Nutritional education interventions on the components of metabolic syndrome in Bangladeshi adults

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Kamruzzaman, Md. Mamunur Roshid, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4948926/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in BMC Nutrition → Version 1 posted 4 You are reading this latest preprint version Abstract Metabolic syndrome (MetS) is a cluster of metabolic abnormalities that includes central obesity, hypertension, dyslipidemia, and disturbed glucose metabolism. To the best of our knowledge, no research in Bangladesh has evaluated the effect of nutritional interventions on MetS.The main objective was to explore the effects of nutritional interventions on participants with MetS. A cross-sectional study was carried out on 500 Bangladeshi adults (30 to 69 years; both males and females) who provided informed consent. Modified NCEP ATP III criteria for Asians were used to diagnose the subjects. This study revealed that the overall percentages of men and women with MetS were 59.6% and 59.1%, respectively.The present study revealed a 2.69 cm reduction ( p < 0.05) in waist circumference in females after 4 months of in-depth nutritional counseling and a 0.24 cm reduction after 4 months of single-intervention nutritional counseling. Similarly, a 2.64 cm reduction ( p > 0.05) in males after 4 months of in-depth nutritional counseling and a 1.57 cm increase after a single intervention of nutritional counseling were found to be significant ( p < 0.05). A 1.08 mmol/L decrease in FBG was found after in-depth nutritional counseling for 4 months, while no significant difference was detected after a single intervention. A 9.37 mg/dl increase in HDL-C was found ( p < 0.05) for females, but for males, the levels of HDL-C remained nearly the same in both intervention groups. A reduction in the MetS proportion was found in the intervention groups ( p 0.05), respectively, in the in-depth intervention group, whereas in the single intervention group, they were 50.0–32.3%, 41.9–35.5%, and 9.7–22.6%, respectively. Thus, community-based in-depth nutritional counseling reduced the proportion of individuals with MetS and significantly improved several metabolic parameters in Bangladeshi adults with MetS. Cardiovascular risk factors waist circumference reduction fasting blood glucose HDL-c improvement community-based intervention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Metabolic syndrome (MetS) represents a constellation of metabolic abnormalities including obesity, dyslipidemia, hypertension, and insulin resistance, which collectively increase the risk of cardiovascular diseases and type 2 diabetes mellitus[ 1 – 3 ]. The prevalence of MetS has reached epidemic proportions worldwide, posing a significant public health challenge[ 4 ]. While genetic predisposition plays a role, the rapid surge in MetS cases is largely attributed to lifestyle factors, particularly dietary habits and physical inactivity[ 5 – 7 ]. Dietary patterns have a profound impact on the development and progression of MetS[ 8 , 9 ]. Studies have consistently shown that diets high in saturated fats, trans fats, and refined carbohydrates contribute to metabolic dysfunction, while diets rich in fruits, vegetables, whole grains, and lean protein sources exert protective effects against MetS[ 6 , 10 , 11 ]. Moreover, specific dietary behaviors, such as eating speed, frequency of dining out, meal timing, and breakfast skipping, have been implicated in the pathogenesis of MetS[ 12 – 14 ]. In Bangladesh, a country undergoing rapid urbanization and dietary transition, the prevalence of MetS is increasing [ 15 , 16 ]. Traditional dietary patterns characterized by rice, fish, and vegetables are being replaced by energy-dense, nutrient-poor foods high in saturated fats, sugars, and refined carbohydrates[ 17 , 18 ]. This dietary shift, coupled with sedentary lifestyles, has contributed to the burgeoning MetS epidemic in Bangladesh[ 19 ]. Nutrition education interventions represent a promising approach for addressing MetS by promoting healthy dietary behaviors and lifestyle modifications[ 20 , 21 ]. These interventions aim to enhance individuals' knowledge, attitudes, and practices related to diet and nutrition, ultimately empowering them to make informed food choices[ 22 – 24 ]. Culturally tailored nutrition education programs are essential in the Bangladeshi context to accommodate traditional dietary practices while promoting healthier alternatives[ 25 , 26 ]. The effectiveness of nutrition education interventions in modifying dietary behaviors and improving metabolic outcomes among Bangladeshi adults remains understudied[ 27 – 29 ]. Research is needed to evaluate the impact of such interventions on MetS incidence, incidence, and management in Bangladesh. Additionally, identifying the barriers to and facilitators of dietary behavior change in this population is crucial for the development of effective and sustainable intervention strategies. Furthermore, the role of specific nutrients and dietary components in the prevention and management of MetS warrants investigation. Studies have shown that diets high in protein, particularly plant protein, may have beneficial effects on blood pressure, lipid profiles, and insulin sensitivity [ 1 , 30 ]. Similarly, the type and quality of dietary fats, carbohydrates, fiber, and micronutrients may influence metabolic health outcomes [ 31 ]. Nutrition education interventions hold great promise for mitigating the burden of MetS in Bangladesh and other settings experiencing similar dietary transitions[ 32 – 34 ]. By promoting healthy dietary behaviors and empowering individuals to make informed food choices, these interventions have the potential to reduce the prevalence of MetS and its associated complications, ultimately improving public health outcomes. However, further research is needed to evaluate the effectiveness and scalability of such interventions in diverse populations and settings. Materials and Methods Design and samples The current research adopted a randomized longitudinal approach, utilizing a probability sampling technique to select participants. Individuals were recruited from various locations and underwent registration, consenting, and eligibility screening before providing blood samples and data. This study focused on Rajshahi city, Kushtiasadar, and Pabna Sadar and targeted morning walkers from these areas (Fig. 1 ). Participants were interviewed and screened randomly to evaluate determinants and diagnose components of metabolic syndrome. Participants eligible for the baseline study were adults aged 30 to 69 years who participated in morning walks, while those excluded were individuals who had recently undergone surgery, who had a history of heart attack or stroke, who were pregnant or postpartum women, and who were outside the specified age range. The sample size calculation was based on the prevalence reported in a previous systematic review, resulting in an estimated sample size of 359 at a 5% significance level with a precision of 0.05. Initially, 500 adults were enrolled, including 300 from Rajshahi city, 150 from Pabna Sadar, and 150 from Kushtiasadar, with both genders represented (297 males and 203 females). Collection of data Initially, 560 participants were approached, and after applying the criteria, a final cohort of 500 participants was screened at specified locations. Fasting blood samples were collected and assessed based on modified NCEP ATP III criteria (2005) [ 35 ]at three centers in Bangladesh, namely, the Plasma Diagnostic Center in Rajshahi, the Padma Diagnostic Centre in Pabna, and the Applied Nutrition and Food Technology Laboratory at Islamic University in Kushtia, Bangladesh.A meticulously crafted close-ended questionnaire was used to collect data encompassing sociodemographic details, lifestyle habits, physical activity, anthropometric measurements, clinical information, and knowledge about MetS and fats. Blood pressure (BP) was measured in a standardized, calm, and comfortable setting, with individuals seated quietly and their armssupported. Specific instructions for accurate electronic BP measurements were followed [ 36 , 37 ]. Body height was measured using Seca stadiometers, and weight was measured with an Omron scale. Height and weight were recorded to the nearest 0.1 cm and 0.1 kg, respectively. The waist circumference, measured at the midpoint between the 12th rib and iliac crest, was rounded to the nearest 0.1 cm [ 38 ]. Fasting blood samples were collected following the WHO Guidelines on Drawing Blood [ 39 ], and the serum glucose, triglyceride, and high-density lipoprotein (HDL) cholesterol levels were assessed [ 40 ]. The samples were transported in a cold chain and centrifuged, and the resulting serum was stored at -80°C until analysis. Serum triglyceride and high-density lipoprotein (HDL)-cholesterol levels were determined using an AE-600F biochemistry analyzer (Emra)[ 41 ].Blood glucose levels were measured using an ACCU-CHEK glucometer by applying a drop of blood onto a chemically treated test strip [ 42 ]. Serum/plasma triglycerides were enzymatically measured through hydrolysis, generating glycerol, which underwent oxidation. The absorbance was quantified at 500 nm following the method of Klotzsch and McNamara [ 43 ]. HDL-C was directly measured in serum using the method byHirano et al .[ 44 ]. Nutritional Intervention A four-month community-based intervention program (December 2020–March 2021) targeted adults aged 30–69 years with metabolic syndrome from two districts, sadars, and one city corporation commune in Bangladesh. Participants provided written informed consent before entry into the trial. The inclusion criteria included age 30 to 69 years and a diagnosis of MetS, with both sexes included, while the exclusion criteria excluded those taking certain medications or receiving counseling elsewhere. Subjects were randomized into intervention and control groups, with 36 in the intervention group and 33 in the control group. The intervention group received three individual in-depth nutrition counseling sessions, while the control group received one session of group counseling. Postintervention assessments were completed by 34 intervention participants (91.8% response rate) and 31 control participants (93.9% response rate). Nutrition counseling incorporated tailored messages, including "10 healthy habits for adults," and considered urban morning walkers' cultural values, favoring free-flowing communication through initial group sessions guided by trained nutritionists, each lasting approximately 60 minutes. Statistical analysis The statistical analysis utilized the Statistical Package for the Social Sciences (SPSS) software version 25.0 [ 45 ]. Descriptive analysis was employed to assess the demographic and clinical characteristics of the participants. Descriptive statistics were computed for each variable entered, with continuous variables presented as the means accompanied by standard deviations (SDs).For categorical variables, frequencies and percentages were computed. Chi-square tests were used to examine associations between categorical variables, and independent sample t tests were used to assess mean differences among continuous variables concerning the presence of MetS and its components. Logistic regression, utilizing the enter method, determined associations between determinants and MetS, with binary logistic regression calculating odds ratios and 95% confidence intervals, adjusting for confounding factors. Statistical significance was set at P < 0.05. Results Sociodemographic information The study included 500 morning walking adults, 59.4% of whom were male and 40.6% of whom were female. The mean ages of the males and females were 44.97 ± 9.31 and 43.33 ± 9.02 years, respectively. Most hailed from Rajshahi city (54.6% male, 45.3% female), which was a statistically significant difference ( P <0.05). Education levels varied significantly between genders ( P <0.05), with proportions ranging from 28.9% to 79.2% for males and 20.8% to 71.1% for females across different levels. Professional categories also showed significant gender disparities ( P <0.05), with varying proportions across service holders, unemployed individuals, laborers, homemakers, business owners, and others for both males and females (Table 1). Table 1 Socio-demographic characteristics of study participants Variables Male Female P value n (%) 297 (59.4) 203 (40.6) Age (years) 44.97 ± 9.31 43.33 ± 9.02 0.299 Area, n (%) Kushtia (sadar) 57 (57.0) 43 (43.0) 0.001* Pabna (sadar) 76 (76) 24 (24) Rajshahi (city) 164 (54.6) 136 (45.3) Education, n (%) No formal education 13 (28.9) 32 (71.1) 0.000* Primary 50 (41.7) 70 (58.3) Secondary 44 (57.1) 33 (42.9) Higher Secondary 49 (61.3) 31 (38.8) Graduation 141 (79.2) 37 (20.8) Occupation, n (%) Employed 175 (86.6) 27 (13.4 0.000* Unemployed 12 (85.7) 2 (14.3) Homemakers 0 (0.0) 160 (100) Laborer 17 (73.9) 6 (26.1) Business 81 (93.1) 6 (6.9) Others 12 (85.7) 2 (14.3) *Significant at P< 0.05. The data are presented as the means ± SDs. The values are presented as the number of participants (percentages). Differences were analyzed by independent samples t tests for continuous variables and chi-square tests for categorical variables. Prevalence and age-stratified distribution of MetS and Non-MetS in both sexes This research revealed that the overall prevalence of MetS was 59.6% in males and 59.1% in females. Among male participants, the third age group (50-59 years) had the highest MetS rate at 35.5%, while the fourth age group (60-69 years) had the lowest at 6.2%. Among females, 45.8% of those in the second age group (40-49 years) had the highest MetS percentage,and 5.8% of those in the fourth age group had the lowest percentage(Fig. 2). Comparison of nutritional knowledge between MetS and non-MetS participants There was a significant difference ( P 0.05) was observed. In males, similar proportions had knowledge about MetS (10.7% MetS, 15.0% non-MetS), while in females, the percentages were 6.7% for MetS and 22.9% for non-MetS. This trend persisted for knowledge about fats. Overall, approximately 80% of both male and female participants lacked knowledge about MetS and fats (Table 2). Table 2 Comparison of nutritional knowledge between MetS and non-MetS participants Variables Male Female Answer MetS Non-MetS P value MetS Non-MetS P value MetS Knowledge Yes 19 (10.7%) 18 (15.0%) 0.275 8 (6.7%) 19 (22.9%) 0.001* No 158 (89.3%) 102 (85.0%) 112 (93.3%) 64 (77.1%) Knowledge about Fat Yes 34 (19.2%) 17 (14.2%) 0.258 16 (13.3%) 24 (28.9%) 0.006* No 143 (80.8%) 103 (85.8%) 104 (86.7%) 59 (71.1%) *Significant at P <0.05. Percentages were analyzed by the chi-square test. Intervention Baseline characteristics of the intervention subjects Both genders were included in the intervention, with 52.9% male and 47.1% female in the in-depth intervention group and 45.2% male and 54.8% female in the single intervention group. The mean age in the in-depth intervention group was 45.35 ± 8.03 years, which was slightly greater than that in the single intervention group (mean age of 40.96 ± 7.18 years). Higher secondary education was more prevalent in the in-depth intervention group (35.3%) than in the single intervention group (19.3%), while a greater proportion of subjects in the single intervention group held graduate degrees (32.2% vs. 17.6% in the in-depth intervention group). The distribution of occupations was mostly similar between the two intervention groups, except for the business category, which was greater in the single intervention group (23.5%) than in the in-depth intervention group (9.6%) (Table 3). Table 3 Baseline characteristics of the intervention subjects Characteristics In-depth intervention (n=34) (n%) Single intervention (n=31) (n%) Gender Male 18 (52.9) 14 (45.2) Female 16 (47.1) 17 (54.8) Age 45.35 ± 8.03 40.96 ± 7.18 Marital Status Married 31 (92.2) 31 (100) Widower 3 (8.8) Education Level Graduation 6 (17.6) 10 (32.2) Higher Secondary 12 (35.3) 6 (19.3) Secondary 4 (11.8) 6 (19.3) Primary 9 (26.5) 5 (9.6) No formal Education 3 (8.8) 4 (12.9) Occupation Employed 12 (35.5) 13 (41.9) Housewife 12 (35.5) 14 (45.1) Business 8 (23.5) 3 (9.6) Laborer 2 (5.9) 1 (3.2) Percentages were analyzed by the chi-square test. Comparison of clinical indices at baseline and after 4 months of intervention A significant difference ( P <0.05) in systolic blood pressure was detected between baseline and after both the in-depth and single interventions. However, for diastolic blood pressure, a significant difference ( P <0.05) was found only between baseline and the in-depth intervention group (Table 4). Table 4 Comparison of clinical indices at baseline and after 4 months of intervention Parameters In-depth Intervention P value Single Intervention P value Baseline In-depth Intervention Baseline Single Intervention SBP (mmHg) 127.35 ±14.71 120.88 ± 8.11 0.004* 122.09 ± 16.10 130.96 ± 15.07 0.000* DBP (mmHg) 84.23 ± 7.18 78.11 ± 5.89 0.000* 80.48 ± 8.12 82.90 ± 7.61 0.226 *The statistical significance level was set at P <0.05. The data are presented as the means ± SDs, and differences were analyzed using paired samples t tests. Comparison of biochemical indices at baseline and after 4 months of intervention Significant differences ( P 0.05) were noted between baseline and the single intervention group for these biochemical indices (Fig. 3). Comparison of gender-stratified HDL-C at baseline and after 4 months of intervention For males, no significant difference ( P >0.05) was observed in either the in-depth or single intervention. However, in females, a significant difference ( P <0.05) was found in the in-depth intervention study, while no significant difference was noted in the single intervention (Fig. 4). Comparison of gender-stratified WC at baseline and after 4 months of intervention In the first group, baseline scores were 93.25% for males and 88.25% for females, which decreased to 90.61% and 85.56%, respectively, after an in-depth intervention. On the other hand, in the second group, the baseline scores were higher, with 96.85% for males and 94.29% for females. Following a single intervention, the male score increased to 98.42%, while the female score slightly decreased to 94.05%. For females, a significant difference ( p 0.05) was found between the in-depth and single intervention studies(Fig. 5). Changes in MetS components at baseline and after 4 months of in-depth and single intervention At baseline, the prevalence of MetS was 0%, 0%, 44.1%, 47.1%, and 3.8% for categories 1, 2, 3, 4, and 5 (load of MetS), respectively. After completing the in-depth intervention period, these values changed to 14.7%, 44.1%, 23.5%, 11.8%, and 5.9%, respectively. In the single intervention group, the baseline prevalence of MetS was 0%, 0%, 48.4%, 41.9%, and 9.7% for categories 1, 2, 3, 4, and 5, respectively. Upon completion of the single intervention period, the prevalence shifted to 0%, 9.7%, 32.3%, 35.5%, and 22.6% for categories 1, 2, 3, 4, and 5, respectively (Table 5). Table 5 Changes in MetS components at baseline and after 4 months of intervention No of MetS components In-depth intervention p value Single intervention p value Baseline In-depth Baseline Single 1 0 (0) 5 (14.7%) 0.004 0 (0) 0 (0) 0.241 2 0 (0) 15 (44.1%) 0 (0) 3 (9.7%) 3 15 (44.1%) 8 (23.5%) 15 (48.4%) 10 (32.2%) 4 16 (47.1%) 4 (11.8%) 13 (41.9%) 11 (35.5%) 5 3 (8.8%) 2 (5.9% 3 (9.7%) 7 (22.6%) The P value was ascertained by the chi-square test. Discussion The study on nutrition education interventions among Bangladeshi adults who participate in morning walks provides notable demographic insights. The participant pool consisted of a greater proportion of males than females, with both groups having a mean age in their mid-forties. A significant finding is the difference in the geographical distribution of participants from Rajshahi city, where more males than females were represented. A previous study focused on the associations among nutrition literacy, demographics, and personal beliefs among Bangladeshi adults and revealed that occupation, income, education level, nutrition-related education, and perceived need for nutritional information significantly influence nutrition literacy scores [ 27 ]. Education levels between genders also varied significantly, indicating disparities in educational attainment, which could influence health literacy and receptiveness to nutrition education [ 46 ]. Furthermore, professional categories showed significant gender disparities, with males and females distributed across different occupations, such as service holders, unemployed individuals, laborers, homemakers, and business owners [ 47 , 48 ]. These differences underscore the necessity for tailored nutrition education interventions that account for gender-specific factors [ 49 , 50 ]. Research has indicated that the incidence of metabolic syndrome (MetS) is similar for both males and females. In males, the highest rate of MetS was observed in those in their 50s, while the lowest rate was in those in their 60s. For females, the highest prevalence occurred in those in their 40s, with the lowest in those in their 60s. These findings suggest that MetS is more common in middle-aged adults, particularly those in their forties and fifties, with a notable decrease in prevalence among older age groups of both genders [ 51 – 53 ]. The current research revealed a significant difference in nutritional knowledge regarding MetS and fats between female participants with and without MetS, whereas no such difference was observed among males. In males, knowledge about MetS was similar between those with and without this condition. However, in females, those without MetS demonstrated notably greater awareness of MetS than did those with MetS[ 54 , 55 ]. This trend was consistent for knowledge about fats. Overall, the majority of both male and female participants lacked sufficient knowledge about MetS and fats, highlighting a general gap in nutritional awareness within the study population. However, a study from Ethiopia revealed the significant impact of men's nutritional knowledge on household dietary diversity, suggesting the enhanced integration of men in nutrition-sensitive agricultural interventions to bolster overall household nutrition outcomes in low-income rural settings [ 56 ]. Nutrition education is crucial for improving dietary practices and overall diet quality, as evidenced by this study assessing knowledge, attitudes, and practices (KAPs) related to nutrition principles in urban and rural households of West Azerbaijan Province, Iran [ 57 ]. Studies on Arab countries such as Egypt, Syria, Saudi Arabia, and Jordan underscore the widespread inadequate nutritional knowledge (73.1%) and identify key demographic predictors, emphasizing the critical need for targeted educational interventions to improve dietary habits and health outcomes in these populations [ 58 ]. The study included both genders in the intervention, with a slightly greater proportion of males in the in-depth intervention group and a greater proportion of females in the single intervention group. Participants in the in-depth intervention group were, on average, older than those in the single intervention group. Education levels varied between the groups, with higher secondary education being more common among those in the in-depth intervention group, while a greater proportion of participants in the single intervention group held graduate degrees [ 23 , 59 ]. The distribution of occupations was generally similar across both intervention groups, with the exception of the business category, which was more represented in the single intervention than in the in-depth intervention [ 60 , 61 ]. These differences highlight the varying demographic characteristics that could influence the effectiveness and approach of the interventions [ 62 , 63 ]. The study's comparison of clinical indices at baseline and after four months of intervention revealed significant changes in systolic blood pressure (SBP) for both the in-depth and single intervention groups. Both groups showed a significant reduction in SBP from baseline to after the intervention. However, diastolic blood pressure (DBP) decreased significantly only in the in-depth intervention group, with no significant change observed in the single intervention group. These findings suggest that while both interventions were effective in reducing systolic blood pressure, the in-depth intervention had a more pronounced effect on lowering diastolic blood pressure than the single intervention [ 64 – 66 ]. This indicates that in-depth intervention may offer greater benefits for managing certain aspects of blood pressure [ 67 ]. A comparison of biochemical indices at baseline and after four months of intervention revealed significant improvements in fasting blood glucose (FBG) and triglyceride (TG) levels in the in-depth intervention group. These indices significantly decreased from their baseline values after the intervention. In contrast, the single intervention group did not exhibit any significant changes in FBG or TG levels over the same period. This suggests that the in-depth intervention was more effective in improving key biochemical markers associated with metabolic health, whereas the single intervention did not produce substantial changes in these indices [ 68 , 69 ]. A comparison of sex-stratified HDL cholesterol (HDL-C) levels at baseline and after four months of intervention revealed that males did not experience significant changes in HDL-C in either the in-depth or single intervention groups. However, females showed a significant improvement in HDL-C levels in the in-depth intervention group, while no significant changes were observed in the single intervention group. This indicates that the in-depth intervention was particularly effective in enhancing HDL-C levels among female participants, whereas neither intervention had a notable impact on HDL-C levels in males [ 70 , 71 ]. A comparison of sex-stratified waist circumference (WC) at baseline and after four months of intervention revealed different outcomes for males and females. In the in-depth intervention group, both males and females experienced a reduction in WC, with females showing a significant decrease. In contrast, in the single intervention group, WC increased for males, while WC decreased slightly for females, although the difference was not significant. These results indicate that the in-depth intervention was effective in significantly reducing WC in females but not in males. On the other hand, a single intervention was not effective for either sex, with males even experiencing an increase in WC [ 72 – 74 ]. This study examined changes in MetS components following two different intervention approaches over a four-month period. Initially, categories reflecting higher MetS risk levels showed the highest prevalence across both intervention groups, with lower-risk categories exhibiting lower prevalence. After the in-depth intervention, there was an observable reduction in MetS incidence across several categories, particularly those initially categorized as highrisk. This suggests that in-depth intervention may effectively mitigate MetS risk factors in these groups [ 75 – 77 ]. Conversely, the single intervention showed varied outcomes, with some categories showing reductions in MetS incidence, while others experienced increases. These results underscore the potential effectiveness of tailored, comprehensive interventions in targeting and improving specific MetS components compared to more generalized approaches [ 6 , 78 ]. Conclusions Both in-depth and single nutritional counseling interventions have significant implications for improving metabolic parameters among adults with metabolic syndrome (MetS). The interventions resulted in notable reductions in waist circumference and improvements in fasting blood glucose levels and HDL-C among participants, particularly females. These findings underscore the effectiveness of community-based nutrition education in managing MetS components and advocate for the integration of such interventions into public health strategies aimed at reducing cardiovascular risk factors in Bangladeshi adults. Further research and broader implementation of tailored nutritional interventions are warranted to optimize health outcomes and mitigate the burden of MetS in this population. Abbreviations MetS Metabolic Syndrome FBG Fasting Blood Glucose HDL High-Density Lipoprotein BP Blood Pressure SPSS Statistical Package for the Social Sciences SD Standard Deviations TG Triglyceride SBP Systolic Blood Pressure DBP Diastolic Blood Pressure WC waist circumference Declarations Acknowledgements The author would like to thank Department of Applied Nutrition and Food Technology, Islamic University, Kushtia, Bangladesh for the laboratory and logistic support. Informed consent/assent form Written informed consent were taken from the participants of the current study. Authors’ contribution SA and BSA conceptualized, prepared, and revised the paper; MK and MMRwrote the paper’s content and collected and analyzed the data; BAA visualize the data and prepare the map. SA, BSA, MK, MMR and BAA reviewed the final manuscript and approved its submission. SA had the final responsibility of submitting the manuscript. Funding No specific grant was given for this research by public, private, or nonprofit funding organizations. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author. Ethics approval and consent to participate No ethical approval was required to conduct the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis. 2017;11:215–25. Lee L, Sanders RA. Metabolic syndrome. Pediatr Rev. 2012;33:459–68. O’Neill S, O’Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev. 2015;16:1–12. Orsini F, D’Ambrosio F, Scardigno A, Ricciardi R, Calabrò GE. 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Gender- and age-specific prevalence of metabolic syndrome among Korean adults. J Cardiovasc Nurs. 2015;30:256–66. Nouri-Keshtkar M, Shojaei Shahrokhabadi M, Ghaheri A, Hosseini R, Ketabi H, Farjam M, et al. Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling. Sci Rep. 2023;13:16007. Mehata S, Shrestha N, Mehta RK, Bista B, Pandey AR, Mishra SR. Prevalence of the Metabolic Syndrome and its determinants among Nepalese adults: Findings from a nationally representative cross-sectional study. Sci Rep. 2018;8:14995. Ambikapathi R, Passarelli S, Madzorera I, Canavan CR, Noor RA, Abdelmenan S, et al. Men’s nutrition knowledge is important for women’s and children’s nutrition in Ethiopia. Matern Child Nutr. 2021;17:e13062. Saeidlou SN, Babaei F, Ayremlou P. Nutritional knowledge, attitude and practice of north west households in Iran: Is knowledge likely to become practice? Maedica (Buchar). 2016;11:286–95. Bany-yasin H, Elmor AA, Ebrahim BK, Ahmed AAM, Alarachi MR, Abedalqader L, et al. Exploration of the nutrition knowledge among general population: multi—national study in Arab countries. BMC Public Health. 2023;23:1178. Egg S, Wakolbinger M, Reisser A, Schätzer M, Wild B, Rust P. Relationship between nutrition knowledge, education and other determinants of food intake and lifestyle habits among adolescents from urban and rural secondary schools in Tyrol, Western Austria. Public Health Nutr. 2020;23:3136–47. Casu L, Gillespie S, Nisbett N. Integrating nutrition and physical activity promotion: A scoping review. PLoS One. 2020;15:e0233908. Byrd-Bredbenner C, Wu F, Spaccarotella K, Quick V, Martin-Biggers J, Zhang Y. Systematic review of control groups in nutrition education intervention research. Int J Behav Nutr Phys Act. 2017;14:91. Deng W-J, Yi Z, Lee JC-K. The demographic variation in nutrition knowledge and relationship with eating attitudes among Chinese university students. Int J Environ Res Public Health. 2024;21:159. Parmenter K, Waller J, Wardle J. Demographic variation in nutrition knowledge in England. Health Educ Res. 2000;15:163–74. Jafar TH, Tan NC, Shirore RM, Allen JC, Finkelstein EA, Hwang SW, et al. Integration of a multicomponent intervention for hypertension into primary healthcare services in Singapore—A cluster randomized controlled trial. PLOS Med. 2022;19:e1004026. Li W, Liu H, Wang X, Liu J, Xiao H, Wang C, et al. Interventions for reducing blood pressure in prehypertension: A meta-analysis. Front Public Heal. 2023;11:1139617. Wang J-G, Staessen JA, Franklin SS, Fagard R, Gueyffier F. Systolic and diastolic blood pressure lowering as determinants of cardiovascular outcome. Hypertension. 2005;45:907–13. Rai T, Morton K, Roman C, Doogue R, Rice C, Williams M, et al. Optimizing a digital intervention for managing blood pressure in stroke patients using a diverse sample: Integrating the person‐based approach and patient and public involvement. Heal Expect. 2021;24:327–40. Resaland GK, Aadland E, Nilsen AKO, Bartholomew JB, Andersen LB, Anderssen SA. The effect of a two‐year school‐based daily physical activity intervention on a clustered CVD risk factor score—The Sogndal school‐intervention study. Scand J Med Sci Sports. 2018;28:1027–35. Xiao Y-L, Gong Y, Qi Y-J, Shao Z-M, Jiang Y-Z. Effects of dietary intervention on human diseases: molecular mechanisms and therapeutic potential. Signal Transduct Target Ther. 2024;9:59. Dudum R, Juraschek SP, Appel LJ. Dose-dependent effects of lifestyle interventions on blood lipid levels: Results from the PREMIER trial. Patient Educ Couns. 2019;102:1882–91. Zakai NA, Minnier J, Safford MM, Koh I, Irvin MR, Fazio S, et al. Race-dependent association of high-density lipoprotein cholesterol levels with incident coronary artery disease. J Am Coll Cardiol. 2022;80:2104–15. Sharkey T, Whatnall MC, Hutchesson MJ, Haslam RL, Bezzina A, Collins CE, et al. Effectiveness of gender-targeted versus gender-neutral interventions aimed at improving dietary intake, physical activity and/or overweight/obesity in young adults (aged 17–35 years): a systematic review and meta-analysis. Nutr J. 2020;19:78. Chen X, Jiang S, Yao Y. Association between obesity and urinary incontinence in older adults from multiple nationwide longitudinal cohorts. Commun Med. 2023;3:142. Seo D-C, Choe S, Torabi MR. Is waist circumference ≥ 102/88 cm better than body mass index ≥ 30 to predict hypertension and diabetes development regardless of gender, age group, and race/ethnicity? Meta-analysis. Prev Med (Baltim). 2017;97:100–8. Dobrowolski P, Prejbisz A, Kuryłowicz A, Baska A, Burchardt P, Chlebus K, et al. Metabolic syndrome – a new definition and management guidelines. Arch Med Sci. 2022;18:1133–56. Ali N, Samadder M, Shourove JH, Taher A, Islam F. Prevalence and factors associated with metabolic syndrome in university students and academic staff in Bangladesh. Sci Rep. 2023;13:19912. Wang H, Dai Y, Huang S, Rong S, Qi Y, Li B. A new perspective on special effective interventions for metabolic syndrome risk factors: a systematic review and meta-analysis. Front Public Heal. 2023;11:1133614. Kim K-B, Choe H, Sung H. Effects of individualized exercise on risk factors of metabolic syndrome: A scoping review. J Obes Metab Syndr. 2024;33:20–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in BMC Nutrition → Version 1 posted Editorial decision: Revision requested 11 Sep, 2024 Editor assigned by journal 23 Aug, 2024 Submission checks completed at journal 21 Aug, 2024 First submitted to journal 21 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4948926","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":355341889,"identity":"49a7ed57-4792-4653-bdb4-9d41da44b556","order_by":0,"name":"Shammy Akter","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACCTBpw8DAfICBIQHM4YEK4teSxsDAlkyalsMQLQzEaJGf3fx008228/LmbPwHGB62McjzN/AevIFPi8GdY2a3c9tuG+5sY2ZgSGxjMJxxgC/ZAq8WiQSwFsYN95vBWhg3MPCY4XfYjPRvQC3n7Dccg9hiT1ALw40ckC0HEmFaEglqMbiRU3Y751xyMlCLwYGEcxLJMw4T8AvQYdtu55TZ2W44xvjw4Y8yG9v+9l78IQYGjGwQ+gA4mpgJqgeBP0SpGgWjYBSMgpEKAF+qSQ0Yjy3kAAAAAElFTkSuQmCC","orcid":"","institution":"Islamic University","correspondingAuthor":true,"prefix":"","firstName":"Shammy","middleName":"","lastName":"Akter","suffix":""},{"id":355341891,"identity":"96c6a982-5ef1-47f5-88e0-cc6d94862ffa","order_by":1,"name":"Bably Sabina Azhar","email":"","orcid":"","institution":"Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Bably","middleName":"Sabina","lastName":"Azhar","suffix":""},{"id":355341892,"identity":"e6338a74-c6ab-4957-aeb9-8100fdf3c58b","order_by":2,"name":"Md. Kamruzzaman","email":"","orcid":"","institution":"Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"","lastName":"Kamruzzaman","suffix":""},{"id":355341894,"identity":"2cffb242-40ca-44d9-b4e4-0c5f0712e782","order_by":3,"name":"Md. Mamunur Roshid","email":"","orcid":"","institution":"Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Mamunur","lastName":"Roshid","suffix":""},{"id":355341895,"identity":"cca026b5-f261-4506-a8bb-efd1975ca687","order_by":4,"name":"Bose Alvin","email":"","orcid":"","institution":"Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Bose","middleName":"","lastName":"Alvin","suffix":""}],"badges":[],"createdAt":"2024-08-21 05:57:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4948926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4948926/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40795-025-01121-2","type":"published","date":"2025-10-14T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66666795,"identity":"1b53985e-997f-4e5c-9728-a7f46352d5a4","added_by":"auto","created_at":"2024-10-15 09:39:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":571039,"visible":true,"origin":"","legend":"\u003cp\u003eThe study area from which the samples were collected for investigation\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4948926/v1/dbbdd51e9c1c528ef2d33c34.png"},{"id":66666793,"identity":"e53da02f-a283-4c6c-8d7c-0105df86a544","added_by":"auto","created_at":"2024-10-15 09:39:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500796,"visible":true,"origin":"","legend":"\u003cp\u003eAge-stratified distribution of metabolic syndrome (MetS) and non-MetS among (A) males and (B) females\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4948926/v1/96f49b9db63a4439ad414b49.png"},{"id":66666797,"identity":"e317ff17-95b6-45fc-b136-2655d7ef1477","added_by":"auto","created_at":"2024-10-15 09:39:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152162,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of TG at baseline and after 4 months of in-depth and single intervention\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4948926/v1/2fee1f12862ddacbc14d1eeb.png"},{"id":66667275,"identity":"ee9c7b44-a5ae-4d0d-a4d7-1a105165c5ff","added_by":"auto","created_at":"2024-10-15 09:47:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96876,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of gender stratified HDL levels at baseline and after 4 months of in-depth and single interventions\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4948926/v1/09750d88fcf805804ca2a69f.png"},{"id":66666796,"identity":"690da201-724a-4404-b44b-96ceb34e98eb","added_by":"auto","created_at":"2024-10-15 09:39:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91910,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of gender-stratified WC after 4 months of an in-depth and single intervention\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4948926/v1/b4c2b58e4b8d27cbc7f80703.png"},{"id":93956113,"identity":"5d7f6ec5-583d-4174-b7ab-4dcf9cd9a2ca","added_by":"auto","created_at":"2025-10-20 16:10:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2687335,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4948926/v1/dba660e5-705d-4005-8351-2b2da7474841.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nutritional education interventions on the components of metabolic syndrome in Bangladeshi adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) represents a constellation of metabolic abnormalities including obesity, dyslipidemia, hypertension, and insulin resistance, which collectively increase the risk of cardiovascular diseases and type 2 diabetes mellitus[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The prevalence of MetS has reached epidemic proportions worldwide, posing a significant public health challenge[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While genetic predisposition plays a role, the rapid surge in MetS cases is largely attributed to lifestyle factors, particularly dietary habits and physical inactivity[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDietary patterns have a profound impact on the development and progression of MetS[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Studies have consistently shown that diets high in saturated fats, trans fats, and refined carbohydrates contribute to metabolic dysfunction, while diets rich in fruits, vegetables, whole grains, and lean protein sources exert protective effects against MetS[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, specific dietary behaviors, such as eating speed, frequency of dining out, meal timing, and breakfast skipping, have been implicated in the pathogenesis of MetS[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Bangladesh, a country undergoing rapid urbanization and dietary transition, the prevalence of MetS is increasing [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Traditional dietary patterns characterized by rice, fish, and vegetables are being replaced by energy-dense, nutrient-poor foods high in saturated fats, sugars, and refined carbohydrates[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This dietary shift, coupled with sedentary lifestyles, has contributed to the burgeoning MetS epidemic in Bangladesh[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNutrition education interventions represent a promising approach for addressing MetS by promoting healthy dietary behaviors and lifestyle modifications[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These interventions aim to enhance individuals' knowledge, attitudes, and practices related to diet and nutrition, ultimately empowering them to make informed food choices[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Culturally tailored nutrition education programs are essential in the Bangladeshi context to accommodate traditional dietary practices while promoting healthier alternatives[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe effectiveness of nutrition education interventions in modifying dietary behaviors and improving metabolic outcomes among Bangladeshi adults remains understudied[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Research is needed to evaluate the impact of such interventions on MetS incidence, incidence, and management in Bangladesh. Additionally, identifying the barriers to and facilitators of dietary behavior change in this population is crucial for the development of effective and sustainable intervention strategies.\u003c/p\u003e \u003cp\u003eFurthermore, the role of specific nutrients and dietary components in the prevention and management of MetS warrants investigation. Studies have shown that diets high in protein, particularly plant protein, may have beneficial effects on blood pressure, lipid profiles, and insulin sensitivity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, the type and quality of dietary fats, carbohydrates, fiber, and micronutrients may influence metabolic health outcomes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNutrition education interventions hold great promise for mitigating the burden of MetS in Bangladesh and other settings experiencing similar dietary transitions[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. By promoting healthy dietary behaviors and empowering individuals to make informed food choices, these interventions have the potential to reduce the prevalence of MetS and its associated complications, ultimately improving public health outcomes. However, further research is needed to evaluate the effectiveness and scalability of such interventions in diverse populations and settings.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and samples\u003c/h2\u003e \u003cp\u003eThe current research adopted a randomized longitudinal approach, utilizing a probability sampling technique to select participants. Individuals were recruited from various locations and underwent registration, consenting, and eligibility screening before providing blood samples and data. This study focused on Rajshahi city, Kushtiasadar, and Pabna Sadar and targeted morning walkers from these areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants were interviewed and screened randomly to evaluate determinants and diagnose components of metabolic syndrome. Participants eligible for the baseline study were adults aged 30 to 69 years who participated in morning walks, while those excluded were individuals who had recently undergone surgery, who had a history of heart attack or stroke, who were pregnant or postpartum women, and who were outside the specified age range. The sample size calculation was based on the prevalence reported in a previous systematic review, resulting in an estimated sample size of 359 at a 5% significance level with a precision of 0.05. Initially, 500 adults were enrolled, including 300 from Rajshahi city, 150 from Pabna Sadar, and 150 from Kushtiasadar, with both genders represented (297 males and 203 females).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCollection of data\u003c/h2\u003e \u003cp\u003eInitially, 560 participants were approached, and after applying the criteria, a final cohort of 500 participants was screened at specified locations. Fasting blood samples were collected and assessed based on modified NCEP ATP III criteria (2005) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]at three centers in Bangladesh, namely, the Plasma Diagnostic Center in Rajshahi, the Padma Diagnostic Centre in Pabna, and the Applied Nutrition and Food Technology Laboratory at Islamic University in Kushtia, Bangladesh.A meticulously crafted close-ended questionnaire was used to collect data encompassing sociodemographic details, lifestyle habits, physical activity, anthropometric measurements, clinical information, and knowledge about MetS and fats. Blood pressure (BP) was measured in a standardized, calm, and comfortable setting, with individuals seated quietly and their armssupported. Specific instructions for accurate electronic BP measurements were followed [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Body height was measured using Seca stadiometers, and weight was measured with an Omron scale. Height and weight were recorded to the nearest 0.1 cm and 0.1 kg, respectively. The waist circumference, measured at the midpoint between the 12th rib and iliac crest, was rounded to the nearest 0.1 cm [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Fasting blood samples were collected following the WHO Guidelines on Drawing Blood [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and the serum glucose, triglyceride, and high-density lipoprotein (HDL) cholesterol levels were assessed [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The samples were transported in a cold chain and centrifuged, and the resulting serum was stored at -80\u0026deg;C until analysis. Serum triglyceride and high-density lipoprotein (HDL)-cholesterol levels were determined using an AE-600F biochemistry analyzer (Emra)[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].Blood glucose levels were measured using an ACCU-CHEK glucometer by applying a drop of blood onto a chemically treated test strip [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Serum/plasma triglycerides were enzymatically measured through hydrolysis, generating glycerol, which underwent oxidation. The absorbance was quantified at 500 nm following the method of Klotzsch and McNamara [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. HDL-C was directly measured in serum using the method byHirano \u003cem\u003eet al\u003c/em\u003e.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eNutritional Intervention\u003c/h2\u003e \u003cp\u003eA four-month community-based intervention program (December 2020\u0026ndash;March 2021) targeted adults aged 30\u0026ndash;69 years with metabolic syndrome from two districts, sadars, and one city corporation commune in Bangladesh. Participants provided written informed consent before entry into the trial. The inclusion criteria included age 30 to 69 years and a diagnosis of MetS, with both sexes included, while the exclusion criteria excluded those taking certain medications or receiving counseling elsewhere. Subjects were randomized into intervention and control groups, with 36 in the intervention group and 33 in the control group. The intervention group received three individual in-depth nutrition counseling sessions, while the control group received one session of group counseling. Postintervention assessments were completed by 34 intervention participants (91.8% response rate) and 31 control participants (93.9% response rate). Nutrition counseling incorporated tailored messages, including \"10 healthy habits for adults,\" and considered urban morning walkers' cultural values, favoring free-flowing communication through initial group sessions guided by trained nutritionists, each lasting approximately 60 minutes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis utilized the Statistical Package for the Social Sciences (SPSS) software version 25.0 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Descriptive analysis was employed to assess the demographic and clinical characteristics of the participants. Descriptive statistics were computed for each variable entered, with continuous variables presented as the means accompanied by standard deviations (SDs).For categorical variables, frequencies and percentages were computed. Chi-square tests were used to examine associations between categorical variables, and independent sample \u003cem\u003et\u003c/em\u003etests were used to assess mean differences among continuous variables concerning the presence of MetS and its components. Logistic regression, utilizing the enter method, determined associations between determinants and MetS, with binary logistic regression calculating odds ratios and 95% confidence intervals, adjusting for confounding factors. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSociodemographic information \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026nbsp;included\u0026nbsp;500 morning walking adults, 59.4%\u0026nbsp;of whom were\u0026nbsp;male and 40.6%\u0026nbsp;of whom were female. The mean ages of the\u0026nbsp;males and females were 44.97 \u0026plusmn; 9.31 and 43.33 \u0026plusmn; 9.02\u0026nbsp;years,\u0026nbsp;respectively.\u0026nbsp;Most\u0026nbsp;hailed from Rajshahi city (54.6% male, 45.3% female),\u0026nbsp;which was\u0026nbsp;a statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Education levels varied significantly between genders (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), with proportions ranging from 28.9% to 79.2% for males and 20.8% to 71.1% for females across different levels. Professional categories also showed significant gender disparities (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), with varying proportions across service holders, unemployed\u0026nbsp;individuals, laborers, homemakers, business owners, and others for both males and females (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003eSocio-demographic characteristics of study participants\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.664556962025316%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.189873417721518%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.474683544303797%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e297 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e203 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.189873417721518%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.474683544303797%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e44.97 \u0026plusmn; 9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e43.33 \u0026plusmn; 9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.189873417721518%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.474683544303797%\" valign=\"top\"\u003e\n \u003cp\u003eKushtia (sadar)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e57 (57.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e43 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003ePabna (sadar)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e76 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e24 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eRajshahi (city)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e164 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e136 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.189873417721518%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.474683544303797%\" valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e13 (28.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e32 (71.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e50 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e70 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e44 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e33 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eHigher Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e49 (61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e31 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eGraduation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e141 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e37 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.189873417721518%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.474683544303797%\" valign=\"top\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e175 (86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" valign=\"top\"\u003e\n \u003cp\u003e27 (13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77848101265823%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.000*\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e12 (85.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e2 (14.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eHomemakers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e160 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eLaborer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e17 (73.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e6 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e81 (93.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e6 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.17274939172749%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e12 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.413625304136254%\" valign=\"top\"\u003e\n \u003cp\u003e2 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant at\u003cem\u003e\u0026nbsp;P\u0026lt;\u003c/em\u003e0.05. The data are presented as the means \u0026plusmn; SDs. The values are presented as the number of participants (percentages). Differences were analyzed by independent samples \u003cem\u003et\u003c/em\u003etests\u0026nbsp;for continuous variables and\u0026nbsp;chi-square\u0026nbsp;tests\u0026nbsp;for categorical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence and age-stratified distribution of MetS and Non-MetS in both sexes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research revealed that the overall prevalence of MetS was 59.6% in males and 59.1% in females. Among male participants, the third age group (50-59 years) had the highest MetS rate at 35.5%, while the fourth age group (60-69 years) had the lowest at 6.2%. Among females, 45.8% of those in the second age group (40-49 years) had the highest MetS percentage,and 5.8% of those in the fourth age group had the lowest percentage(Fig. 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of nutritional knowledge between MetS and non-MetS participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was\u0026nbsp;a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in nutritional knowledge regarding MetS and fats (good/bad) between\u0026nbsp;the\u0026nbsp;MetS and non-MetS groups, whereas in males, no significant difference (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) was observed. In males, similar proportions had knowledge about MetS (10.7% MetS, 15.0% non-MetS), while in females, the percentages were 6.7% for MetS and 22.9% for non-MetS. This trend persisted for knowledge about fats. Overall, approximately 80% of both male and female participants lacked knowledge about MetS and fats (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of nutritional knowledge between MetS and non-MetS participants\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"727\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.283356258596974%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21595598349381%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.52544704264099%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97524071526823%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.252747252747252%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.203296703296703%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnswer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.225274725274724%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-MetS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.065934065934066%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.362637362637363%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.362637362637363%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-MetS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.340659340659341%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.252747252747252%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetS Knowledge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.203296703296703%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.225274725274724%\" valign=\"top\"\u003e\n \u003cp\u003e19 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\" valign=\"top\"\u003e\n \u003cp\u003e18 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.065934065934066%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.362637362637363%\" valign=\"top\"\u003e\n \u003cp\u003e8 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.362637362637363%\" valign=\"top\"\u003e\n \u003cp\u003e19 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.340659340659341%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.50925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.60185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e158 (89.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e102 (85.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e112 (93.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e64 (77.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.252747252747252%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnowledge about Fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.203296703296703%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.225274725274724%\" valign=\"top\"\u003e\n \u003cp\u003e34 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\" valign=\"top\"\u003e\n \u003cp\u003e17 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.065934065934066%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.362637362637363%\" valign=\"top\"\u003e\n \u003cp\u003e16 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.362637362637363%\" valign=\"top\"\u003e\n \u003cp\u003e24 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.340659340659341%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.50925925925926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.60185185185185%\" valign=\"top\"\u003e\n \u003cp\u003e143 (80.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e103 (85.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e104 (86.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e59 (71.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant at\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.05. Percentages were analyzed by the chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the intervention subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth genders were included in the intervention, with 52.9% male and 47.1% female in the in-depth intervention group and 45.2% male and 54.8% female in the single intervention group. The mean age in the in-depth intervention group was 45.35 \u0026plusmn; 8.03 years, which was slightly greater than that in the single intervention group (mean age of 40.96 \u0026plusmn; 7.18 years). Higher secondary education was more prevalent in the in-depth intervention group (35.3%) than in the single intervention group (19.3%), while a greater proportion of subjects in the single intervention group held graduate degrees (32.2% vs. 17.6% in the in-depth intervention group). The distribution of occupations was mostly similar between the two intervention groups, except for the business category, which was greater in the single intervention group (23.5%) than in the in-depth intervention group (9.6%) (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eBaseline characteristics of the intervention subjects\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-depth intervention (n=34) (n%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle intervention (n=31) (n%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e18 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e14 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e16 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e17 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e45.35 \u0026plusmn; 8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e40.96 \u0026plusmn; 7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e31 (92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e31 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eWidower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e3 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eGraduation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e6 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e10 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eHigher Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e12 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e6 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e4 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e6 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e9 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e5 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eNo formal Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e3 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e4 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e12 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e13 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eHousewife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e12 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e14 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e8 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e3 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.770642201834864%\" valign=\"top\"\u003e\n \u003cp\u003eLaborer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.44954128440367%\" valign=\"top\"\u003e\n \u003cp\u003e2 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.77981651376147%\" valign=\"top\"\u003e\n \u003cp\u003e1 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePercentages were analyzed by the chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of clinical indices at baseline and after 4 months of intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in systolic blood pressure was detected between baseline and after both the in-depth and single interventions. However, for diastolic blood pressure, a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) was found only between baseline and the in-depth intervention group (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eComparison of clinical indices at baseline and after 4 months of intervention\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.091678420310297%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.286318758815234%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-depth Intervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.46262341325811%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.85049365303244%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle Intervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.30888575458392%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.630252100840337%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.949579831932773%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-depth Intervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.210084033613445%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.210084033613445%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle Intervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.091678420310297%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.207334273624824%\" valign=\"top\"\u003e\n \u003cp\u003e127.35 \u0026plusmn;14.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07898448519041%\" valign=\"top\"\u003e\n \u003cp\u003e120.88 \u0026plusmn; 8.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.46262341325811%\" valign=\"top\"\u003e\n \u003cp\u003e0.004*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.92524682651622%\" valign=\"top\"\u003e\n \u003cp\u003e122.09 \u0026plusmn; 16.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.92524682651622%\" valign=\"top\"\u003e\n \u003cp\u003e130.96 \u0026plusmn; 15.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.30888575458392%\" valign=\"top\"\u003e\n \u003cp\u003e0.000*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.091678420310297%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.207334273624824%\" valign=\"top\"\u003e\n \u003cp\u003e84.23 \u0026plusmn; 7.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.07898448519041%\" valign=\"top\"\u003e\n \u003cp\u003e78.11 \u0026plusmn; 5.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.46262341325811%\" valign=\"top\"\u003e\n \u003cp\u003e0.000*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.92524682651622%\" valign=\"top\"\u003e\n \u003cp\u003e80.48 \u0026plusmn; 8.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.92524682651622%\" valign=\"top\"\u003e\n \u003cp\u003e82.90 \u0026plusmn; 7.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.30888575458392%\" valign=\"top\"\u003e\n \u003cp\u003e0.226\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*The statistical significance level was set at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. The data are presented as the means \u0026plusmn; SDs, and differences were analyzed using paired samples \u003cem\u003et\u003c/em\u003e tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of biochemical indices at baseline and after 4 months of intervention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) were observed in both fasting blood glucose (FBG) and triglyceride (TG) levels between baseline and after 4 months of intervention. Conversely, no significant differences (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) were noted between baseline and the single intervention group for these biochemical indices (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of gender-stratified HDL-C at baseline and after 4 months of intervention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor males, no significant difference (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) was observed in either the in-depth or single intervention. However, in females, a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) was found in the in-depth intervention study, while no significant difference was noted in the single intervention (Fig. 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of gender-stratified WC at baseline and after 4 months of intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the first group, baseline scores were 93.25% for males and 88.25% for females, which decreased to 90.61% and 85.56%,\u0026nbsp;respectively,\u0026nbsp;after an in-depth intervention. On the other hand,\u0026nbsp;in the\u0026nbsp;second group,\u0026nbsp;the\u0026nbsp;baseline scores were higher, with 96.85%\u0026nbsp;for males and\u0026nbsp;94.29%\u0026nbsp;for females. Following a single intervention,\u0026nbsp;the\u0026nbsp;male\u0026nbsp;score\u0026nbsp;increased to 98.42%, while\u0026nbsp;the\u0026nbsp;female\u0026nbsp;score\u0026nbsp;slightly decreased to 94.05%.\u0026nbsp;For females, a significant difference (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05)\u0026nbsp;was observed\u0026nbsp;in\u0026nbsp;the\u0026nbsp;in-depth intervention\u0026nbsp;group. Conversely, in\u0026nbsp;the\u0026nbsp;case of\u0026nbsp;males, no significant difference (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05) was found\u0026nbsp;between the\u0026nbsp;in-depth and single intervention\u0026nbsp;studies(Fig. 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in MetS components at baseline and after 4 months of in-depth and single intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt baseline, the prevalence of MetS was 0%, 0%, 44.1%, 47.1%, and 3.8% for categories 1, 2, 3, 4, and 5 (load of MetS),\u0026nbsp;respectively. After completing the in-depth intervention period, these values changed to 14.7%, 44.1%, 23.5%, 11.8%, and 5.9%,\u0026nbsp;respectively. In the single intervention group,\u0026nbsp;the\u0026nbsp;baseline prevalence of MetS was 0%, 0%, 48.4%, 41.9%, and 9.7% for categories 1, 2, 3, 4, and 5,\u0026nbsp;respectively. Upon completion of the single intervention period, the prevalence shifted to 0%, 9.7%, 32.3%, 35.5%, and 22.6% for categories 1, 2, 3, 4, and 5,\u0026nbsp;respectively (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eChanges in MetS components at baseline and after 4 months of intervention\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.857142857142858%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of MetS components\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.464285714285715%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-depth intervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.952380952380953%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle intervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.119047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.297101449275363%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.57246376811594%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-depth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.28985507246377%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.029761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43452380952381%\" valign=\"top\"\u003e\n \u003cp\u003e5 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.119047619047619%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.813688212927758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.20152091254753%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.44106463878327%\" valign=\"top\"\u003e\n \u003cp\u003e15 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.250950570342205%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29277566539924%\" valign=\"top\"\u003e\n \u003cp\u003e3 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.813688212927758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.20152091254753%\" valign=\"top\"\u003e\n \u003cp\u003e15 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.44106463878327%\" valign=\"top\"\u003e\n \u003cp\u003e8 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.250950570342205%\" valign=\"top\"\u003e\n \u003cp\u003e15 (48.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29277566539924%\" valign=\"top\"\u003e\n \u003cp\u003e10 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.813688212927758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.20152091254753%\" valign=\"top\"\u003e\n \u003cp\u003e16 (47.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.44106463878327%\" valign=\"top\"\u003e\n \u003cp\u003e4 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.250950570342205%\" valign=\"top\"\u003e\n \u003cp\u003e13 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29277566539924%\" valign=\"top\"\u003e\n \u003cp\u003e11 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.813688212927758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.20152091254753%\" valign=\"top\"\u003e\n \u003cp\u003e3 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.44106463878327%\" valign=\"top\"\u003e\n \u003cp\u003e2 (5.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.250950570342205%\" valign=\"top\"\u003e\n \u003cp\u003e3 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.29277566539924%\" valign=\"top\"\u003e\n \u003cp\u003e7 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe \u003cem\u003eP\u003c/em\u003e value was ascertained by the chi-square test.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study on nutrition education interventions among Bangladeshi adults who participate in morning walks provides notable demographic insights. The participant pool consisted of a greater proportion of males than females, with both groups having a mean age in their mid-forties. A significant finding is the difference in the geographical distribution of participants from Rajshahi city, where more males than females were represented. A previous study focused on the associations among nutrition literacy, demographics, and personal beliefs among Bangladeshi adults and revealed that occupation, income, education level, nutrition-related education, and perceived need for nutritional information significantly influence nutrition literacy scores [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Education levels between genders also varied significantly, indicating disparities in educational attainment, which could influence health literacy and receptiveness to nutrition education [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Furthermore, professional categories showed significant gender disparities, with males and females distributed across different occupations, such as service holders, unemployed individuals, laborers, homemakers, and business owners [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These differences underscore the necessity for tailored nutrition education interventions that account for gender-specific factors [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch has indicated that the incidence of metabolic syndrome (MetS) is similar for both males and females. In males, the highest rate of MetS was observed in those in their 50s, while the lowest rate was in those in their 60s. For females, the highest prevalence occurred in those in their 40s, with the lowest in those in their 60s. These findings suggest that MetS is more common in middle-aged adults, particularly those in their forties and fifties, with a notable decrease in prevalence among older age groups of both genders [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current research revealed a significant difference in nutritional knowledge regarding MetS and fats between female participants with and without MetS, whereas no such difference was observed among males. In males, knowledge about MetS was similar between those with and without this condition. However, in females, those without MetS demonstrated notably greater awareness of MetS than did those with MetS[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This trend was consistent for knowledge about fats. Overall, the majority of both male and female participants lacked sufficient knowledge about MetS and fats, highlighting a general gap in nutritional awareness within the study population. However, a study from Ethiopia revealed the significant impact of men's nutritional knowledge on household dietary diversity, suggesting the enhanced integration of men in nutrition-sensitive agricultural interventions to bolster overall household nutrition outcomes in low-income rural settings [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Nutrition education is crucial for improving dietary practices and overall diet quality, as evidenced by this study assessing knowledge, attitudes, and practices (KAPs) related to nutrition principles in urban and rural households of West Azerbaijan Province, Iran [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Studies on Arab countries such as Egypt, Syria, Saudi Arabia, and Jordan underscore the widespread inadequate nutritional knowledge (73.1%) and identify key demographic predictors, emphasizing the critical need for targeted educational interventions to improve dietary habits and health outcomes in these populations [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study included both genders in the intervention, with a slightly greater proportion of males in the in-depth intervention group and a greater proportion of females in the single intervention group. Participants in the in-depth intervention group were, on average, older than those in the single intervention group. Education levels varied between the groups, with higher secondary education being more common among those in the in-depth intervention group, while a greater proportion of participants in the single intervention group held graduate degrees [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The distribution of occupations was generally similar across both intervention groups, with the exception of the business category, which was more represented in the single intervention than in the in-depth intervention [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These differences highlight the varying demographic characteristics that could influence the effectiveness and approach of the interventions [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study's comparison of clinical indices at baseline and after four months of intervention revealed significant changes in systolic blood pressure (SBP) for both the in-depth and single intervention groups. Both groups showed a significant reduction in SBP from baseline to after the intervention. However, diastolic blood pressure (DBP) decreased significantly only in the in-depth intervention group, with no significant change observed in the single intervention group. These findings suggest that while both interventions were effective in reducing systolic blood pressure, the in-depth intervention had a more pronounced effect on lowering diastolic blood pressure than the single intervention [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This indicates that in-depth intervention may offer greater benefits for managing certain aspects of blood pressure [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA comparison of biochemical indices at baseline and after four months of intervention revealed significant improvements in fasting blood glucose (FBG) and triglyceride (TG) levels in the in-depth intervention group. These indices significantly decreased from their baseline values after the intervention. In contrast, the single intervention group did not exhibit any significant changes in FBG or TG levels over the same period. This suggests that the in-depth intervention was more effective in improving key biochemical markers associated with metabolic health, whereas the single intervention did not produce substantial changes in these indices [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA comparison of sex-stratified HDL cholesterol (HDL-C) levels at baseline and after four months of intervention revealed that males did not experience significant changes in HDL-C in either the in-depth or single intervention groups. However, females showed a significant improvement in HDL-C levels in the in-depth intervention group, while no significant changes were observed in the single intervention group. This indicates that the in-depth intervention was particularly effective in enhancing HDL-C levels among female participants, whereas neither intervention had a notable impact on HDL-C levels in males [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA comparison of sex-stratified waist circumference (WC) at baseline and after four months of intervention revealed different outcomes for males and females. In the in-depth intervention group, both males and females experienced a reduction in WC, with females showing a significant decrease. In contrast, in the single intervention group, WC increased for males, while WC decreased slightly for females, although the difference was not significant. These results indicate that the in-depth intervention was effective in significantly reducing WC in females but not in males. On the other hand, a single intervention was not effective for either sex, with males even experiencing an increase in WC [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study examined changes in MetS components following two different intervention approaches over a four-month period. Initially, categories reflecting higher MetS risk levels showed the highest prevalence across both intervention groups, with lower-risk categories exhibiting lower prevalence. After the in-depth intervention, there was an observable reduction in MetS incidence across several categories, particularly those initially categorized as highrisk. This suggests that in-depth intervention may effectively mitigate MetS risk factors in these groups [\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Conversely, the single intervention showed varied outcomes, with some categories showing reductions in MetS incidence, while others experienced increases. These results underscore the potential effectiveness of tailored, comprehensive interventions in targeting and improving specific MetS components compared to more generalized approaches [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBoth in-depth and single nutritional counseling interventions have significant implications for improving metabolic parameters among adults with metabolic syndrome (MetS). The interventions resulted in notable reductions in waist circumference and improvements in fasting blood glucose levels and HDL-C among participants, particularly females. These findings underscore the effectiveness of community-based nutrition education in managing MetS components and advocate for the integration of such interventions into public health strategies aimed at reducing cardiovascular risk factors in Bangladeshi adults. Further research and broader implementation of tailored nutritional interventions are warranted to optimize health outcomes and mitigate the burden of MetS in this population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eMetS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eMetabolic Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eFasting Blood Glucose\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eHigh-Density Lipoprotein\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eBlood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eSPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"88.46153846153847%\" valign=\"top\"\u003e\n \u003cp\u003ewaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to thank Department of Applied Nutrition and Food Technology, Islamic University, Kushtia, Bangladesh for the laboratory and logistic support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent/assent form\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent were taken from the participants of the current study. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSA and BSA conceptualized, prepared, and revised the paper; MK and MMRwrote the paper\u0026rsquo;s content and collected and analyzed the data; BAA visualize the data and prepare the map. \u0026nbsp;SA, BSA, MK, MMR and BAA reviewed the final manuscript and approved its submission. SA had the final responsibility of submitting the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific grant was given for this research by public, private, or nonprofit funding organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo ethical approval was required to conduct the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. 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J Obes Metab Syndr. 2024;33:20\u0026ndash;6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular risk factors, waist circumference reduction, fasting blood glucose, HDL-c improvement, community-based intervention","lastPublishedDoi":"10.21203/rs.3.rs-4948926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4948926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolic syndrome (MetS) is a cluster of metabolic abnormalities that includes central obesity, hypertension, dyslipidemia, and disturbed glucose metabolism. To the best of our knowledge, no research in Bangladesh has evaluated the effect of nutritional interventions on MetS.The main objective was to explore the effects of nutritional interventions on participants with MetS. A cross-sectional study was carried out on 500 Bangladeshi adults (30 to 69 years; both males and females) who provided informed consent. Modified NCEP ATP III criteria for Asians were used to diagnose the subjects. This study revealed that the overall percentages of men and women with MetS were 59.6% and 59.1%, respectively.The present study revealed a 2.69 cm reduction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in waist circumference in females after 4 months of in-depth nutritional counseling and a 0.24 cm reduction after 4 months of single-intervention nutritional counseling. Similarly, a 2.64 cm reduction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in males after 4 months of in-depth nutritional counseling and a 1.57 cm increase after a single intervention of nutritional counseling were found to be significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A 1.08 mmol/L decrease in FBG was found after in-depth nutritional counseling for 4 months, while no significant difference was detected after a single intervention. A 9.37 mg/dl increase in HDL-C was found (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for females, but for males, the levels of HDL-C remained nearly the same in both intervention groups. A reduction in the MetS proportion was found in the intervention groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The loads of MetS components 3, 4, and 5 were 44\u0026ndash;23.5%, 47.1\u0026ndash;11.8%, and 8.8\u0026ndash;5.9% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), respectively, in the in-depth intervention group, whereas in the single intervention group, they were 50.0\u0026ndash;32.3%, 41.9\u0026ndash;35.5%, and 9.7\u0026ndash;22.6%, respectively. Thus, community-based in-depth nutritional counseling reduced the proportion of individuals with MetS and significantly improved several metabolic parameters in Bangladeshi adults with MetS.\u003c/p\u003e","manuscriptTitle":"Nutritional education interventions on the components of metabolic syndrome in Bangladeshi adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 09:39:39","doi":"10.21203/rs.3.rs-4948926/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-11T12:46:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-23T13:15:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-22T00:35:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2024-08-21T05:56:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"257aa841-da5a-47d5-8e23-7049472747ed","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:04:56+00:00","versionOfRecord":{"articleIdentity":"rs-4948926","link":"https://doi.org/10.1186/s40795-025-01121-2","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2025-10-14 15:58:39","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2024-10-15 09:39:39","video":"","vorDoi":"10.1186/s40795-025-01121-2","vorDoiUrl":"https://doi.org/10.1186/s40795-025-01121-2","workflowStages":[]},"version":"v1","identity":"rs-4948926","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4948926","identity":"rs-4948926","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}