Abstract The rs1544410 (BsmI) polymorphism of the vitamin D receptor (VDR) gene has been implicated in metabolic regulation, although its role in metabolic syndrome (MetS) and related phenotypes remains unclear. This study aimed to evaluate associations between rs1544410, MetS status, and anthropometric and biochemical parameters in institutionalized older adults. A total of 95 participants were included, of whom 40% met the criteria for MetS. Anthropometric and biochemical profiles were assessed, and rs1544410 genotyping was performed. Differences between MetS and non-MetS groups were analyzed, and two-way ANOVA was used to evaluate genotype, MetS status, and their interaction effects. Participants with MetS showed an adverse cardiometabolic profile, characterized by higher triglycerides (TGs), waist-to-hip ratio (WHR), and atherogenic index of plasma (AIP), as well as lower HDL-C levels compared with non-MetS individuals. No differences were observed for total cholesterol (TC), LDL-C, or non-esterified fatty acids (NEFAs) between groups. Genotype distributions did not differ between MetS and non-MetS participants. However, significant genotype × MetS interactions were observed for TG and NEFA, with a borderline interaction for WHR that was not confirmed in post hoc analyses. Carriers of the rs1544410 AA genotype within the MetS group exhibited higher TG and NEFA levels compared with other genotypes, whereas no genotype-dependent differences were observed in the non-MetS group. Importantly, AIP was higher in participants with MetS, with the highest values observed in AA genotype carriers. In conclusion, the rs1544410 polymorphism was not associated with MetS status but was linked to MetS-related differences in TG, NEFA, and AIP, suggesting selective effects on lipid metabolism. 1. Introduction MetS is highly prevalent in older adults and contributes significantly to cardiovascular morbidity in this population. Epidemiological studies indicate that the prevalence of MetS increases progressively with age and varies depending on the diagnostic criteria applied, reaching particularly high levels in elderly populations [ 3, 4, 5]. In institutionalized older adults, reported prevalence rates vary widely across studies and regions, ranging from approximately 20% to nearly 60% [ 6]. Its occurrence is partly driven by aging-related metabolic dysregulation, including increased insulin resistance, altered lipid metabolism, and a higher prevalence of central adiposity [ 3]. Recent evidence also suggests substantial heterogeneity in metabolic profiles among older individuals, reflecting the complex interactions between biological aging, comorbid conditions, and functional decline [ 7]. Institutionalized older adults constitute a particularly vulnerable subgroup due to a high burden of multimorbidity, functional impairment, and increased dependence on long-term care. In this setting, metabolic disorders often coexist with frailty and polypharmacy, which may further exacerbate cardiometabolic risk [ 4, 5]. The biological effects of vitamin D are primarily mediated by the vitamin D receptor (VDR), a ligand-activated transcription factor encoded by the VDR gene and expressed in multiple metabolically relevant tissues, including adipose tissue, skeletal muscle, liver, and pancreatic β cells. Its wide tissue distribution underscores the systemic nature of vitamin D signaling, extending beyond its classical role in calcium and bone metabolism. Upon ligand binding, VDR forms a heterodimer with the retinoid X receptor and regulates gene transcription via vitamin D response elements, thereby modulating pathways involved in immune function, metabolic regulation, and cellular homeostasis [ 12]. In addition, non-genomic signaling mechanisms mediated by membrane-associated receptor forms have been described [ 13]. Notably, aging is associated with reduced VDR expression [ 14], which may contribute to increased susceptibility to metabolic disturbances [ 15]. In this context, both age-related changes in receptor expression and genetic variability within the VDR gene may play a role in modulating individual metabolic risk. Given the central role of VDR in vitamin D signaling, polymorphisms within the VDR gene are considered potential determinants of inter-individual differences in metabolic responses [ 16]. Such variants may influence receptor activity and downstream signaling efficiency, thereby modifying metabolic processes. In particular, the rs1544410 (BsmI) polymorphism has been widely investigated in relation to metabolic traits; however, the findings remain inconsistent [ 17]. Moreover, data on the interaction between VDR polymorphisms and metabolic syndrome status, particularly in older institutionalized populations, remain limited. The aim of the present study was to investigate whether the rs1544410 (BsmI) polymorphism of the VDR gene modifies metabolic and anthropometric characteristics according to MetS status in older individuals residing in long-term-care facilities. We hypothesized that VDR genetic variability interacts with metabolic status, contributing to differences in biochemical and anthropometric profiles. 2. Results The study population consisted of 95 institutionalized elderly participants. Detailed descriptive statistics for all analyzed parameters are presented in Table 1. On average, BMI values were within the overweight range, and a considerable proportion of participants met the criteria for abdominal obesity according to sex-specific waist circumference cut-offs ( Table 2). The lipid profile was characterized by features consistent with atherogenic dyslipidemia, including elevated TG and reduced HDL-C concentrations. MetS was identified in 40.0% of participants, while the remaining individuals did not meet the diagnostic criteria. The most frequently observed abnormality was non-HDL cholesterol meeting the defined threshold or lipid-lowering treatment, followed by increased waist circumference and elevated blood pressure. Impaired fasting glucose or glucose-lowering treatment was less frequent. The distribution of MetS components and diagnostic criteria is presented in Table 2. Comparison of clinical and biochemical parameters between participants with and without MetS is presented in Table 3. Participants with MetS exhibited an adverse cardiometabolic profile. Specifically, significantly higher TG levels and WHR, together with significantly lower HDL-C concentrations, were observed in the MetS group ( p 0.05). To assess the genetic background of the study population, rs1544410 (BsmI) genotype distributions were first evaluated for deviation from Hardy–Weinberg equilibrium (HWE). No significant deviation from HWE was observed in either the MetS or non-MetS group ( Table 4), indicating that the observed genotype frequencies were consistent with expected population distributions. Subsequently, genotype and allele frequencies were compared between participants with and without MetS. No significant differences were observed in either genotype distribution (χ 2 = 0.450, p = 0.451) or allele frequencies (χ 2 = 0.406, p = 0.524) between groups ( Table 5). The effects of metabolic syndrome status, rs1544410 (BsmI) polymorphism, and their interaction on anthropometric and biochemical parameters were assessed using two-way ANOVA ( Table 6). Two-way ANOVA revealed significant effects of metabolic syndrome status on TG ( p 0.05). The association between the rs1544410 (BsmI) VDR polymorphism, metabolic syndrome status, biochemical parameters, and anthropometric measurements was examined using two-way analysis of variance (ANOVA), including genotype, MetS status (MetS vs. non-MetS), and their interaction. Sex was not included as a factor due to limited sample size. Fisher’s LSD post hoc test was applied following a significant ANOVA result because the number of planned pairwise comparisons was limited, and this method provides greater statistical power for detecting subtle differences between groups compared with more conservative post hoc procedures. The atherogenic index of plasma (AIP) was analyzed separately from the ANOVA model, as it is a derived measure based on TG and HDL-C concentrations. To avoid redundancy, AIP was assessed using multiple linear regression with dummy-coded rs1544410 genotypes, with the heterozygous genotype (AG) used as the reference category. All statistical analyses were performed using STATISTICA 13 (TIBCO Software Inc., Palo Alto, CA, USA). 5. Conclusions In conclusion, the rs1544410 polymorphism of the VDR gene was not associated with MetS status in institutionalized older adults but contributed to inter-individual variability in TG, NEFA, and AIP depending on metabolic status. These findings suggest that VDR-related genetic effects on lipid metabolism are context-specific and become more evident under metabolic stress conditions. Institutional Review Board Statement The study was conducted according to the guidelines of the Bioethics Committee of the Medical College at the University of Zielona Gora, Poland (Approval No. 16/2024, approved on 6 June 2024), in accordance with the Declaration of Helsinki (KB-889/18). Figure 1. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on triglycerides (TG). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 1. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on triglycerides (TG). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 2. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on total cholesterol (TC). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 2. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on total cholesterol (TC). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 3. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on non-esterified fatty acids (NEFA). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 3. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on non-esterified fatty acids (NEFA). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 4. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on thigh circumference (cm). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 4. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on thigh circumference (cm). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 5. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on waist-to-hip ratio (WHR). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Figure 5. Interaction between VDR rs1544410 (BsmI) genotype and metabolic syndrome status on waist-to-hip ratio (WHR). MetS—participants with metabolic syndrome; non-MetS—participants without metabolic syndrome. Table 1. Characteristics of the study population. Table 1. Characteristics of the study population. Variable Mean ବ୍ଦ ଝଊ Median (IQR) Age (years) ୬୨.୭ ବ୍ଦ ୧୪.୧ 64 (54.0; 71.0) SBP (mmHg) ୧୨୨.୦୪ ବ୍ଦ ୧୨.୦୮ 121.50 (113.00; 131.25) DBP (mmHg) ୭୬.୫୩ ବ୍ଦ ୭.୮୮ 76.25 (72.25; 81.00) Anthropometric parameters Body weight (kg) ୭୬.୪ ବ୍ଦ ୧୬.୭ 76 (63.7; 87.0) Height (cm) ୧୬୬.୮ ବ୍ଦ ୯.୮ 165 (160.0; 175.0) BMI (kg/m 2) ୨୭.୫ ବ୍ଦ ୫.୪ 26.5 (23.5; 31.6) Waist circumference (cm) ୧୦୧.୭ ବ୍ଦ ୧୫.୧ 102.0 (89.0; 113.0) Arm circumference (cm) ୨୯.୨ ବ୍ଦ ୩.୯ 29.0 (26.0; 31.0) Thigh circumference (cm) ୪୯.୩ ବ୍ଦ ୬.୬ 49.0 (44.0; 54.0) Hip circumference (cm) ୧୦୨.୯ ବ୍ଦ ୧୦.୧ 103.0 (96.0; 108.0) WHR ୦.୯୮୭ ବ୍ଦ ୦.୦୯୮ 1.0 (0.919; 1.056) Biochemical parameters Glucose (mg/dL) ୮୭.୬୩ ବ୍ଦ ୩୩.୫୧ 76.29 (68.24; 97.91) TG (mg/dL) ୧୫୭.୩୪ ବ୍ଦ ୬୮.୨୯ 139.63 (115.35; 169.83) TC (mg/dL) ୨୧୩.୭୦ ବ୍ଦ ୫୧.୫୯ 202.59 (179.91; 239.71) LDL-C (mg/dL) ୧୦୬.୦୧ ବ୍ଦ ୩୩.୩୬ 103.77 (89.62; 121.70) HDL-C (mg/dL) ୪୧.୭୫ ବ୍ଦ ୧୩.୦୪ 39.22 (33.79; 46.47) non-HDL-C (mg/dL) ୧୭୧.୯୪ ବ୍ଦ ୪୬.୮୬ 160.07 (145.56; 196.26) NEFA (mmol/L) ୦.୯୨୯ ବ୍ଦ ୦.୧୭୧ 0.880 (0.818; 0.998) AIP ୦.୨୦୭ ବ୍ଦ ୦.୨୦୬ 0.176 (0.060; 0.319) Abbreviations: SBP—systolic blood pressure; DBP—diastolic blood pressure; BMI—body mass index; WHR—waist-to-hip ratio; TG—triglycerides; TC—total cholesterol; LDL-C—low-density lipoprotein cholesterol; HDL-C—high-density lipoprotein cholesterol; non-HDL-C—non-high-density lipoprotein cholesterol; NEFA—non-esterified fatty acids; AIP—atherogenic index of plasma; SD—standard deviation; IQR—interquartile range. Table 2. Metabolic syndrome components and clinical characteristics. Table 2. Metabolic syndrome components and clinical characteristics. Variable Criterion (Cut-Off/Treatment) n (%) BMI ≥30 kg/m 228 (29.5) Waist circumference ≥102 cm (men), ≥88 cm (women) 59 (62.1) Fasting glucose ≥100 mg/dL or glucose-lowering treatment 29 (30.5) Non-HDL-C ≥130 mg/dL or lipid-lowering treatment 89 (93.7) SBP/DBP ≥130/85 mmHg or antihypertensive treatment 53 (55.8) Metabolic syndrome (MetS) Obesity and 2 of 3 additional criteria 38 (40.0) Abbreviations: BMI—body mass index; non-HDL-C—non-high-density lipoprotein cholesterol; SBP/DBP—systolic/diastolic blood pressure; MetS—metabolic syndrome. Table 3. Clinical and biochemical characteristics of MetS and non-MetS participants. Table 3. Clinical and biochemical characteristics of MetS and non-MetS participants. Variable MetS (n = 38) Non-MetS (n = 57) Test Statistic ( t or U) p Age (years) ୬୩.୬୬ ବ୍ଦ ୧୫.୯୩ ୬୨.୦୭ ବ୍ଦ ୧୨.୯୦ 0.534 0.594 Body weight (kg) ୭୯.୩୦ ବ୍ଦ ୧୫.୫୭ ୭୪.୪୯ ବ୍ଦ ୧୭.୨୧ 1.384 0.170 Height (cm) ୧୬୭.୦୦ ବ୍ଦ ୧୦.୦୦ ୧୬୬.୦୦ ବ୍ଦ ୧୦.୦୦ 0.542 0.589 BMI (kg/m 2) ୨୮.୨୨ ବ୍ଦ ୪.୭୧ ୨୬.୯୪ ବ୍ଦ ୫.୮୬ 1.124 0.264 Arm circumference (cm) ୨୯.୯୭ ବ୍ଦ ୩.୪୨ ୨୮.୭୪ ବ୍ଦ ୪.୧୨ 1.530 0.130 Thigh circumference (cm) ୪୯.୫୮ ବ୍ଦ ୬.୦୬ ୪୯.୧୪ ବ୍ଦ ୬.୯୨ 0.318 0.751 Waist circumference (cm) ୧୦୪.୭୯ ବ୍ଦ ୧୩.୫୨ ୯୯.୭୨ ବ୍ଦ ୧୫.୭୮ 1.622 0.108 Hip circumference (cm) ୧୦୩.୫୦ ବ୍ଦ ୭.୮୦ ୧୦୨.୫୪ ବ୍ଦ ୧୧.୪୫ 0.449 0.654 WHR (waist/hip ratio) ୧.୦୧ ବ୍ଦ ୦.୧୧ ୦.୯୭ ବ୍ଦ ୦.୦୯ 2.041 0.044 Glucose (mg/dL) ୯୩.୦୪ ବ୍ଦ ୪୨.୦୫ ୮୪.୦୧ ବ୍ଦ ୨୬.୧୫ 0.429 0.668 TG (mg/dL) ୨୦୧.୨୭ ବ୍ଦ ୮୫.୪୫ ୧୨୮.୦୬ ବ୍ଦ ୨୮.୬୭ 6.203 <0.001 TC (mg/dL) ୨୧୯.୯୫ ବ୍ଦ ୫୦.୬୪ ୨୦୯.୫୨ ବ୍ଦ ୫୨.୨୩ 0.964 0.337 LDL-C (mg/dL) ୧୦୮.୨୫ ବ୍ଦ ୨୫.୬୫ ୧୦୪.୫୨ ବ୍ଦ ୩୭.୭୮ 0.533 0.595 HDL-C (mg/dL) ୩୪.୧୭ ବ୍ଦ ୮.୯୭ ୪୬.୮୨ ବ୍ଦ ୧୨.୯୨ −5.246 <0.001 NEFA (mmol/L) ୦.୯୭ ବ୍ଦ ୦.୧୯ ୦.୯୦ ବ୍ଦ ୦.୧୫ 1.641 0.101 AIP ୦.୪୦ ବ୍ଦ ୦.୧୮ ୦.୦୮ ବ୍ଦ ୦.୦୯ 8.223 <0.001 Abbreviations and notes: MetS—metabolic syndrome; non-MetS—no metabolic syndrome; WHR—waist-to-hip ratio; TG—triglycerides; TC—total cholesterol; LDL-C—low-density lipoprotein cholesterol; HDL-C—high-density lipoprotein cholesterol; NEFA—non-esterified fatty acids; AIP—atherogenic index of plasma. Student’s t-test was used for normally distributed variables, whereas the Mann–Whitney U test was applied for variables that did not meet the assumption of normality (TG, glucose, NEFA). A p-value < 0.05 was considered statistically significant. Table 4. Hardy–Weinberg equilibrium for the rs1544410 (BsmI) polymorphism of the VDR gene. Table 4. Hardy–Weinberg equilibrium for the rs1544410 (BsmI) polymorphism of the VDR gene. Group Genotype Observed (Expected) Allele Frequencies χ 2p MetS (n = 38) GG 20 (19.2) p(G) = 0.71; q(A) = 0.29 0.414 0.520 AG 14 (15.6) AA 4 (3.2) non-MetS (n = 57) GG 26 (25.3) p(G) = 0.67; q(A) = 0.33 0.158 0.691 AG 24 (25.3) AA 7 (6.3) Abbreviations and notes: MetS—metabolic syndrome; non-MetS—no metabolic syndrome. A p-value < 0.05 was considered statistically significant. Table 5. Distribution of rs1544410 BsmI polymorphism in MetS and non-MetS participants. Table 5. Distribution of rs1544410 BsmI polymorphism in MetS and non-MetS participants. Genotype/Allele MetS (n = 38) Non-MetS (n = 57) χ 2p Genotypes 0.450 0.451 GG–n (%) 20 (52.63) 26 (45.61) AG–n (%) 14 (36.84) 24 (42.11) AA–n (%) 4 (10.53) 7 (12.28) Alleles 0.406 0.524 G–n (%) 54 (71.05) 76 (66.67) A–n (%) 22 (28.95) 38 (33.33) Abbreviations and notes: MetS—metabolic syndrome; non-MetS—no metabolic syndrome. p-value < 0.05 was considered statistically significant. Table 6. Effects of metabolic syndrome status and rs1544410 (BsmI) VDR polymorphism on anthropometric and biochemical parameters: two-way ANOVA. Table 6. Effects of metabolic syndrome status and rs1544410 (BsmI) VDR polymorphism on anthropometric and biochemical parameters: two-way ANOVA. Parameter Group Genotype ANOVA GG (n = 46) AG (n = 38) AA (n = 11) Effect F p η 2Body weight (kg) MetS ୭୫.୭୦ ବ୍ଦ ୧୬.୩୩ ୮୧.୦୪ ବ୍ଦ ୧୫.୨୧ ୯୧.୨୦ ବ୍ଦ ୨.୮୩ MetS F(1,9) = 3.14 0.080 0.034 non-MetS ୭୬.୬୫ ବ୍ଦ ୧୬.୯୮ ୭୧.୩୦ ବ୍ଦ ୧୮.୨୨ ୭୭.୪୧ ବ୍ଦ ୧୪.୮୬ Genotype (BsmI) F(2,89) = 1.10 0.338 0.024 MetS × Genotype F(2,89) = 1.46 0.239 0.032 Height (cm) MetS ୧୬୬ ବ୍ଦ ୧୦ ୧୬୮ ବ୍ଦ ୯ ୧୭୧ ବ୍ଦ ୧୩ MetS F(1,89) = 0.62 0.433 0.007 non-MetS ୧୬୮ ବ୍ଦ ୯ ୧୬୪ ବ୍ଦ ୧୦ ୧୬୭ ବ୍ଦ ୧୩ Genotype (BsmI) F(2,89) = 0.36 0.697 0.008 MetS × Genotype F(2,89) = 0.83 0.438 0.018 BMI (kg/m 2) MetS ୨୭.୨୩ ବ୍ଦ ୪.୭୮ ୨୮.୬୮ ବ୍ଦ ୪.୫୨ ୩୧.୫୨ ବ୍ଦ ୪.୩୧ MetS F(1,89) = 1.95 0.167 0.021 non-MetS ୨୭.୨୧ ବ୍ଦ ୫.୭୫ ୨୬.୩୪ ବ୍ଦ ୬.୧୩ ୨୮.୦୨ ବ୍ଦ ୫.୯୧ Genotype (BsmI) F(2,89) = 0.92 0.403 0.020 MetS × Genotype F(2,89) = 0.68 0.511 0.015 Arm circumference (cm) MetS ୨୯.୧୦ ବ୍ଦ ୩.୫୮ ୩୦.୬୪ ବ୍ଦ ୩.୧୦ ୩୨.୦୦ ବ୍ଦ ୨.୯୪ MetS F(1,89) = 2.32 0.131 0.025 non-MetS ୨୮.୭୭ ବ୍ଦ ୩.୭୪ ୨୮.୨୯ ବ୍ଦ ୪.୭୧ ୩୦.୧୪ ବ୍ଦ ୩.୪୮ Genotype (BsmI) F(2,89) = 1.28 0.282 0.028 MetS × Genotype F(2,89) = 0.71 0.495 0.016 Thigh circumference (cm) MetS ୪୮.୧୦ ବ୍ଦ ୫.୪୮ ୫୦.୨୯ ବ୍ଦ ୬.୭୨ ୫୪.୫୦ ବ୍ଦ ୪.୨୦ MetS F(1,89) = 1.86 0.176 0.020 non-MetS ୫୦.୮୮ ବ୍ଦ ୬.୭୭ ୪୭.୭୫ ବ୍ଦ ୭.୦୯ ୪୭.୪୩ ବ୍ଦ ୬.୧୯ Genotype (BsmI) F(2,89) = 0.35 0.701 0.008 MetS × Genotype F(2,89) = 3.19 0.046 0.067 Waist circumference (cm) MetS ୧୦୧.୬୦ ବ୍ଦ ୧୩.୬୭ ୧୦୬.୨୮ ବ୍ଦ ୧୩.୭୧ ୧୧୫.୫୦ ବ୍ଦ ୫.୦୦ MetS F(1,89) = 3.78 0.055 0.041 non-MetS ୧୦୦.୬୫ ବ୍ଦ ୧୫.୮୦ ୯୭.୯୨ ବ୍ଦ ୧୬.୩୪ ୧୦୨.୪୩ ବ୍ଦ ୧୫.୩୫ Genotype (BsmI) F(2,89) = 1.15 0.320 0.025 MetS × Genotype F(2,89) = 1.01 0.368 0.022 Hip circumference (cm) MetS ୧୦୨.୧୦ ବ୍ଦ ୮.୬୭ ୧୦୫.୭୯ ବ୍ଦ ୬.୨୩ ୧୦୨.୫୦ ବ୍ଦ ୮.୧୯ MetS F(1,89) = 0.09 0.768 0.001 non-MetS ୧୦୪.୮୧ ବ୍ଦ ୧୦.୮୩ ୯୯.୮୩ ବ୍ଦ ୧୧.୯୨ ୧୦୩.୪୩ ବ୍ଦ ୧୧.୭୯ Genotype (BsmI) F(2,89) = 0.04 0.959 0.001 MetS × Genotype F(2,89) = 1.85 0.163 0.040 WHR (waist/hip ratio) MetS ୦.୯୯ ବ୍ଦ ୦.୦୯ ୧.୦୦ ବ୍ଦ ୦.୧୧ ୧.୧୩ ବ୍ଦ ୦.୧୩ MetS F(1,89) = 7.92 0.006 0.082 non-MetS ୦.୯୬ ବ୍ଦ ୦.୦୯ ୦.୯୮ ବ୍ଦ ୦.୦୯ ୦.୯୯ ବ୍ଦ ୦.୦୯ Genotype (BsmI) F(2,89) = 3.44 0.036 0.072 MetS × Genotype F(2,89) = 1.66 0.196 0.036 Glucose (mg/dL) MetS ୯୨.୮୨ ବ୍ଦ ୩୬.୦୪ ୮୪.୪୪ ବ୍ଦ ୩୭.୭୮ ୧୨୪.୨୩ ବ୍ଦ ୭୫.୮୪ MetS F(1,89) = 3.33 0.072 0.036 non-MetS ୮୭.୧୦ ବ୍ଦ ୨୮.୮୫ ୭୯.୪୧ ବ୍ଦ ୨୩.୬୨ ୮୮.୩୪ ବ୍ଦ ୨୫.୦୪ Genotype (BsmI) F(2,89) = 2.20 0.117 0.047 MetS × Genotype F(2,89) = 0.95 0.389 0.021 TG (mg/dL) MetS ୧୮୨.୪୫ ବ୍ଦ ୪୫.୭୧ ୧୮୧.୦୫ ବ୍ଦ ୫୭.୧୬ ୩୬୬.୧୪ ବ୍ଦ ୧୪୮.୯୧ MetS F(1,89) = 93.27 <0.001 0.512 non-MetS ୧୩୧.୨୭ ବ୍ଦ ୩୨.୯୫ ୧୨୫.୪୪ ବ୍ଦ ୨୫.୫୦ ୧୨୫.୦୮ ବ୍ଦ ୨୩.୯୨ Genotype (BsmI) F(2,89) = 16.96 <0.001 0.275 MetS × Genotype F(2,89) = 18.31 <0.001 0.291 TC (mg/dL) MetS ୨୦୮.୮୯ ବ୍ଦ ୩୪.୮୯ ୨୨୧.୬୫ ବ୍ଦ ୫୭.୪୪ ୨୬୯.୨୭ ବ୍ଦ ୭୪.୫୧ MetS F(1,89) = 3.79 0.055 0.041 non-MetS ୨୨୧.୬୬ ବ୍ଦ ୬୫.୫୩ ୧୯୭.୯୪ ବ୍ଦ ୩୮.୬୬ ୨୦୪.୨୦ ବ୍ଦ ୨୫.୨୮ Genotype (BsmI) F(2,89) = 1.11 0.331 0.025 MetS × Genotype F(2,89) = 2.97 0.056 0.063 LDL-C (mg/dL) MetS ୧୦୬.୪୧ ବ୍ଦ ୨୪.୪୦ ୧୧୧.୪୬ ବ୍ଦ ୨୬.୨୫ ୧୦୬.୩୦ ବ୍ଦ ୩୫.୭୯ MetS F(1,89) = 0.68 0.410 0.008 non-MetS ୧୧୧.୦୭ ବ୍ଦ ୪୩.୯୭ ୧୦୧.୬୮ ବ୍ଦ ୨୮.୨୬ ୮୯.୮୯ ବ୍ଦ ୪୧.୬୮ Genotype (BsmI) F(2,89) = 0.41 0.662 0.009 MetS × Genotype F(2,89) = 0.67 0.514 0.015 HDL-C (mg/dL) MetS ୩୪.୩୪ ବ୍ଦ ୮.୮୦ ୩୬.୦୦ ବ୍ଦ ୮.୯୫ ୨୬.୮୫ ବ୍ଦ ୮.୨୨ MetS F(1,89) = 23.70 <0.001 0.210 non-MetS ୪୮.୪୬ ବ୍ଦ ୧୫.୭୪ ୪୪.୯୧ ବ୍ଦ ୯.୧୦ ୪୭.୨୪ ବ୍ଦ ୧୩.୫୦ Genotype (BsmI) F(2,89) = 0.59 0.556 0.013 MetS × Genotype F(2,89) = 1.12 0.330 0.025 NEFA (mg/dL) MetS ୦.୯୩ ବ୍ଦ ୦.୧୫ ୦.୯୪ ବ୍ଦ ୦.୧୬ ୧.୨୪ ବ୍ଦ ୦.୩୨ MetS F(1,89) = 9.52 0.003 0.097 non-MetS ୦.୯୧ ବ୍ଦ ୦.୧୯ ୦.୮୯ ବ୍ଦ ୦.୧୦ ୦.୯୪ ବ୍ଦ ୦.୧୫ Genotype (BsmI) F(2,89) = 5.25 0.007 0.106 MetS × Genotype F(2,89) = 3.13 0.049 0.066 Abbreviations and notes: MetS—metabolic syndrome; non-MetS—no metabolic syndrome; BMI—body mass index; WHR—waist-to-hip ratio; TG—triglycerides; TC—total cholesterol; LDL-C—low-density lipoprotein cholesterol; HDL-C—high-density lipoprotein cholesterol; NEFA—non-esterified fatty acids. Two-way analysis of variance (ANOVA) was used to assess the effects of metabolic syndrome status (MetS vs. non-MetS), rs1544410 (BsmI) VDR genotype, and their interaction on the analyzed variables. F values represent main and interaction effects, and η 2 indicates partial eta squared (effect size). Statistical significance was set at p < 0.05. Table 7. Fisher’s LSD post hoc pairwise comparisons of rs1544410 (BsmI) genotype × metabolic syndrome status across metabolic and anthropometric parameters. Table 7. Fisher’s LSD post hoc pairwise comparisons of rs1544410 (BsmI) genotype × metabolic syndrome status across metabolic and anthropometric parameters. TG (mg/dL) GG MetS AG MetS AA MetS GG Non-MetS AG Non-MetS AA Non-MetS GG MetS - 0.931 <0.001 <0.001 <0.001 0.006 AG MetS - <0.001 0.002 <0.001 <0.001 AA MetS - <0.001 <0.001 <0.001 GG non-MetS - 0.660 0.756 AG non-MetS - 0.986 AA non-MetS - TC (mg/dL) GG MetS AG MetS AA MetS GG non-MetS AG non-MetS AA non-MetS GG MetS - 0.471 0.032 0.399 0.477 0.833 AG MetS - 0.101 0.999 0.167 0.458 AA MetS - 0.084 0.011 0.043 GG non-MetS - 0.102 0.420 AG non-MetS - 0.774 AA non-MetS - NEFA (mmol/L) GG MetS AG MetS AA MetS GG non-MetS AG non-MetS AA non-MetS GG MetS - 0.863 <0.001 0.580 0.375 0.921 AG MetS - 0.001 0.498 0.329 0.971 AA MetS - <0.001 <0.001 0.003 GG non-MetS - 0.712 0.625 AG non-MetS - 0.467 AA non-MetS - Thigh circumference (cm) GG MetS AG MetS AA MetS GG non-MetS AG non-MetS AA non-MetS GG MetS - 0.336 0.075 0.152 0.859 0.814 AG MetS - 0.255 0.781 0.248 0.344 AA MetS - 0.302 0.057 0.085 GG non-MetS - 0.091 0.214 AG non-MetS - 0.908 AA non-MetS - WHR (ratio) GG MetS AG MetS AA MetS GG non-MetS AG non-MetS AA non-MetS GG MetS - 0.740 0.008 0.221 0.607 0.931 AG MetS - 0.017 0.149 0.420 0.739 AA MetS - 0.001 0.003 0.017 GG non-MetS - 0.460 0.443 AG non-MetS - 0.784 AA non-MetS - Abbreviations and notes: MetS—metabolic syndrome; non-MetS—no metabolic syndrome; TG—triglycerides; TC—total cholesterol; NEFA—non-esterified fatty acids; WHR—waist-to-hip ratio. Values represent p-values from pairwise post hoc comparisons (LSD test) within a two-way ANOVA model including rs1544410 (BsmI) genotype and metabolic syndrome status (MetS vs. non-MetS). The table presents all pairwise genotype × metabolic status comparisons. Statistical significance was set at p < 0.05. Share and Cite MDPI and ACS Style Michniewicz, S.; Chmielowiec, K.; Gibas-Dorna, M.; Czyżniewski, B.; Pruszyńska-Oszmałek, E.; Kołodziejski, P.; Kowalski, M.T.; Grzywacz, A.; Chmielowiec, J. Association of the VDR rs1544410 Polymorphism with Metabolic Syndrome and Cardiometabolic Traits in Institutionalized Older Adults. Int. J. Mol. Sci. 2026, 27, 5212. https://doi.org/10.3390/ijms27125212 AMA Style Michniewicz S, Chmielowiec K, Gibas-Dorna M, Czyżniewski B, Pruszyńska-Oszmałek E, Kołodziejski P, Kowalski MT, Grzywacz A, Chmielowiec J. Association of the VDR rs1544410 Polymorphism with Metabolic Syndrome and Cardiometabolic Traits in Institutionalized Older Adults. International Journal of Molecular Sciences. 2026; 27(12):5212. https://doi.org/10.3390/ijms27125212 Chicago/Turabian Style Michniewicz, Szymon, Krzysztof Chmielowiec, Magdalena Gibas-Dorna, Bartłomiej Czyżniewski, Ewa Pruszyńska-Oszmałek, Paweł Kołodziejski, Michał Tomasz Kowalski, Anna Grzywacz, and Jolanta Chmielowiec. 2026. "Association of the VDR rs1544410 Polymorphism with Metabolic Syndrome and Cardiometabolic Traits in Institutionalized Older Adults" International Journal of Molecular Sciences 27, no. 12: 5212. https://doi.org/10.3390/ijms27125212 APA Style Michniewicz, S., Chmielowiec, K., Gibas-Dorna, M., Czyżniewski, B., Pruszyńska-Oszmałek, E., Kołodziejski, P., Kowalski, M. T., Grzywacz, A., & Chmielowiec, J. (2026). Association of the VDR rs1544410 Polymorphism with Metabolic Syndrome and Cardiometabolic Traits in Institutionalized Older Adults. 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