Exploring Waist-to-Hip Ratio and Health Risks: Insights from NFHS-5 on Trends, Patterns, and Predictors among the Indian Population

doi:10.21203/rs.3.rs-5139378/v1

Introduction: With a rising trend in obesity rates, particularly among young adults, understanding factors contributing to abdominal obesity is crucial for public health interventions. The study explores the significance of the Waist-to-Hip Ratio [WHR], focusing on the regional trends of abdominal obesity and associated factors.

Methods: The study made use of information from the National Family Health Survey 5, an extensive household survey which was conducted out in all of India's states and union territories.Socio-demographic variables such as age, gender, residence type, wealth index, and lifestyle factors were considered as independent variables. Descriptive statistics were used in statistical analysis along with chi-square tests, and logistic regression to identify predictors of increased WHR.

Results: Abdominal obesity, as indicated by WHR, was prevalent in 57.6% of the population, with 51.2% of males and 58.4% of females exhibiting high WHR values. Regional trends showed higher prevalence in the northern region of India. Significant correlations between WHR and variables such age, wealth index, type of dwelling, alcohol intake, and comorbid conditions were found by bivariate and regression analysis.Age and BMI were linked to increase WHR, and those with elevated blood pressure had a 1.24 times higher risk of increased WHR in contrast to people who have ideal Blood pressure.

Conclusion: The study emphasizes how critical it is to implement focused interventions in order to reduce India's rate of abdominal obesity. By comprehending the intricate relationship between lifestyle and socio demographic factors that impact WHR, public health initiatives can be customized to reduce the health hazards linked to central adiposity.

abdominal obesity

waist-to-hip-ratio

obesity burden

overweight

non-communicable burden

public health interventions

NFHS-5

fat distribution

India

Non-communicable diseases are now a global epidemic posing a serious threat to the health of the community at large and threatening to overwhelm health systems. More than half of the burden of non-communicable diseases can be attributable to unhealthy diets leading to malnutrition disorders such as overweight and obesity.[1]

The significance of abdominal fat mass, also known as visceral, central, or abdominal obesity, was acknowledged by the WHO Expert Consultation on Obesity in 1997. This type of obesity can vary significantly within a limited range of total body fat and body mass index [BMI].In 2022, approximately 43% of adults aged 18 and older were overweight, marking an increase from 25% in 1990, with similar rates observed among men [43%] and women [44%]. Prevalence of overweight varied by region, from 31% in the WHO Southeast Asia and the Africa Region to 67% in the Region of Americas.[2] This rising prevalence of overweight and obesity in India is increasing faster than the world average. In India, the prevalence of overweight increased from 8.4–15.5% among women between 1998 and 2015, and the prevalence of obesity increased from 2.2–5.1% over the same period. This fast-paced growth has been accompanied by notable increases in the burden of non-communicable diseases [NCDs].[3]

The existing body of research has also demonstrated the necessity for additional markers to be used in addition to BMI measurements in order to identify patients who have an elevated risk of obesity-related morbidity as a result of abdominal fat buildup.[4]The waist-hip ratio, which is calculated by dividing the waist circumference by the hip circumference, has been proposed as an extra indicator of the distribution of body fat.[5] The ratio offers an index of subcutaneous and intra-abdominal adipose tissue and is more accurate to assess than skin folds.[6] Over time, there has been a notable rise in abdominal obesity (AO), and numerous studies have documented the associated health risks, including non-communicable diseases (NCDs). [7]Among women, the WHR showed a significant positive association with myocardial infection, stroke, angina pectoris, and death. [8]

For the very first time, the National Family Health Survey in its fifth round [NFHS-5] included waist and hip circumference measurements for both eligible women and men for abdominal obesity.[9] Many studies across the globe have documented the contributing factors and Health hazards connected to obesity in the abdomen Nevertheless, there is a dearth of data from India indicating state-by-state patterns and the variables linked to elevated abdominal fat.The survey provides a unique opportunity for scientific investigation of health, economic, and social determinants, and the current scenario of abdominal obesity in India. Therefore, the goal of the latest study is to use nationally representative data to determine the prevalence of WHR.The purpose of this research is to estimate the regional trends of WHR among Indian states and union territories. The second objective is to explore the association of possible socio-demographic, behavioral habits, and co-morbidity conditions among the Indian population.

Data Source

Data from the National Family Health Survey [NFHS-5] was retrieved, which is a house assessment that gathers demographic and health surveys [DHS] for all 29 states and 7 union territories of India. It was conducted by the Ministry of Health and Family Welfare [MoHFW], managed by the International Institute for Population Sciences [IIPS], Mumbai, and implemented by a group of survey organizations and Population Research Centres. The sample design for NFHS-5 was uniform design implemented by using a two-stage cluster sampling approach, where primary sampling units [villages in rural areas and census enumeration blocks in urban areas] were chosen with a probability proportionate to the size of the population, and following a mapping and household listing, households were chosen by simple random sampling

Study Variables

· Dependant variables:

The primary outcome of interest was the waist-to-hip ratio. For the first time, NFHS-5 included waist and hip circumference measurements which were recorded by using Gulick tapes for eligible women and men for measurements of abdominal obesity. WHR is categorized as high risk in women at≥0.85 cm and men at ≥0.90 cm.

· Independent variables:

We considered independent or predictor variables as per the following broad domains.

a) Socio-demographic variables:

Caste, age, sex, and marital status, region[urban/rural], religion, type of family, wealth index, education and place of residence, region of country.

b) Anthropometric indicators:

High waist circumference [cut-off values 90 cm for men and 80 cm for women], Hip circumference, high waist-to-hip ratio [cut-off value 0.90 cm for men and 0.85 cm for women], levels of BMI [underweight, normal, overweight calculated by using formula Weight in kg/Height in m²]

c) Co-morbidity and behavioural habits:

We have included smoking/tobacco consumption, and alcohol consumption to understand the behavioural habits among the study population. Additionally, morbidity patterns such as hypertension were also considered. In NFHS-5, three blood pressure [systolic and diastolic pressure] Subjects' observations were collected at least 5 min between each BP measurement with a standardized OMRON BP monitor. We have considered the average of the three measurements of blood pressure to decide whether a participant was hypertensive. If a single measurement was absent for an individual in the dataset, then the average of the remaining two measurements was used. We used the remaining measurements if two measurements were missing. The person was considered hypertensive if the average of all three systolic pressure was between 140-200 mmHg or diastolic pressure was between 90-140 mmHg.

Sample Selection:

Data from 2,022,037 individuals [household member recode file] was available from the DHS datasets. The information regarding particular variables was downloaded and then checked to identify and address errors or inconsistencies, such as missing values.

The missing information was managed by two approaches: If data of a particular subject was absent for multiple variables, it was deleted and not considered in the analysis and If data was lacking for one or two variables, it was imputed using appropriate statistical methods [by substituting the mean value]. The identification of outliers was achieved through visual inspection and was corrected by computing the mean and removing extreme values.

Some variables were transformed to ensure compatibility with statistical analysis. This included the categorization of the age variable into 3 sub-groups, and creating composite indices such as hypertension status by using three measurement values of BP, BMI using weight and height variables and WHR using waist and hip circumference.

A final inspection of the cleaned dataset was conducted to validate and ensure that each problem discovered during the cleaning process was resolved. The cleaned data collection was ready for statistical analysis with a comprehensive data dictionary documenting all variables, coding schemes, and transformations applied.

Data Analysis

Initially, for the visualization of national and state-level data on waist-hip ratio, the maps were drawn to study the patterns of WHR across India's states and union territories using the software Tableau and the prevalence of waist-hip circumference studied trends. The states representing darker shades in the map indicate a higher prevalence of WHR in the region while the lighter shades indicate low prevalence. In addition, the prevalence of WHR was further connected with the predicting factors using individual-level data. All the variables were coded and categorized using the raw data in Excel and the data sheet was exported to statistical software SPSS version 25.0 to run the statistical analysis. The descriptive statistics were used to examine the socio-demographic features of the research's subject using frequency and percentage. Pearson’s chi-square test of significance is used to test the possible relationship between men's and women's independent and dependent variables separately. Logistic regression was utilized to determine the factors substantially connected to higher abdominal obesity. The binary logistic regression test was performed by the ‘enter’ method, considering waist-hip ratio as the dependent variable and entering the following variables as the independent variables in Step 1: age in years, gender, type of residence, wealth index, Household Structure, smoking, alcohol, BMI Status, and BP. Bivariate analysis and logistic regression were reported as a chi-square test and adjusted odds ratio [OR], the statistical analysis was undertaken at a 95% confidence level with significance at a p-value of <0.05.

Ethical Consideration:

Since there was no human engagement with the study and it made use of secondary information from a national survey, an ethical review was not considered essential.

State-wise trends of WHR in the Indian States and UTs:

The mapping showed the regional trends of the prevalence of abdominal obesity across Indian states ranging between 0.10% and 12.60%. The prevalence was discovered to be greater in the northern state of Uttar Pradesh [12.6%], Bihar [6.03%] followed by Assam, [5.47%] a few western states like Rajasthan, [6.13%] Madhya Pradesh, [4.8%] and Orissa, [4.33%] and lower in the states like Andra Pradesh [1.32%], Puducherry [0.53%], and Ladakh [0.47%].

Prevalence of WHR among Study Population:

Men and women differ in fat deposition patterns and fat consumption by the body. Also, the cut-offs for the waist-hip ratio for the men and women are differently classified. So, the risk stratification of the predicting variables among men and women was analysed separately as shown in Table B and Table C respectively. Among the study population, women represented 87.3% of the population, in contrast to men's lower proportion of 12.2%. The prevalence of a high waist-to-hip ratio was greater in females [58.4%] compared to males with an overall prevalence of 57.6% among the total study population.

Table A- Prevalence of increased WHR among the research group

Gender

Prevalence of WHR

N [%]

95%Confidence Interval

[CI]

Male

50254 [51.2%]

[50.22–52.18]

Female

414382 [58.4%]

[58.29–58.51]

Total

464636 [57.6%]

-

Risk Stratification and Association of Predicting Variables:

Most research participants belonged to the younger adult age group having a mean age of 30. 6 years; however, the middle age group [30–45 yrs.] and older adults [> 45 yrs.] possessed a greater chance of developing WHR compared to their counterparts. Literacy had no appreciable effect with the risk of high WHR because all the groups had approximately identical risks. Additionally, there were no greater disparities in risk between urban and rural people, indicating a nearly equal risk of abdominal obesity in both groups among women. This difference was higher in men representing, urban men having a higher risk [55%] of increased WHR in contrast to the rural population [49.9%]. Compared to other individuals, Adults in marriage had a higher probability to have abdominal obesity. The risk of higher WHR in various religious groups was highest among Sikhs [72.3%], followed by Muslims [68.7%], and Christians [61.9%]. Identical trends were also noted in men representing cultural variability and statistically significant differences. Among the different wealth quintiles, the risk of increased WHR is greater in higher quintiles that are richer and richest in contrast to the lower quintiles.

Regional variations are also noted in the percentage of elevated waist-hip ratio, which indicates distinct dietary habits and trends in that area. The risk of greater abdominal obesity was seen highest in the Northern region [64.9%], followed by eastern India [63.9%], southern [51.9%], and least observed in western Indian women [47.2%]. While in men it differed representing the highest in the northern [59.8%], next to the southern [51.6%], eastern [49.3%], and least noticed at western region. [44.5%] Behavioural habits and co-morbidity condition was also found as strong correlations with the increased risk of abdominal obesity. Consumption of alcohol and smoking was observed greater in men as compared to women and increased the chances of higher obesity. With increasing BMI, the chances of a higher waist-to-hip ratio also increased representing 70% and 72.8% in men and women respectively. Additionally, individuals having hypertension also had greater chances of having a higher waist-to-hip ratio representing the possible contribution of abdominal obesity to the chronic conditions.

Table B- Risk Stratification and its association among women [N = 709117]

Variables	Normal WHR [< 0.85 cm]	At Risk WHR [> 0.85 cm]	Total N [%]	Chi-square and p-value
Age
15–29	162738 [46.4%]	188211 [53.6%]	350949[49.5%]	X²⁼7025.19 p = 0.000
30–45	112549 [37.6%]	186813 [62.4%]	299362 [42.2%]
> 45	19448 [33.1%]	39358 [66.9%]	58806 [8.3%]
Level of education
No education	62229 [39.6%]	94743[60.4%]	156972 [22.2%]	514.8 [0.00]
Primary	36377 [40.6%]	53168 [59.4%]	89545 [12.6%]
Secondary	156373 [42.8%]	209053 [57.2%]	365426 [51.6%]
Higher	39756 [40.9%]	57418 [59.1%]	97174 [13.8%]
Place of residence
Urban	67224 [39.1%]	104884 [60.9%]	172108 [24.3%]	586.9[0.00]
Rural	227511 [42.4%]	309498 [57.6%]	537009 [75.7%]	586.9[0.00]
Marital Status
Unmarried	89654 [49.1%]	92777 [50.9%]	182431 [25.7%]	5811.0[0.00]
Married	205081 [38.9%]	321605 [61.1%]	526686 [74.3%]	5811.0[0.00]
Religion
Hindu	236134[44.2%]	298255[55.8%]	534389 [75.4%]	7115.4[0.00]
Muslim	27702[31.3%]	60712 [68.7%]	88414 [12.5%]
Christian	19713[38.1%]	32048 [61.9%]	51761 [7.3%]
Sikh	4249[27.7%]	11115 [72.3%]	15364 [2.2%]
Buddhist	3534[38.8%]	5566 [61.2%]	9100 [1.3%]
Jain	371[41.7%]	519 [58.3%]	890 [0.1%]
Others	3032[33%]	6167 [67%]	9199 [1.3%]
Caste/Tribe
SC	59478[42.2%]	81369 [57.8%]	140847 [19.9%]	3221.7 [0.00]
ST	55799[41%]	80402 [59%]	136201 [19.2%]
OBC	118417[44.9%]	145251 [55.1%]	263668 [37.2%]
Others	61041[36.2%]	107360 [63.8%]	168401 [23.7%]
Wealth index
Poorest	63408 [42.8%]	84677 [57.2%]	148085 [20.9%]	1359.6[0.00]
Poorer	68367 [43.2%]	90035 [56.8%]	158402 [22.3%]
Middle	63751 [42.7%]	85582 [57.3%]	149333 [21.1%]
Richer	55979 [41%]	80473 [59%]	136452 [19.2%]
Richest	43230 [37%]	73615 [63%]	116845 [16.5%]
HH structure
Nuclear	146574 [40.8%]	212858 [59.2%]	359432 [50.7%]	184.6[0.00]
Non-nuclear	148161 [42.4%]	201524 [57.6%]	349685 [49.3%]	184.6[0.00]
Region
East	90198 [36.4%]	157548 [63.6%]	247746 [34.9%]	16173.1 [0.00]
North	67047 [35.1%]	123933 [64.9%]	190980 [26.9%]
South	52973[48.1%]	57199 [51.9%]	110172 [15.5%]
West	84517 [52.8%]	75702 [47.2%]	160219 [22.6%]
Smoking
No	270366 [41.7%]	377987[58.3%]	648353[91.4%]	58.27[0.00]
Yes	24369[40.1%]	36395[59.9%]	60764[8.6%]	58.27[0.00]
Alcohol
No	289239[41.7%]	403969 [58.3%]	693208 [97.8%]	329.9 [0.00]
Yes	5496 [34.5%]	10413 [65.5%]	15909 [2.2%]	329.9 [0.00]
BMI
Less	76692 [60.2%]	50755[39.8%]	1227447[18%]	31414.0[0.00]
More	42388[27.2%]	113406 [72.8%]	155794 [22%]
Normal	175655 [41.2%]	250221[58.8%]	425876[60.1%]
BP
No	268753 [42.7%]	360025 [57.3%]	628778 [88.7%]	3173.3[0.00]
Yes	25982 [32.3%]	54357 [67.7%]	80339 [11.3%]	3173.3[0.00]

Table C - Risk Stratification and its association among men [N = 98212]

Age
Variables	Normal WHR [< 0.90 cm]	At Risk WHR [> 0.90 cm]	Total N [%]	Chi-square value [p-value]
15–29	25738 [59.2%]	17735[40.8%]	43473 [44.3%]	3438.1[0.00]
30–45	16374 [41.8%]	22809 [58.2%]	39183 [39.9%]
> 45	5846 [37.6%]	9710 [62.4%]	15556 [15.8%]
Level of education
No education	5252 [48.7%]	5526 [51.3%]	10778 [11%]	129.7[0.00]
Primary	5539 [47.5%]	6113 [52.5%]	11652 [11.9%]
Secondary	29395 [50.1%]	29252 [49.9%]	58647 [59.7%]
Higher	7772 [45.4%]	9363 [54.6%]	17135 [17.4%]
Place of residence
Urban	11208 [45%]	13673 [55%]	24881 [25.3%]	191[0.00]
Rural	36750 [50.1%]	36581 [49.9%]	73331 [74.7%]	191[0.00]
Marital Status
Unmarried	21426 [60.1%]	14222 [39.9%]	35648 [36.3%]	2846.2 [0.00]
Married	26532 [42.4%]	36032 [57.6%]	62564 [63.7%]	2846.2 [0.00]
Religion
Hindu	36211 [48.7%]	38096 [51.3%]	74307 [75.7%]	603.9[0.00]
Muslim	5156 [44.8%]	6344 [55.2%]	11500 [11.7%]
Christian	4354 [59.8%]	2933 [40.2%]	7287 [7.4%]
Sikh	779 [34.9%]	1454 [65.1%]	2233 [2.3%]
Buddhist	712 [52.7%]	639 [47.3%]	1351 [1.4%]
Jain	63 [45.7%]	75 [54.3%]	138 [0.1%]
Others	683 [48.9%]	713 [51.1%]	1396 [1.4%]
Caste/Tribe
SC	9032 [47.3%]	10053 [52.7%]	19085 [19.4%]	904.5 [0.00]
ST	11185 [57.8%]	8174 [42.2%]	19359 [19.7%]
OBC	17660 [48.3%]	18940 [51.7%]	36600 [37.3%]
Others	10081 [43.5%]	13087 [56.5%]	23168 [23.6%]
Wealth index
Poorest	10789 [55.7%]	8579 [44.3%]	19368 [19.7%]	1055.7 [0.00]
Poorer	11485 [52.2%]	10525 [47.8%]	22010 [22.4%]
Middle	10330 [49.1%]	10709 [50.9%]	21039 [21.4%]
Richer	8733 [45.1%]	10613 [54.9%]	19346 [19.7%]
Richest	6621 [40.3%]	9828 [59.7%]	16449 [16.7%]
HH structure
Nuclear	24983 [49.3%]	25690 [50.7%]	50673 [51.6%]	9.3[0.002]
Non-nuclear	22975 [48.3%]	24564 [51.7%]	47539 [48.4%]	9.3[0.002]
Region
East	17005 [50.7%]	16508 [49.3%]	33513 [34.1%]	1249.3 [0.00]
North	10198 [40.2%]	15194 [59.8%]	25392 [25.9%]
South	7334 [48.4%]	7810 [51.6%]	15144 [15.4%]
West	13421 [55.5%]	10742 [44.5%]	24163 [24.6%]
Smoking
No	29792 [49.9%]	29926 [50.1%]	59718 [60.8%]	68.1[0.00]
Yes	18166 [47.2%]	20328 [52.8%]	38494 [39.2%]	68.1[0.00]
Alcohol
No	37709 [49.8%]	38053 [50.2%]	75762 [77.1%]	117.6[0.00]
Yes	10249[45.7%]	12201 [54.3%]	22450 [22.9%]	117.6[0.00]
BMI
Less	10291 [71.2%]	4170 [28.8%]	14461 [14.7%]	6309.4 [0.00]
More	6540 [29.4%]	15681 [70.6%]	22221 [22.6%]
Normal	31127 [50.6%]	30403 [49.4%]	61530 [62.7%]
BP
No	40982 [51.9%]	38017 [48.1%]	78999 [80.4%]	1499 [0.00]
Yes	6976 [36.3%]	12237 [63.7%]	19213 [19.6%]	1499 [0.00]

Regression Analysis of Predictors of Abdominal Obesity:

A logistic regression analysis investigated the relationship between multiple factors for assessing the impact of possible contributing factors on a higher waist-hip ratio [WHR]. The model was determined to be statistically significant [χ2 = 186.617, p < .0001], suggesting that the variables had a significant effect on WHR. The model explained 7.1% [Nagelkerke R²] of the variance in WHR, with the classification demonstrating a 62.0% of model accuracy. Growing older was linked to a higher likelihood of elevated WHR, with age 29–45 years having an odds ratio of 1.177 and > 45 years having an odds ratio of 1.386. BMI was also a significant predictor, with normal and overweight individuals being at 2.082- and 3.769 times higher risk of developing an above-normal WHR respectively. Gender-specific differences were observed, with males having a lesser chance of high WHR compared to females.

Additionally, variables such as Consumption of Smoking and Alcohol, Wealth Index, Type of Residence, HH structure, and BP were identified as significant predictors. Individuals having hypertension had 1.24 times higher odds of having increased abdominal obesity than others. Additionally, alcohol consumers also had 1.13 times higher chances of having a high waist-to-hip ratio than non-consumers. Individuals belonging to higher quintiles also represented 1.01 times higher chances of increased abdominal obesity.

Table D - Binary Logistic Regression of Predicting Variables

Age
Variables	Odds ratio	95% CI	SE
15–29	Ref.	-	-
30–45	1.177	1.16–1.18	0.005
> 45	1.386	1.36–1.41	0.009
Gender
Female	Ref.	-	-
Male	0.671	0.66–0.68	0.008
Place of residence
Urban	Ref.	-	-
Rural	0.989	0.97–1.00	0.006
Wealth Index
Poorest	Ref.	-	-
Poorer	0.936	0.92–0.95	0.007
Middle	0.903	0.89–0.92	0.007
Richer	0.920	0.91–0.93	0.008
Richest	1.010	0.99–1.02	0.009
HH structure
Nuclear	Ref.	-	-
Non-Nuclear	0.973	0.96–0.98	0.005
Smoking
No	Ref.	-	-
Yes	0.993	0.97–1.01	0.008
Alcohol
No	Ref.	-	-
Yes	1.131	1.10–1.16	0.012
BMI
Less	Ref.	-	-
More	2.082	2.05–2.12	0.006
Normal	3.769	3.71–3.83	0.008
BP
No	Ref.	-	-
Yes	1.24	1.23–1.26	0.009

Waist-hip ratio [WHR] is a critical measure of abdominal obesity and a predictor of various health risks, including cardiovascular diseases and metabolic disorders. The assessment of secondary information was done to explore the determinants of WHR, finding significant associations that shed light on the multifaceted nature of this health indicator. The result from the above analysis shows a heterogeneous distribution of increased waist-to-hip ratio around its predicting variables both in men and women. The prevalence of above-normal WHR is higher among northern Indian states than in other regions and is comparable to primary studies done in that region[3] Further, the prevalence of high waist-to-hip ratio in the present analysis was higher in women [54.8%] than men [51.2%] which aligns with the existing evidence from northern and southern India. However, regression analysis indicated males have 0.6 times higher odds of having increased WHR. [10]An increased occurrence of abdominal obesity might also increase the prevalence of gestational diabetes, type 2 diabetes, [11]hypertension,[12]and cardiovascular disease[13] later in life.

The present study observed higher chances of abdominal obesity in urban areas than in rural areas, resembling many other community-based studies conducted in India.[14] Keeping in mind the lifestyle changes, the higher prevalence comes as no surprise; however, as known from the analysis, nearly 70% of the Indians reside in rural areas and thus, has a major bearing on prevalence of obesity in our country. This rising incidence might be due to swift change towards a sedentary lifestyle, lower metabolic rate, better access to technology, and increased usage of packed and processed foods. This might be presented as a major epidemiological shift from communicable to non-communicable diseases. Thus, it demands for targeted interventions focussing on different aspects like nutrition, behaviour change, health promotion, and other interventions for controlling obesity among adolescents and young adults.

Studies conducted within India have shown a significant association between increased waist-to-hip ratio [WHR] and age, indicating increase in central obesity with advancing age. The study's conclusions are in align with those published by Jena et al. [2023], that as age increases, the prevalence of above-normal WHR also rises. People who fall within the age range of [50–54 years] exhibited a significantly higher percentage of high-risk WHR [> 60%] compared to those in the 15–19 age group [40%] [15]This observed rise is attributed to a decline in basal metabolic rate [BMR], levels of sex hormones with age i.e. Oestrogen in women and testosterone in men, and Lifestyle factors with a decline in physical activity which can influence fat distribution often concentrating it towards the abdominal region leading to an increased WHR.

A report on an empirical investigation of abdominal obesity and income reveals a positive association between wealth index, education quantile, and waist-to-hip ratio [WHR]. While higher education can promote health knowledge, it might not always translate to healthy food choices, factors like busy lifestyles and time constraints could lead to unhealthy dietary choices and decreased physical activity. Similarly higher wealth might have easier access to calorie-dense, processed foods while having a more sedentary lifestyle, as a result, this might lead to central adiposity. Further research investigating food habits, degrees of physical exercise, and self-reported stress across wealth and education quintiles potentially offers insightful information about the observed association. [16]

Several earlier cross-sectional studies that looked at the relation between alcohol intake and body fat distribution support our findings. These studies consistently show a positive link, suggesting that above-normal waist-to-hip ratio is more common in those who drink more alcohol. [17–18, 19]

A study conducted in rural India suggests that despite only 11.3% of hypertensive individuals having a body mass index exceeding the overweight threshold of 25 kg/m², a significantly higher proportion [38.5%] of hypertensive individuals exhibited an elevated waist-hip ratio [WHR] above the normal range in our study. This observation highlights the importance of WHR as a superior measure to BMI for assessing fat distribution and associated health risks in hypertensive populations. [20] Additionally, Individuals with a waist-to-hip ratio exceeding the cut-offs for females and males exhibited noticeably greater mean systolic blood pressure across all the three BMI categories suggesting a positive association between central adiposity as determined by WHR and higher systolic blood pressure, irrespective of overall body weight as indicated by BMI. Conversely, people with high WHR also tended to have higher mean diastolic blood pressure; however, this difference became statistically significant for individuals in the normal-weight BMI category. The mechanisms underlying this rise involve greater amounts of visceral fat activating the sympathetic nervous system, leading to a faster heart rate and narrowed blood vessels, ultimately raising blood pressure. The fundamental processes connecting WHR particularly to systolic blood pressure and its correlation with diastolic blood pressure require more investigation. [21]

Implications for policy, programme, and research:

We looked into the abdominal obesity risk factors that are most common in the Indian population. As far as we are aware, this is the first research that depicts a comprehensive overview of the prevalence and provides a broad understanding of determinants of waist-to-hip ratio using nationally representative survey data. The government and other key stakeholders should target adolescent and young adult individuals to transform nutritional patterns to reduce the rate of non-communicable and chronic diseases. The current NP-NCD programme should also involve strategies focused on this target group to effectively implement public health interventions.

The prevailing epidemic of abdominal obesity is in its nascent stage so more primary community-based studies are required to comprehend the function of various biological, social, and genetic factors supporting to this silent killer.

Strengths and limitations of the study:

Besides the strength of the study, the study also had a few limitations. Given the variables presented in the survey data, the study was not able to associate the prevalence with dietary habits and other contributing factors such as physical activity. So future research should focus on understanding health risks and lifestyle habits associated with abdominal obesity and identify the intervention addressing it.

Abdominal obesity is the result of the interaction between the various elements that impact to abdominal obesity. The rising incidence of abdominal obesity in the general population, especially among young adults, highlights the critical need for large studies to determine the causes and best ways to diagnose it. One measure that can be used to detect an individual's onset of obesity and assist reverse the prevalence of obesity in the population is the waist-to-hip ratio. This ratio can be corrected at an early stage. Given the enormous socioeconomic diversity of the population, a single technique may not be effective given the study's identification of several factors leading to the increased prevalence of waist-hip ratio.

Funding:

The current study was carried out independently using the author's resources and without receiving funds from public, commercial, or non-profit organizations or funding bodies.

Conflict of interest:

The authors declare that there is no potential conflict of interest to disclose.

Data Availability:

All data used in this analysis is available in the public domain [NFHS-5] downloaded from https://www.dhsprogram.com/

Authors Contribution:

RG - study planning, data evaluation, and final draft writing. AY and RS - Writing - original draft, data retrieval from NFHS-5, Preparation of figures and tables. RG, AY, RS - Data analysis. AP and MW – editing and reviewing all authors approved the final version of the manuscript.

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The authors declare no competing interests.

Exploring Waist-to-Hip Ratio and Health Risks: Insights from NFHS-5 on Trends, Patterns, and Predictors among the Indian Population

Status:

Version 1

Abstract

Figures

INTRODUCTION

METHODOLOGY

RESULTS

State-wise trends of WHR in the Indian States and UTs:

Prevalence of WHR among Study Population:

Risk Stratification and Association of Predicting Variables:

Regression Analysis of Predictors of Abdominal Obesity:

DISCUSSION

Implications for policy, programme, and research:

Strengths and limitations of the study:

CONCLUSION

Declarations

References

Additional Declarations

Status:

Version 1