Prevalence, Regional Variations, and Predictors of Overweight, Obesity, and Hypertension Among Healthy Reproductive-Age Indian Women: Nationwide Cross-Sectional Polycystic Ovary Syndrome Task Force Study
Notice bibliographique
Résumé
BACKGROUND: A clear understanding of the anthropometric and sociodemographic risk factors related to BMI and hypertension categories is essential for more effective disease prevention, particularly in India. There is a paucity of nationally representative data on the dynamics of these risk factors, which have not been assessed among healthy reproductive-age Indian women. OBJECTIVE: This cross-sectional polycystic ovary syndrome (PCOS) task force study aimed to assess the anthropometric and sociodemographic characteristics of healthy reproductive-age Indian women and explore the association of these characteristics with various noncommunicable diseases. METHODS: We conducted a nationwide cross-sectional survey from 2018 to 2022 as part of the Indian Council of Medical Research-PCOS National Task Force study, with the primary aim of estimating the national prevalence of PCOS and regional phenotypic variations among women with PCOS. A multistage random sampling technique was adopted, and 7107 healthy women (aged 18-40 years) from 6 representative geographical zones of India were included in the study. The anthropometric indices and sociodemographic characteristics of these women were analyzed. Statistical analysis was performed to assess the association between exposure and outcome variables. RESULTS: Of the 7107 study participants, 3585 (50.44%) were from rural areas and 3522 (49.56%) were from urban areas. The prevalence of obesity increased from 8.1% using World Health Organization criteria to 40% using the revised consensus guidelines for Asian Indian populations. Women from urban areas showed higher proportions of overweight (524/1908, 27.46%), obesity (775/1908, 40.62%), and prehypertension (1008/1908, 52.83%) categories. A rising trend of obesity was observed with an increase in age. Women aged 18 to 23 years were healthy (314/724, 43.4%) and overweight (140/724, 19.3%) compared with women aged 36 to 40 years with obesity (448/911, 49.2%) and overweight (216/911, 23.7%). The proportion of obesity was high among South Indian women, with 49.53% (531/1072) and 66.14% (709/1072), using both World Health Organization criteria and the revised Indian guidelines for BMI, respectively. BMI with waist circumference and waist-to-height ratio had a statistically significant linear relationship (r=0.417; P<.001 and r=0.422; P<.001, respectively). However, the magnitude, or strength, of the association was relatively weak (0.3<|r|<0.5). Statistical analysis showed that the strongest predictors of being overweight or obese were older age, level of education, wealth quintile, and area of residence. CONCLUSIONS: Anthropometric and sociodemographic characteristics are useful predictors of overweight- and obesity-related syndromes, including prehypertension, among healthy Indian women. Increased attention to the health of Indian women from public health experts and policy makers is warranted. The findings of this study can be leveraged to offer valuable insights, informing health decision-making and targeted interventions that mitigate risk factors of overweight, obesity, and hypertension. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/23437.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».