteria. A Venn diagram was also created to visualize the overlap and differences between obesity cases identified by each diagnostic criteria.

All analyses were elaborated according to the sample design's complexity; stratification, clustering, and sample weights were considered in all statistical calculations.

Ethical Aspects

This study used a secondary, freely accessible, and cost-free dataset that does not contain personal identifiers (http://iinei.inei.gob.pe/microdatos/). Since the participants' identity is unknown, their privacy and anonymity are guaranteed. The ethical principles established in the Declaration of Helsinki were used to conduct the research.

Characteristics of the study population

A total of 275,580 participants were included. The prevalence of obesity according to BMI, WC-ATPIII, and WHtR was 25.65%, 42.04%, and 46.49%, respectively. The distribution by sex was nearly balanced, with 51.13% women. Participants over 60 years old constituted 16.73%. Wealth distribution was pretty flat, with percentages close to 20% in each category. The majority of participants resided in urban areas (80.75%). Only 9.82% reported current or daily smoking, and excessive alcohol consumption was 3.08% —Table 1.

Table 1

Descriptive characteristics of factors associated with obesity.
Characteristic	n = 161,477^¥
Sex
Female	82,565 (51.13%)
Male	78,912 (48.87%)
Group age
20–35 years	68,810 (42.78%)
36–59 years	65,122 (40.49%)
60–69 years	14,690 (9.13%)
70 years to more	12,226 (7.60%)
Natural region
Metropolitan Lima	59,726 (36.99%)
Resy of coast	41,784 (25.88%)
Montain Range	39,713 (24.59%)
Jungle	20,254 (12.54%)
Educational Level
No Level	314 (0.22%)
Primary	28,256 (19.55%)
Secondary	68,016 (47.05%)
Superior	47,979 (33.19%)
Civil status
Single	48,770 (39.88%)
With a partner	73,521 (60.12%)
Wealth index
The poorest	30,063 (18.62%)
Poor	33,297 (20.62%)
Medium	33,840 (20.96%)
Rich	32,466 (20.11%)
Richest	31,811 (19.70%)
Area of residence
Urban	130,388 (80.75%)
Rural	31,089 (19.25%)
Smoker Status
Has never smoked	133,776 (82.85%)
Former smoker	11,843 (7.33%)
Currently or Daily smoker	15,858 (9.82%)
Alcohol consumption
Never or did not use in the last 12 months	39,798 (24.65%)
Non-excessive alcohol consumption	116,706 (72.27%)
Excessive alcohol consumption	4,973 (3.08%)
Physical disability
No	157,948 (97.81%)
Yes	3,529 (2.19%)
Arterial Hypertension
No	127,978 (79.26%)
Yes	33,498 (20.74%)
T2DM
No	154,494 (95.72%)
Yes	6,916 (4.28%)
Fruit and vegetable consumption ≥ 5 servings per day
No	146,739 (90.87%)
Yes	14,738 (9.13%)
Altitude
0 to 499	106,169 (65.75%)
500 to 1499	12,916 (8.00%)
1500 to 2999	18,605 (11.52%)
3000 or more	23,786 (14.73%)
Obesity by BMI
Normal	120,056 (74.35%)
Obesity	41,421 (25.65%)
Obesity by WC
Normal	93,587 (57.96%)
Obesity	67,890 (42.04%)
Obesity by WHtR
Normal	86,409 (53.51%)
Obesity	75,067 (46.49%)
n (%)
^¥ The values of some variables may not add up to the total n due to missing values

Additionally, the prevalence of obesity, as determined by WC using the IDF criteria, was 64.66%, whereas the prevalence of obesity using the WHtR with a cut-off point of ≥ 0.5 was 84.78%.

Obesity prevalence trend

In Fig. 1, a general upward trend in obesity prevalence is observed from 2014 to 2022 in the three association measures: BMI, WC-ATPIII, and WHtR. At the same time, BMI and WHR showed a constant increase as the years progressed. Additionally, this study offers a comprehensive visual illustration of these patterns, catering to those who seek a more rigorous examination. The persistent discrepancy in obesity prevalence between women and men across all metrics is more pronounced in women, particularly when evaluating obesity based on WC-ATPIII and WHtR criteria.

Nutricional status trend

The nutritional status trends within the general population, including men and women, from 2014 to 2022 exhibit several intriguing dynamics. Generally, the prevalence of underweight has remained stable over time, with a slight indication of a downward trend in recent years. As for average weight, there is a continuous albeit not significant decrease. Conversely, the incidence of overweight and all three categories of obesity are on the rise. When stratified by sex, women exhibit a more significant increase in the prevalence of overweight and obesity compared to men—figure 2.

Factors associated with the three definitions of obesity

When working with obesity as defined by BMI, it was found that men exhibited a lower prevalence of obesity compared to women. The age groups 36–59 years and 60–69 years showed a higher prevalence than the 15–35-year age group, but people 70 years and older have a slight protective role. The Rest of the coast and Jungle regions displayed a high and lower prevalence, respectively, compared to Metropolitan Lima. Individuals with a partner showed a higher prevalence compared to singles. The wealth groups Poor, Middle, Rich, and Richest were more prevalent than the Poorest group. The rural area was associated with a lower prevalence. Physical disability and residing at an altitude of 3000 or more were associated with a lower prevalence of obesity by BMI, while Current or Daily smokers HTN and T2DM were the opposite —Table 2.

Table 2

Bivariate and multivariate regression analysis of factors associated with obesity according to BMI, WC-ATPIII and WHtR
	Obesity by BMI				Obesity by WC-ATPIII				Obesity by WHtR
Characteristic	Normal, n = 120,056	Obesity, n = 41,421	aPR	95% CI	Normal, n = 93,587	Obesity, n = 67,890	aPR	95% CI	Normal, n = 86,409	Obesity, n = 75,067	aPR	95% CI
Sex
Female	58,101 (70.37%)	24,464 (29.63%)	Ref.		31,405 (38.04%)	51,160 (61.96%)	Ref.		35,555 (43.06%)	47,010 (56.94%)	Ref.
Male	61,955 (78.51%)	16,957 (21.49%)	0.68	0.66, 0.69	62,182 (78.80%)	16,730 (21.20%)	0.35	0.34, 0.36	50,854 (64.44%)	28,058 (35.56%)	0.64	0.63, 0.65
Group age
20–35 years	56,688 (82.38%)	12,122 (17.62%)	Ref.		49,725 (72.26%)	19,085 (27.74%)	Ref.		50,193 (72.95%)	18,616 (27.05%)	Ref.
36–59 years	43,136 (66.24%)	21,986 (33.76%)	1.74	1.69, 1.79	30,926 (47.49%)	34,196 (52.51%)	1.95	1.90, 2.00	26,970 (41.41%)	38,152 (58.59%)	2.29	2.23, 2.34
60–69 years	9,992 (68.02%)	4,698 (31.98%)	1.43	1.37, 1.49	6,386 (43.48%)	8,303 (56.52%)	1.89	1.82, 1.95	4,828 (32.87%)	9,861 (67.13%)	2.36	2.29, 2.44
70 years to more	9,633 (78.79%)	2,592 (21.21%)	0.9	0.85, 0.95	6,346 (51.91%)	5,879 (48.09%)	1.56	1.50, 1.63	4,198 (34.34%)	8,028 (65.66%)	2.23	2.15, 2.31
Natural region
Metropolitan Lima	42,036 (70.38%)	17,690 (29.62%)	Ref.		31,923 (53.45%)	27,803 (46.55%)	Ref.		30,184 (50.54%)	29,542 (49.46%)	Ref.
Rest of coast	29,226 (69.95%)	12,558 (30.05%)	1.09	1.06, 1.12	22,654 (54.22%)	19,130 (45.78%)	1.03	1.01, 1.05	20,341 (48.68%)	21,443 (51.32%)	1.04	1.02, 1.06
Montain Range	32,679 (82.29%)	7,034 (17.71%)	0.91	0.81, 1.01	25,804 (64.98%)	13,909 (35.02%)	0.96	0.89, 1.04	23,773 (59.86%)	15,940 (40.14%)	0.96	0.89, 1.03
Jungle	16,115 (79.57%)	4,139 (20.43%)	0.91	0.87, 0.96	13,206 (65.20%)	7,048 (34.80%)	0.91	0.88, 0.95	12,112 (59.80%)	8,142 (40.20%)	0.91	0.88, 0.94
Educational Level
No Level	245 (77.92%)	69 (22.08%)	Ref.		171 (54.60%)	142 (45.40%)	Ref.		147 (46.83%)	167 (53.17%)	Ref.
Primary	20,929 (74.07%)	7,327 (25.93%)	1.1	0.87, 1.42	14,938 (52.87%)	13,318 (47.13%)	1.1	0.93, 1.31	11,946 (42.28%)	16,309 (57.72%)	1.15	0.99, 1.36
Secondary	51,029 (75.02%)	16,987 (24.98%)	1.02	0.81, 1.33	42,869 (63.03%)	25,148 (36.97%)	1.05	0.89, 1.25	39,160 (57.57%)	28,856 (42.43%)	1.07	0.92, 1.26
Superior	34,824 (72.58%)	13,155 (27.42%)	1.02	0.80, 1.32	27,779 (57.90%)	20,200 (42.10%)	1.09	0.92, 1.30	26,876 (56.02%)	21,103 (43.98%)	1.03	0.88, 1.22
Civil status
Single	36,595 (75.04%)	12,174 (24.96%)	Ref.		28,762 (58.98%)	20,008 (41.02%)	Ref.		26,923 (55.20%)	21,847 (44.80%)	Ref.
With a partner	54,537 (74.18%)	18,984 (25.82%)	1.1	1.08, 1.13	42,210 (57.41%)	31,311 (42.59%)	1.12	1.10, 1.14	38,067 (51.78%)	35,454 (48.22%)	1.12	1.10, 1.14
Wealth index
The poorest	25,942 (86.29%)	4,121 (13.71%)	Ref.		21,483 (71.46%)	8,580 (28.54%)	Ref.		19,383 (64.48%)	10,679 (35.52%)	Ref.
Poor	25,244 (75.81%)	8,054 (24.19%)	1.54	1.46, 1.64	20,104 (60.38%)	13,193 (39.62%)	1.34	1.28, 1.40	18,181 (54.60%)	15,116 (45.40%)	1.26	1.21, 1.31
Medium	23,902 (70.63%)	9,937 (29.37%)	1.75	1.64, 1.86	18,490 (54.64%)	15,349 (45.36%)	1.42	1.35, 1.48	16,886 (49.90%)	16,954 (50.10%)	1.3	1.25, 1.36
Rich	22,598 (69.61%)	9,867 (30.39%)	1.77	1.66, 1.89	17,225 (53.06%)	15,241 (46.94%)	1.43	1.36, 1.50	16,004 (49.30%)	16,461 (50.70%)	1.3	1.24, 1.35
Richest	22,369 (70.32%)	9,442 (29.68%)	1.69	1.58, 1.81	16,285 (51.19%)	15,527 (48.81%)	1.4	1.33, 1.47	15,954 (50.15%)	15,857 (49.85%)	1.2	1.15, 1.26
Area of residence
Urban	93,640 (71.82%)	36,747 (28.18%)	Ref.		71,857 (55.11%)	58,531 (44.89%)	Ref.		66,768 (51.21%)	63,619 (48.79%)	Ref.
Rural	26,415 (84.97%)	4,674 (15.03%)	0.88	0.84, 0.93	21,730 (69.90%)	9,359 (30.10%)	0.91	0.88, 0.95	19,641 (63.18%)	11,448 (36.82%)	0.9	0.87, 0.94
Smoker Status
Has never smoked	99,538 (74.41%)	34,238 (25.59%)	Ref.		74,040 (55.35%)	59,736 (44.65%)	Ref.		68,937 (51.53%)	64,839 (48.47%)	Ref.
Former smoker	9,062 (76.52%)	2,781 (23.48%)	1.15	1.09, 1.20	8,673 (73.23%)	3,170 (26.77%)	1	0.96, 1.05	7,717 (65.16%)	4,126 (34.84%)	1.02	0.98, 1.06
Currently or Daily smoker	11,456 (72.24%)	4,402 (27.76%)	1.24	1.20, 1.29	10,874 (68.57%)	4,984 (31.43%)	1.11	1.07, 1.15	9,756 (61.52%)	6,102 (38.48%)	1.03	1.00, 1.06
Alcohol consumption
Never or did not use in the last 12 months	29,561 (74.28%)	10,237 (25.72%)	Ref.		20,952 (52.65%)	18,846 (47.35%)	Ref.		18,457 (46.38%)	21,341 (53.62%)	Ref.
Non-excessive alcohol consumption	86,755 (74.34%)	29,951 (25.66%)	1.03	1.00, 1.06	69,487 (59.54%)	47,218 (40.46%)	1.02	1.00, 1.04	64,922 (55.63%)	51,784 (44.37%)	1	0.98, 1.02
Excessive alcohol consumption	3,740 (75.21%)	1,233 (24.79%)	0.93	0.86, 0.99	3,147 (63.28%)	1,826 (36.72%)	0.96	0.90, 1.02	3,030 (60.94%)	1,943 (39.06%)	0.88	0.76, 1.00
Physical disability
No	117,366 (74.31%)	40,582 (25.69%)	Ref.		91,604 (58.00%)	66,344 (42.00%)	Ref.		84,848 (53.72%)	73,100 (46.28%)	Ref.
Yes	2,690 (76.22%)	839 (23.78%)	0.9	0.82, 0.97	1,983 (56.19%)	1,546 (43.81%)	0.94	0.88, 0.99	1,561 (44.24%)	1,968 (55.76%)	0.99	0.94, 1.05
Arterial Hypertension
No	99,999 (78.14%)	27,979 (21.86%)	Ref.		79,622 (62.21%)	48,357 (37.79%)	Ref.		75,914 (59.32%)	52,064 (40.68%)	Ref.
Yes	20,056 (59.87%)	13,442 (40.13%)	1.72	1.68, 1.77	13,965 (41.69%)	19,533 (58.31%)	1.4	1.37, 1.43	10,495 (31.33%)	23,003 (68.67%)	1.34	1.32, 1.37
T2DM
No	115,800 (74.95%)	38,693 (25.05%)	Ref.		91,108 (58.97%)	63,385 (41.03%)	Ref.		84,459 (54.67%)	70,035 (45.33%)	Ref.
Yes	4,210 (60.86%)	2,707 (39.14%)	1.1	1.05, 1.15	2,446 (35.36%)	4,471 (64.64%)	1.09	1.05, 1.13	1,923 (27.80%)	4,994 (72.20%)	1.07	1.04, 1.11
Fruit and vegetable consumption ≥ 5 servings per day
No	109,090 (74.34%)	37,649 (25.66%)	Ref.		84,387 (57.51%)	62,352 (42.49%)	Ref.		77,656 (52.92%)	69,083 (47.08%)	Ref.
Yes	10,965 (74.40%)	3,773 (25.60%)	1.01	0.97, 1.05	9,200 (62.42%)	5,538 (37.58%)	0.96	0.93, 0.99	8,753 (59.39%)	5,985 (40.61%)	0.93	0.90, 0.96
Altitude
0 to 499	75,307 (70.93%)	30,863 (29.07%)	Ref.		58,220 (54.84%)	47,950 (45.16%)	Ref.		53,918 (50.79%)	52,251 (49.21%)	Ref.
500 to 1499	9,920 (76.80%)	2,996 (23.20%)	0.99	0.94, 1.04	7,812 (60.48%)	5,104 (39.52%)	1	0.97, 1.04	7,105 (55.00%)	5,812 (45.00%)	1.03	0.99, 1.07
1500 to 2999	14,844 (79.79%)	3,760 (20.21%)	0.92	0.83, 1.02	11,555 (62.11%)	7,050 (37.89%)	0.98	0.91, 1.06	10,691 (57.47%)	7,913 (42.53%)	0.97	0.91, 1.04
3000 or more	19,984 (84.02%)	3,802 (15.98%)	0.82	0.73, 0.91	16,000 (67.26%)	7,787 (32.74%)	0.89	0.81, 0.97	14,695 (61.78%)	9,091 (38.22%)	0.91	0.84, 0.98

Each factor has been independently adjusted for Sex, Group Age, Natural Region, Area Of Residence, Educational Level, Wealth Index, Civil Status, Smoker Status, Alcohol Consumption, Arterial Hypertension, T2DM, Physical Disability, Fruits and Vegetable Consumption, and Altitude

aPR: Prevalence ratio. CI 95%: Confidence interval at 95%

Source: self-made

In the case of obesity measured by WC-ATPIII, men exhibited a lower prevalence of obesity than women. The age groups 36–59, 60–69, and 70 years or more showed a higher prevalence than the 15–35 years. The Mountain and Jungle regions displayed a lower prevalence compared to Metropolitan Lima. Individuals with a partner showed a higher prevalence compared to singles. The wealth groups Poor, Middle, Rich, and Richest were more prevalent than the Poorest group. The rural area was associated with a lower prevalence. Fruit and vegetable consumption, Physical disability, and residing at an altitude of 3000 or more were associated with a lower prevalence of obesity by WC-ATPIII. In contrast, Current or Daily smokers, HTN, and T2DM were the opposite —Table 2.

For obesity measured by WHtR, men exhibited a lower prevalence of obesity compared to women. All age groups showed higher prevalence than those aged 20–35. Unlike those who resided in the jungle, the rest of the coast presented a more significant association. Individuals with a partner showed a higher prevalence compared to singles. The wealth groups Poor, Middle, Rich, and Richest were more prevalent than the Poorest group. The rural area was associated with a lower prevalence. Fruit and vegetable consumption and residing at an altitude of 3000 or more were associated with a lower prevalence of obesity by WHtR, while HTN and T2DM were the opposite —Table 2.

Agreement of the three definitions of obesity

The concordance between the three criteria was evaluated through the Kappa index. The concordance between obesity by BMI and WC-ATPIII was 0.5141 (p < 0.001). The concordance between BMI and WHtR was 0.5099 (p < 0.001). Finally, the concordance between WC-ATPIII and WHtR was 0.7514 (p < 0.001)—Table 3.

Table 3

Concordance of obesity diagnoses considering BMI, WC-ATPIII, and WHtR
Test	Kappa	Agreement	Expected agreement	p-value
BMI & WC-ATPIII	0.5141	77.95%	54.62%	< 0.001
BMI & WHtR	0.5099	76.84%	52.74%	< 0.001
WC-ATPIII & WHtR	0.7514	87.80%	50.92%	< 0.001
BMI: body mass index; WC-ATPIII: WC altererd by ATPIII; WHtR: Waist to height ratio

In the Venn diagram of Fig. 3, the values for obesity according to BMI, WC-ATPIII, and WHtR represented 1.5%, 8%, and 12.7% of the cases in isolation, respectively. The intersection of the three criteria accounted for 42.4% of the cases.

Choice of obesity cut-off points

To determine the BMI cut-off point, the classic threshold of ≥ 30 Kg/m^2 established by the WHO was utilized ⁽²²⁾. Although it has been critiqued for measuring overall body fat rather than specifically intra-abdominal fat, this threshold is globally standardized and supported by extensive scientific evidence as a reliable indicator of this pathology, justifying its selection for the current study. This is particularly pertinent given that the globally chosen cut-off points for WC and WHtR are not the most suitable despite their utility.

In defining obesity based on WC, various specific criteria have been established, with cut-off points linked to cardiometabolic risk. The International Diabetes Federation (IDF) ⁽²³⁾ suggests a cut-off for the European population, while the American Heart Association (AHA) ⁽¹⁵⁾ recommends a different criterion for the American population. The Harmonizing Metabolic Syndrome Joint Statement ⁽²⁴⁾ challenges the universal applicability of these criteria, advocating for an adaptation according to ethnic group, regardless of country of residence. Initially, for the Peruvian population, lower WC cut-off points, akin to those used for Asian populations (≥ 80 cm for women and ≥ 90 cm for men) ⁽²³⁾, were recommended. However, these criteria led to an exceptionally high prevalence of obesity or cardiometabolic risk, exceeding 50%. Similarly, the standard cut-off of 0.5 for WHtR ⁽⁹⁾ would indicate a prevalence of up to 80% ^(25,26). Additionally, justifying the chosen threshold, the 2018 study by Ruderman ⁽²⁷⁾, which examined the ancestral genomics of Latin American mestizos, showed that Peru has a high percentage of Native American traditional genetics, influencing the determination of higher cut-off points like those of the ATPIII.

These discrepancies underscore the necessity of further research to define more appropriate and precise cut-off points, likely higher than those used in this study, for a more accurate evaluation of obesity or cardiometabolic risk in the Peruvian population.

Agreement of the three definitions of obesity

The concordance between BMI and WC-ATPIII and BMI and WHtR is moderate, while the concordance between WC-ATPIII and WHtR is substantial. This suggests that WC-ATPIII and WHtR may be more aligned in their classification of obesity compared to BMI with the other two methods. The actual agreements in all comparisons were higher than expected by chance, indicating that these methods have some level of consistency in classifying obesity, but the degree of consistency varies. The difference in concordance levels between pairs of methods suggests that although all are designed to measure obesity, they may capture different aspects or dimensions. This could be due to each method's different definitions and criteria.

A study in children and adolescents showed that WHtR is similar to BMI and WC in identifying those with higher cardiometabolic risk. This study suggests WHtR may be a valuable indicator for assessing cardiometabolic risk in young populations ⁽²⁸⁾. In a survey conducted in 2021, moderate agreement was found between BMI and WC, fair to reasonable agreement between WC and WHR, and poor agreement between BMI, WC, WHR, and neck circumference. This study highlights the variability in concordance between different obesity diagnostic criteria and suggests that multiple measures should be considered when assessing obesity ⁽²⁹⁾.

A study in Korea showed the consistency of cut-off values for BMI, WC, and visceral fat area in predicting obesity-related comorbidities; like the previous work, the authors support the importance of using different obesity diagnostic criteria to assess the risk of comorbidities ⁽³⁰⁾. While one study compared the three classic markers in identifying abdominal obesity, it was found that BMI, WC, and other measures were not materially different in determining cardiovascular disease risk factors ⁽³¹⁾.

In Peru, the work of Aparco JP and Cardenas-Quintana H ⁽²⁵⁾ found a low correlation and concordance between the three obesity measures, so they suggested that they are not interchangeable. Although the present work found similar measures, these were slightly higher than those of this study; the differences may be due to the different amounts of sample evaluated and the other cut-off points, as they used the classic cuts.

Differences in obesity prevalence according to sex

There are variations in obesity problems according to gender. Generally, the proportion of people considered obese tends to be more prominent among adult women compared to adult men.

A study done in the United States in the year 2016 revealed this difference, showing weight issues impacted 35% of men and 40% of women. Increasing patterns were noteworthy among women but not among men, reflecting the complexity of the interaction involving sex and obesity ⁽³²⁾. Research on weight issues across geographical regions found that in areas such as India, the overall frequency of being overweight or obese was determined to be 25%, with abdominal obesity levels calculated at 21%, with both measures affecting women more prevalently than men. This strengthens the idea that sex may interact with other elements to influence the prevalence of obesity ⁽³³⁾.

In addition, discrepancies relating to sex also seem to take part in these divergences. An examination from 2014 investigating relationships between signs of gender imbalance and how widespread obesity had noticed a higher occurrence in females than males. However, the extent of this divergence significantly fluctuated between nations, proposing that societal and financial aspects may affect these divergences ⁽³⁴⁾.

However, a synthesis of snapshot reports in Brazil found a widespread rate of excessive weight among kids and teenagers, yet without notable gaps between sexes. This potential outcome mirrors variances in technique or societal and demographic traits across the populations analyzed ⁽³⁵⁾.

The bond between gender and obesity is rooted in how men and women change food into energy differently. These variations appear in how each sex stores and utilizes fat and how they respond to insulin. Typically, while fat is kept by females just under the skin more so than males, who tend to store fat in the abdominal region instead, this has been associated with a higher likelihood of metabolic ailments developing. Also, sex hormones like estrogen in women influence where fat goes and how sensitive the body is to insulin, which may contribute to the imbalance in how widespread obesity is between males and females. Understanding these metabolic variations is essential for developing treatments for obesity that are aimed at people specifically and tailored to the person, accounting for the gaps between genders ⁽³⁶⁾.

Nutricional status trends

The observed increases in overweight and obesity levels, as reported in the results section, indicate that public health in the country still faces challenges in combating this condition. The sustained rise in obesity prevalence, especially type III obesity, underscores the cumulative impact of environmental and lifestyle factors on metabolic health over time. Consistent with global reports, this rising pattern corresponds to amplified obesity worldwide and its accompanying comorbid conditions, including type 2 diabetes and cardiovascular ailments ^(37–39).

While gender analysis illuminates notable divergences in weight patterns over time, it shows that females have undergone a more sharply amplified increase in the prevalence of being overweight or obese relative to males. There are likely biological and socioeconomic factors contributing to this discrepancy between men and women that disproportionately impact women's access to healthy foods, exercise options, and financial resources, which can influence health outcomes. Moreover, this pattern underscores the importance of addressing obesity from a gender perspective, ensuring interventions are culturally sensitive and sex-specific ^(40,41).

The steady rise in the percentages of those categorized as overweight or obese, coinciding with a declining percentage of the population maintaining a healthy weight, underscores the importance of implementing vigorous public initiatives to encourage nutritious dietary habits and consistent physical activity. Strategies should create environments conducive to healthy food choices and physical activity. Evidence suggests that effective interventions integrate fiscal policies, such as taxes on unhealthy foods, with health education and improvements in the food environment. These endeavors should be supported by ongoing research that monitors trends and assesses the effectiveness of implemented interventions ^(42,43).

Factors associated with obesity according to each criterion

This work also found that older years tended to exhibit heavier body weights more often. These conclusions matched multiple reports. Research done in Kerman, Iran, saw that the rates of being overweight or too heavy grew as age increased. The study unveiled that the altered likelihoods for being deemed too heavy fluctuated in an age-related design, highest amongst those 55–65 at 11.7 while 45–54 year-olds followed closely at 10.1, and 65–75 year-olds coming in at 7.9, each compared divergently to the baseline group of youngest adults from 15–24 years ⁽⁴⁴⁾. The look into differences relating to sex in child weight issues pointed out earlier also told us that rates of obesity are higher among boys than girls 5–19 nearly everywhere worldwide with high or moderately high earnings. This proposes that age plays a part in how prevalent obesity is among children and teenagers, enforcing the connection between age and weight issues ⁽⁴⁵⁾.

The connection between the status of the relationship, specifically living with a partner, and possessing obesity has been analyzed in numerous investigations globally. Data from the United States National Health and Diet Study (2007–2014) indicated that married gentlemen were more likely to hold more weight than singles and uncovered a meaningful interaction between ancestry, income, and relationship status ⁽⁴⁶⁾. Analogously, a study of Chinese identical twins found that the relationship status was connected to Body Mass Index independently of familial factors and that united individuals had a higher risk of being obese ⁽⁴⁷⁾.

In Iran, scientists found that higher education levels were connected to an increased chance of weight-related body fat but with regular weight in men and that the frequency of unions was more significant in men with less schooling ⁽⁴⁸⁾. This study from Pakistan revealed an inverse link between physical extra and student satisfaction in life. It hinted that relational position might intervene in the tie between plumpness and a sense of well-being, as referenced in the source ⁽⁴⁹⁾. These discoveries propose that having a companion may be related to a greater risk of weight-related body fat, even though the underlying mechanisms and interactions with other elements, such as origin and education, may be complex and require further exploration.

The bond involving where people live, depending on how far away they are from towns and cities (urban vs rural areas), and excess weight has been examined in several reports under different situations. Researchers in West Bengal looked at food intake, physical activities, and body measurements between overweight rural and city-dwelling male adults, finding significant gaps in calories eaten and eating patterns, which were worse for those in urban places ⁽⁵⁰⁾. Experts found inequality in rates of abdominal fat build-up existed between urban and rural areas within Malawi, with frequency consistently higher among people residing in city environments over separate survey times, as documented in source thirty-seven ⁽⁵¹⁾. A study in Nigeria investigated the co-occurrence of maternal overweight and obesity with child malnutrition in rural and urban groups, showing a more significant part of overweight and obesity among mothers in city areas ⁽⁵²⁾. These gaps may originate from diverse eating routines, distinct levels of movement, and differences in socioeconomic backgrounds among various demographics.

The bond between socioeconomic status and obesity in this examination uncovers an intricate layout in which all wage tiers display a similar peril of putting on pounds. This can be illuminated by the reality that, no matter the financial place, there are dietary things that contribute to putting on weight. In reduced socioeconomic levels, restricted access to fresh and nutritious eats may lead to greater dependence on refined and prepared foods filled with sodium, refined carbohydrates, and soaked fats, as found in a survey on food areas in disadvantaged neighborhoods ⁽⁵³⁾. The Healthy Living Project in Atlanta, Georgia, highlighted how a lack of food in homes can lead to food areas in city parts with confined access to fresh and reasonably priced food ⁽⁵⁴⁾. On the other hand, even though they have access to a broader assortment of foods, higher socioeconomic levels may also opt for unhealthy selections that appeal to taste and convenience ^(55,56). Thus, both prepared foods consumed in low-income groups and unhealthy choices in high-income groups may similarly add to the risk of being overweight, mirroring the intricacy of this connection.

Limitations of the Study

This study faces several limitations that must be considered when interpreting the results. The nature of the study's design might limit the ability to establish causal relationships, and the sample selection might affect the generalization of the findings. Differences in definitions and cut-off points for obesity and the possible lack of control for all confounding variables might have introduced variability and biases. These limitations underscore the need to interpret the results cautiously and suggest areas for improvement in future research.

This study has provided a comprehensive view of the prevalence and factors associated with obesity in the studied population, using different diagnostic methods and cut-off points. The findings reveal a moderate to substantial concordance between the three other criteria, suggesting alignment in the classification of obesity among these methods. Significant associations were identified between obesity and variables such as sex, age, region, marital status, and wealth groups.

The results underscore the complexity of obesity and the need for multidimensional approaches to prevention and treatment. They also highlight the importance of considering different diagnostic methods and researching new cut-off points to define obesity. The findings of this study have important implications for public health, policy, and clinical practice and offer a solid foundation for future research and intervention strategies in the fight against the obesity epidemic.

Ethics approval and consent to participate

(Not applicable)

Consent for publication

(Not applicable)

Availability of data and materials

The data supporting the findings of this study can be accessed by the original research paper at the follow link: https://proyectos.inei.gob.pe/microdatos/

Competing interests ·

The authors declare no conflict of interest

Funding

This study is self-financed.

Authors' contributions

Luisa Erika Milagros Vásquez Romero: Methodology, Validation, Writing – Review & Editing. Víctor Juan Vera-Ponce: Conceptualization, Data Analysis, Writing – Review & Editing. Joan A. Loayza-Castro: Data Analysis, Methodology, Validation, Writing – Original Draft. Fiorella E. Zuzunaga-Montoya: Data Analysis, Methodology, Validation, Writing – Original Draft. Jenny Raquel Torres-Malca: Validation, Visualization, Writing – Review & Editing. Cori Raquel Iturregui Paucar: Supervision, Project Administration, Writing – Review & Editing. Mario J. Valladares-Garrido: Project Administration, Funding Acquisition, Writing – Review & Editing.

María del Socorro Romero-Figueroa: Methodology, Supervision, Writing – Review & Editing. Mariana Silva: Methodology, Supervision, Writing – Review & Editing. Willy Ramos: Supervision, Methodology, Writing – Review & Editing. Jhony A. De La Cruz-Vargas: Supervision, Writing – Review & Editing.

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Associated factors, concordance, and trends of obesity: body mass index, abdominal waist, and waist- to-height ratio between 2019 and 2022. Analysis of nine national surveys

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Version 1

Abstract

Figures

INTRODUCTION

METHODOLOGY

Design and Study Population

Population, Sample, and Selection Criteria

Definition of Variables

Statistical Analysis

Ethical Aspects

RESULTS

Characteristics of the study population

Obesity prevalence trend

Nutricional status trend

Factors associated with the three definitions of obesity

Agreement of the three definitions of obesity

DISCUSSION

Choice of obesity cut-off points

Agreement of the three definitions of obesity

Differences in obesity prevalence according to sex

Nutricional status trends

Factors associated with obesity according to each criterion

Limitations of the Study

CONCLUSIONS

DECLARATIONS

REFERENCES

Additional Declarations

Status:

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