European Journal of Cardiovascular Medicine

Vitamin D plays a central role in calcium–phosphate homeostasis and skeletal mineralization during childhood, and deficiency has been increasingly recognized as a common nutritional problem even among apparently healthy pediatric populations. Indian studies have consistently reported a substantial burden of hypovitaminosis D across diverse pediatric age groups, underscoring that deficiency is not limited to children with overt illness or severe malnutrition. In a study from North India, Angurana et al. reported a high prevalence of vitamin D deficiency in apparently healthy children, highlighting the hidden nature of this micronutrient deficit and the likelihood of under-detection in routine care settings [1].

Despite wide use of serum 25-hydroxyvitamin D (25[OH]D) as the biomarker of vitamin D status, clinically meaningful thresholds for deficiency remain debated across populations and outcomes. Some studies have attempted to derive cutoffs using metabolic risk markers rather than bone endpoints alone. For example, Sharifi et al. proposed a deficiency cutoff anchored to insulin resistance metrics in children, illustrating that vitamin D status may have implications beyond skeletal growth and that threshold selection can influence estimated prevalence [2]. Such variability in definitions, alongside differences in geography, sunlight exposure, diet, and supplementation practices, contributes to heterogeneity in reported prevalence and complicates comparisons between regions.

Beyond prevalence estimation, an important public health question is whether vitamin D deficiency is associated with impaired growth in children. Evidence from outside India suggests that low vitamin D status may be linked to adverse anthropometric outcomes. Mokhtar et al. demonstrated an association between vitamin D status and underweight and stunting among young children in the Ecuadorian Andes, providing biologic and epidemiologic support for evaluating growth correlates of vitamin D in pediatric populations [3]. However, the strength and pattern of association may vary by setting due to differences in baseline nutrition, infection burden, socioeconomic gradients, and sunlight availability.

Within India, multicentric data have reinforced that vitamin D deficiency is widespread and shaped by multiple determinants. Khadilkar et al., in a large multicentre study of Indian children and adolescents, described both the distribution of vitamin D status and key determinants such as demographic and environmental factors, emphasizing that risk is multifactorial and not explained by a single exposure [4]. These findings further support the need for region-specific estimates, particularly in underrepresented settings such as coastal and semi-rural districts, where sunlight patterns, clothing practices, dietary habits, and socioeconomic profiles may differ from large metropolitan cohorts. The same multicentre evidence base also underscores the importance of simultaneously assessing determinants (e.g., sun exposure, diet, supplementation, socioeconomic position) when examining growth correlates, to reduce confounding and improve interpretability of observed associations [5].

Sindhudurg district in coastal Maharashtra has distinctive environmental and lifestyle features that may influence pediatric vitamin D status and growth. Yet, there remains limited local evidence quantifying deficiency and evaluating its relationship with growth parameters among children in the 1–10 year age range. Therefore, this cross-sectional study was designed to estimate the prevalence of vitamin D deficiency in children aged 1–10 years attending SSPM Medical College and Lifetime Hospital, Padve, and to examine the association of vitamin D status with anthropometric growth indicators, while accounting for key demographic and exposure-related determinants.

Objectives

Aim

To determine the prevalence of vitamin D deficiency and evaluate its association with growth parameters among children aged 1–10 years attending SSPM Medical College and Lifetime Hospital, Padve, Sindhudurg, Maharashtra.

Specific objectives

1. To estimate the prevalence of vitamin D status categories using serum 25(OH)D: deficiency (<20 ng/mL), insufficiency (20–29 ng/mL), and sufficiency (≥30 ng/mL).
2. To compare anthropometric and growth indices (height, weight, BMI; HAZ, WAZ, BAZ) across vitamin D categories.
3. To assess the association between vitamin D deficiency and undernutrition outcomes (stunting, underweight, thinness) and over-nutrition (overweight/obesity).
4. To identify key determinants of vitamin D deficiency, including age, sex, residence, socioeconomic status, season of sampling, sun exposure, outdoor activity, dietary frequency (milk/egg/fish), and recent vitamin D supplementation.

Study design and setting A hospital-based cross-sectional study was conducted over 6 months at SSPM Medical College and Lifetime Hospital, Padve, Sindhudurg, Maharashtra. Participants and eligibility Children aged 1–10 years attending the pediatric outpatient department and/or admitted for non-critical indications during the study period were eligible. Written informed consent was obtained from parents/guardians prior to enrollment. Children were excluded if they had conditions known to affect vitamin D metabolism or growth, including chronic renal or liver disease, malabsorption syndromes, endocrine disorders, genetic syndromes influencing growth, current or recent therapeutic (high-dose) vitamin D treatment, or critical illness requiring emergency resuscitation. Sample size and sampling A total sample of 400 children was enrolled using consecutive sampling over the study period. Data collection A structured case record form was used to collect demographic and exposure information, including age, sex, residence (rural/urban), socioeconomic status, season of sampling, average daily sun exposure (hours/day), outdoor activity level, dietary frequency (milk/egg/fish intake expressed as days per week), and history of vitamin D supplementation in the preceding three months. Anthropometry and growth assessment Weight was measured using a calibrated digital weighing scale with children in light clothing and without footwear. Standing height was measured using a stadiometer; where age-appropriate, recumbent length was measured using a length board. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Age- and sex-standardized indices—height-for-age Z-score (HAZ), weight-for-age Z-score (WAZ), and BMI-for-age Z-score (BAZ)—were derived using standard reference algorithms. Nutritional status was categorized as stunting (HAZ < −2), underweight (WAZ < −2), thinness (BAZ < −2), overweight (BAZ > +1), and obesity (BAZ > +2). Laboratory assessment and definitions Venous blood samples were collected under aseptic precautions. Serum 25-hydroxyvitamin D [25(OH)D] was measured in the institutional laboratory using a standardized assay with routine internal quality checks. Vitamin D status was defined as deficiency (<20 ng/mL), insufficiency (20–29 ng/mL), and sufficiency (≥30 ng/mL). Where available from routine evaluation, serum calcium and alkaline phosphatase were also recorded. Outcomes The primary outcome was the prevalence of vitamin D deficiency (<20 ng/mL). Secondary outcomes included associations between vitamin D status and growth parameters (height, weight, BMI and derived HAZ/WAZ/BAZ), associations with nutritional status categories (stunting, underweight, thinness, overweight/obesity), and determinants of vitamin D deficiency. Statistical analysis Continuous variables were summarized as mean ± standard deviation and categorical variables as number (percentage). Prevalence estimates were reported with 95% confidence intervals. Comparisons across vitamin D categories were performed using one-way ANOVA for continuous variables and chi-square tests for categorical variables. Multivariable linear regression was used to evaluate associations between vitamin D status and Z-scores (HAZ, WAZ, BAZ), while multivariable logistic regression was used to assess determinants of vitamin D deficiency and associations with binary nutritional outcomes. Models were adjusted for prespecified covariates including age, sex, residence, socioeconomic status, season, sun exposure, outdoor activity, dietary frequency variables, and recent vitamin D supplementation. A two-sided p value <0.05 was considered statistically significant. Ethics The study protocol was approved by the Institutional Ethics Committee. Confidentiality was maintained using de-identified study codes.

1.Participant characteristics (N=400)

A total of 400 children aged 1–10 years were included in the analysis. Participant demographics, sampling season, sun exposure profile, dietary frequency indicators, and supplementation history are summarized in Table 1.

Table1. Participant characteristics (N=400)

2. Prevalence of vitamin D deficiency

Overall, 161/400 children (40.3%) were vitamin D deficient (25[OH]D <20 ng/mL; 95% CI 35.6–45.1). An additional 101/400 (25.3%) had insufficiency (20–29 ng/mL; 95% CI 21.2–29.7), while 138/400 (34.5%) were sufficient (≥30 ng/mL; 95% CI 30.0–39.3). The distribution of vitamin D status is presented in Table 2and shown graphically in Figure 1

Table 2. Prevalence of vitamin D status categories among children aged 1–10 years (N=400).
(Serum 25[OH]D classified as deficient <20 ng/mL, insufficient 20–29 ng/mL, and sufficient ≥30 ng/mL; values shown as n, % and 95% confidence intervals.)

Figure 1. Prevalence of vitamin D deficiency, insufficiency, and sufficiency among children aged 1–10 years (N=400).

3.Baseline characteristics by vitamin D status

Baseline characteristics stratified by vitamin D category are summarized in Table 3. Mean daily sun exposure differed across vitamin D groups, with children who were vitamin D deficient reporting lower sun exposure (1.56 ± 0.85 h/day) compared with those who were vitamin D sufficient (1.82 ± 0.82 h/day; p = 0.027). Age distribution, sex, residence, socioeconomic status, season of sampling, and reported dietary frequency (milk/egg/fish) were broadly comparable across vitamin D categories (all p > 0.05). Milk intake frequency showed a borderline trend toward lower intake in deficient children (4.56 ± 1.94 days/week) versus sufficient children (5.05 ± 1.72 days/week; p = 0.065), and outdoor activity pattern also showed a borderline difference (p = 0.064). Reported vitamin D supplementation in the preceding 3 months did not differ significantly by vitamin D status (p = 0.144).

Table 3. Baseline characteristics of children aged 1–10 years stratified by vitamin D status (N=400).

Note: Values are mean ± SD or n (%). p-values from one-way ANOVA (continuous variables) or χ² test (categorical variables).

4.Growth parameters and nutritional status by vitamin D status

Growth parameters stratified by vitamin D category are shown in Table 4. Mean HAZ differed significantly across categories, with children who were vitamin D deficient showing lower HAZ than those who were sufficient (−1.06 ± 1.07 vs −0.73 ± 1.17; p = 0.028). Mean WAZ showed a similar direction but did not reach conventional statistical significance (p = 0.058), while BAZ did not differ (p = 0.747). The proportions of stunting, underweight, thinness, overweight, and obesity were not significantly different across vitamin D categories (all p > 0.05). Biochemical correlates showed expected variation: mean serum calcium differed across groups (p < 0.001) and alkaline phosphatase was higher in the deficient group (p < 0.001) (Table 4).

Table 4. Growth parameters and nutritional status stratified by vitamin D status (N=400)

Note: Continuous variables compared by one-way ANOVA; categorical outcomes compared by χ² test. Values are mean ± SD or n (%).

Abbreviations: HAZ (Height-for-age Z-score), WAZ (Weight-for-age Z-score), BAZ (BMI-for-age Z-score) 

  5.Adjusted association between vitamin D status and growth outcomes

In multivariable linear regression (adjusted for age, sex, residence, socioeconomic status, season, sun exposure, outdoor activity, diet frequency, and recent supplementation), vitamin D deficiency was independently associated with lower HAZ and lower WAZ compared with vitamin D sufficiency. Specifically, deficient children had a 0.37 SD lower HAZ (adjusted β −0.37, 95% CI −0.64 to −0.10; p=0.008) and a 0.29 SD lower WAZ (adjusted β −0.29, 95% CI −0.55 to −0.04; p=0.024). There was no adjusted association with BAZ (p>0.05). Vitamin D insufficiency was not significantly associated with HAZ, WAZ, or BAZ after adjustment (all p>0.05).

In adjusted logistic regression models, vitamin D deficiency showed higher odds of stunting, underweight, and thinness compared with sufficiency; however, these associations did not reach statistical significance and confidence intervals were wide (Table 5).

Table 5. Adjusted association of vitamin D status with growth indicators and undernutrition outcomes (N=400)

Table 5A. Adjusted linear regression (reference: Sufficient ≥30 ng/mL)
Outcome = Z-score (HAZ/WAZ/BAZ); values are adjusted β (95% CI)

Table 5B. Adjusted logistic regression (reference: Sufficient ≥30 ng/mL)
Outcome = binary nutritional status; values are adjusted OR (95% CI)

Note: Models adjusted for age, sex, residence, SES, season, sun exposure, outdoor activity, milk/egg/fish intake frequency, and recent vitamin D supplementation.

 6.Determinants of vitamin D deficiency

In a multivariable logistic regression model with vitamin D deficiency (<20 ng/mL) as the outcome (adjusted for age, sex, residence, socioeconomic status, season, sun exposure, outdoor activity, diet frequency, and recent supplementation), greater daily sun exposure was independently associated with lower odds of deficiency (aOR 0.77 per 1 hour/day increase; 95% CI 0.60–0.99; p=0.042). Compared with children reporting high outdoor activity, those with moderate outdoor activity had lower odds of deficiency (aOR 0.56; 95% CI 0.32–0.96; p=0.036). Recent vitamin D supplementation showed a protective direction but did not meet conventional significance (aOR 0.59; 95% CI 0.35–1.02; p=0.057). Other demographic variables (age, sex, residence, SES, season) and dietary frequency measures did not show statistically significant independent associations (Table 6).

Table 6. Multivariable determinants of vitamin D deficiency (<20 ng/mL) among children aged 1–10 years (N=400)

Note: Outcome = vitamin D deficiency (<20 ng/mL). Reference categories: Female, Rural, Lower SES, Monsoon, High outdoor activity.

Our study from coastal Sindhudurg found that 40.2% (161/400) of children aged 1–10 years had vitamin D deficiency (<20 ng/mL), with another 25.2% insufficient—so nearly two-thirds (65.4%) were below 30 ng/mL. The deficiency burden was higher in 6–10-year-olds (45.4%) than 1–5-year-olds (36.7%), while it was similar by sex (≈40% in both boys and girls). Importantly, deficiency showed a measurable growth signal: mean HAZ was lower in deficient children (−1.06 ± 1.07) vs sufficient (−0.73 ± 1.17) (p=0.028), and after adjustment, deficiency remained associated with lower HAZ (adjusted β −0.37; 95% CI −0.64 to −0.10; p=0.008) and lower WAZ (adjusted β −0.29; 95% CI −0.55 to −0.04; p=0.024). However, categorical outcomes (stunting/underweight/thinness) trended higher but did not reach significance (e.g., stunting aOR 1.55; p=0.187), consistent with an effect that may be detectable first on continuous growth metrics rather than dichotomized cut-offs in a cross-sectional design.

Pai and colleagues (2025), while presenting a protocol rather than outcome data, synthesize the Indian context by noting that childhood vitamin D deficiency prevalence varies widely across regions (often cited in the 30%–82% range), and they planned their under-five trial using an estimated baseline prevalence of ~60.9% for sample-size assumptions (total planned n=120). That range overlaps with our 65.4% below 30 ng/mL and underscores that even in sun-rich settings, deficiency/insufficiency can remain common—supporting our finding that a coastal geography does not guarantee vitamin D sufficiency in children [6].

Mandlik et al. (2018) provide an instructive parallel: in semirural Indian schoolchildren aged 6–12 years, they reported that a majority (71%) fell into an insufficient range (reported as 50–74.9 nmol/L), despite 80% reporting >2 hours/day of sunlight exposure, and they identified sunlight exposure and adiposity as key determinants [7]. Our data align in direction but add nuance: we observed a significant independent association of sunlight exposure with lower odds of deficiency (aOR 0.77 per additional hour/day; p=0.042), yet deficiency persisted at ~40% even with a mean reported exposure of 1.66 ± 0.87 hours/day, highlighting that exposure duration alone may not fully capture effective UVB dose (timing, clothing, skin pigmentation, and outdoor behavior patterns likely matter), and that “adequate” exposure by self-report can still coexist with low 25(OH)D.

Intervention implications are supported by randomized supplementation evidence. In Marwaha et al.’s large North Indian trial (n=1008 vitamin D–deficient schoolchildren/adolescents) comparing daily 600 vs 1000 vs 2000 IU vitamin D₃, treatment adherence was high (93% reported compliance over 12 months), demonstrating feasibility of daily dosing strategies at scale in school-age groups [8]. While our study was observational, the magnitude of deficiency we observed (40%) and the growth Z-score shifts associated with deficiency support the public-health value of testing and correction strategies in pediatric services—particularly in older children where we saw higher deficiency prevalence.

Not all literature shows a straightforward “lower vitamin D = worse growth” pattern, and that contrast is useful when interpreting our findings. In the Bogor (Indonesia) case–control analysis by Fajrunni’mah et al. (2025), mean 25(OH)D was 26.67 ± 5.29 nmol/L in stunted children versus 23.34 ± 4.17 nmol/L in controls, i.e., values were low in both groups and the direction was not consistently lower among the stunted group [9]. Compared with our findings—where deficiency related to lower continuous HAZ and WAZ but only nonsignificant elevation in odds of stunting—this suggests that when populations cluster at very low vitamin D levels, or when confounding by diet/infection/inflammation differs, between-group contrasts on categorical growth outcomes may weaken, even if subtle growth gradients persist on continuous scales.

Prospective cohort evidence strengthens biological plausibility for the growth gradient we observed. Xiao et al. (2023), in a cohort of 10,450 children with repeated 25(OH)D measurement, found that each 10 nmol/L higher 25(OH)D was associated with a 0.15 cm/year higher height growth velocity (P<0.001) and a 7% lower risk of incident low BMD (RR 0.93; 95% CI 0.87–0.98); vitamin D sufficiency also conferred a 22% lower low-BMD risk versus deficiency (RR 0.78; 95% CI 0.62–0.98) [10]. Although our design cannot estimate growth velocity, the direction is consistent: children with lower 25(OH)D showed poorer growth Z-scores (HAZ/WAZ), suggesting that vitamin D may contribute to linear growth and skeletal health pathways that become clearer in longitudinal follow-up.

Indian hospital-based prevalence and risk-pattern data also align broadly with our burden estimate while illustrating how setting shapes measured prevalence. Vasudevan et al. (2014) reported hypovitaminosis D in 62.2% of children (6 months–18 years) and found higher odds with increasing age (OR 1.3) and female sex (OR 1.1) [11]. Our prevalence of deficiency alone was lower (40.2%), but when combined with insufficiency our “<30 ng/mL” proportion (65.4%) is strikingly comparable to Vasudevan’s overall hypovitaminosis estimate; similarly, we observed higher deficiency in older children (6–10 years: 45.4%) than younger (1–5 years: 36.7%), supporting an age-gradient even within a narrower pediatric band.

Older population-based Indian data emphasize that low vitamin D is not confined to one geography or urbanicity. Harinarayan et al. (2008) evaluated urban/rural adults and children in Andhra Pradesh and reported that 25(OH)D levels in both urban and rural children were low, with parathyroid hormone inversely correlated with 25(OH)D (r approximately −0.20 in children; P<0.05), consistent with physiologic bone-mineral homeostasis stress when vitamin D is low [12]. While we did not measure PTH/biochemical bone markers, our observed growth Z-score shifts among deficient children fit with the concept that vitamin D inadequacy may manifest along a spectrum—from biochemical compensation to measurable growth differences—depending on severity and duration.

Large-scale laboratory surveillance further illustrates how prevalence varies with cut-offs, sampling frame, and seasonality. Sreenivasulu et al. (2024), analyzing 11,428 assays (2018–2020), reported a median 25(OH)D of 17.2 ng/mL with 60% deficient (<20 ng/mL), and a clear seasonal shift in median values (18.7 ng/mL in summer/monsoon vs 15.8 ng/mL in winter/spring) [13]. Compared with those values, our cohort had a higher mean 25(OH)D (24.6 ± 10.6 ng/mL) and lower deficiency prevalence (40%), plausibly reflecting differences in age mix, outpatient sampling vs lab-referral populations, coastal lifestyle factors, and regional UVB exposure patterns; notably, our deficiency prevalence did not spike in winter (winter 36%), which may differ from North-Western institutional patterns.

The infant–mother dyad evidence from Jain et al. (2011) contextualizes why early-life deficiency can be especially severe even in India: among 3-month healthy breastfed infants, vitamin D deficiency was 66.7% and insufficiency another 19.8%; mothers had deficiency 81.1%, and 30.3% of infants with 25(OH)D <10 ng/mL had radiologic rickets. Maternal 25(OH)D, infant supplementation, and sunlight exposure predicted infant vitamin D status; infants born to deficient mothers had higher odds of deficiency (OR 3.4; 95% CI 1.2–9.9) [14]. Although our cohort was older (1–10 years), these data support a life-course interpretation: children may enter early childhood with low vitamin D stores, and without consistent supplementation/adequate effective sunlight, deficiency can persist into later age groups where we observed the highest prevalence.

Finally, our findings on adiposity-related nuances should be interpreted alongside interventional evidence in overweight/obese children. Corsello et al. (2023) synthesized 23 trials and found that vitamin D supplementation in pediatric overweight/obesity increased 25(OH)D only modestly, with a pooled mean difference of 1.6 ng/mL versus placebo, while metabolic/cardiovascular effects were inconsistent [15]. In our dataset, overweight prevalence was low (6.5%), and vitamin D deficiency was more clearly patterned by sunlight exposure and age than by residence/SES; nonetheless, Corsello’s results caution that supplementation response may be attenuated in higher-adiposity subgroups, which is relevant if local pediatric obesity rises over time.

Overall, our results support three publishable inferences: (i) vitamin D deficiency is common in coastal western India (40% deficient; 65% below 30 ng/mL), (ii) deficiency is linked to small-to-moderate decrements in continuous growth indices (adjusted HAZ and WAZ differences), and (iii) modifiable exposures—especially sunlight exposure duration—show measurable association with deficiency risk. The differences between our estimates and those from other Indian regions and sampling frames emphasize the importance of regional and methodological variation (age band, season distribution, assay context, and cut-offs) when comparing prevalence, and they justify locally generated data to guide pediatric screening and prevention strategies.

Limitations

This was a single-center, hospital-based cross-sectional study, which limits generalizability and precludes causal inference between vitamin D status and growth. Sun-exposure and dietary frequency were self-reported, introducing potential recall/misclassification bias. Residual confounding from unmeasured factors (e.g., infection burden, detailed nutrient intake, physical activity intensity, and assay/seasonal variability) may persist despite adjustment.

Vitamin D deficiency was common in children aged 1–10 years in coastal Sindhudurg, with 40.3% deficient and 65.6% below sufficiency. Deficiency was independently associated with lower linear growth indices (HAZ and WAZ), and greater sun exposure was protective against deficiency. These findings support strengthening routine screening/targeted testing in high-risk children and emphasizing pragmatic exposure and supplementation strategies alongside broader nutrition programs, while larger community-based longitudinal studies are needed to confirm growth impacts and guide region-specific policy.

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