Clustering Of Lifestyle Risk Factors Research Articles

Clustering of lifestyle risk factors in relation to suicidal thoughts and behaviors in young adolescents: a cross-national study of 45 low- and middle-income countries

BackgroundPrior research has reaffirmed lifestyle risk behaviors to cluster among adolescents. However, the lifestyle cluster effect on suicidal thoughts and behaviors (STBs) was unclear among adolescents in low- and middle-income countries (LMICs). No comparison of such associations was conducted across nations.MethodsData from 45 LMICs were obtained from the Global School-based Student Health Survey (GSHS) between 2009 and 2019. Lifestyle behavior factors were collected through a structured questionnaire. Suicidal ideation, plan, and attempt were ascertained by three single-item questions. Lifestyle risk scores were calculated via a sufficient dimension reduction technique, and lifestyle risk clusters were constructed using a latent class analysis. Generalized linear mixed models with odds ratio (OR) and 95% confidence interval (CI) were used to estimate the lifestyle-STB associations.ResultsA total of 229,041 adolescents were included in the final analysis. The weighted prevalence of suicidal ideation, plan, and attempt was 7.37%, 5.81%, and 4.59%, respectively. Compared with the favorable lifestyle group, the unfavorable group had 1.48-, 1.53-, and 3.11-fold greater odds of suicidal ideation (OR = 1.48, 95%CI: 1.30–1.69), plan (OR = 1.53, 95%CI 1.34–1.75), and attempt (OR = 3.11, 95%CI 2.64–3.65). Four clusters of lifestyle risk behaviors were identified, namely healthy lifestyles (H–L), insufficient intake of vegetables and fruit (V-F), frequent consumption of soft drinks and fast food (D-F), and tobacco smoking and alcohol drinking (S-A) clusters. Compared with H–L cluster, V-F cluster was associated with 43% and 42% higher odds of suicidal ideation and plan, followed by S-A cluster (26% for ideation and 20% for plan), but not significant in D-F cluster (P > 0.05). D-F cluster was associated with 2.85-fold increased odds of suicidal attempt, followed by V-F cluster (2.43-fold) and S-A cluster (1.18-fold).ConclusionsClustering of lifestyle risk behaviors is informative for risk stratification of STBs in resource-poor settings. Lifestyle-oriented suicide prevention efforts should be initiated among school-attending adolescents in LMICs.

Stability of clustering of lifestyle risk factors in the Dutch adult population and the association with mental health.

Lifestyle factors often co-occur in clusters. This study examines whether clusters of lifestyle risk factors, such as smoking, alcohol use, physical inactivity, poor diet, sexual risk behaviour, cannabis and other drug use, change over time in a representative sample of Dutch adults. Additionally, the association between mental health and self-reported depression of lifestyle clusters was examined. Each year cross-sectional data of approximately 7500 individuals of 18 years and older from the annual Dutch Health Survey of 2014-2019 were used. Clusters were determined by a two-step cluster analysis. Furthermore, regression analyses determined the association between clusters of lifestyle risk factors and mental health. Results show six clusters composed of one, multiple or no lifestyle risk factors. The clusters remained relatively stable over time: in some clusters, the number of people slightly changed between 2014 and 2019. More specifically, clusters that increased in size were the cluster with no lifestyle risk factors and the cluster with multiple lifestyle risk factors. Furthermore, results show that clusters with none to a few lifestyle risk factors were associated with better mental health and a lower prevalence of self-reported depression compared with clusters with multiple lifestyle risk factors. The clustering of lifestyle risk factors remained stable over time. People with multiple lifestyle risk factors had poorer mental health than those without risk factors. These findings may emphasize the need for intervention strategies targeting this subgroup with multiple lifestyle risk factors.

Clustering of Lifestyle Risk Factors among Algerian Adolescents: Comparison between Urban and Rural Areas: GSHS Data.

Objective: Compare the clustering of LBRs between urban and rural Algerian adolescents. Design: Data of this cross-sectional study was derived from the Global School-based Health Survey (GSHS). A self-administered, anonymous questionnaire was filled out by 4532 adolescents (11–16 years), which addressed LBRs of NCDs. Life style behavioral risk factors (LBRs) clustering was measured by the ratios of observed (O) and expected (E) prevalence of one or more simultaneously occurring LBRs for urban and rural areas separately. Multivariate logistic regression was performed to examine the association of LBRs as dependent variable with demographic variables (location, age, gender). Results: The most common LBR was physical inactivity (84.6%: 50.9% for urban and 49.1% for rural). Adolescents in urban areas had a higher prevalence of two (56.8% vs. 43.2%) and three and more (61.3% vs. 38.7%) LBRs than in rural areas. In urban areas, a significant positive association was found between (low fruit and vegetable consumption + physical inactivity) [2.06 (1.61–2.64)] and (high SB + smoking) [2.10 (1.54–2.76)], while (physical inactivity + high SB) [0.70 (0.54–0.91)] showed a significant negative association. In rural areas, (high SB + overweight/obesity) [1.49 (1.09–2.04)] had a significant positive association. While, (low fruit and vegetable consumption + high SB) [0.75 (0.60–0.94)], (physical inactivity + high SB) [0.65 (0.49–0.86)], and (physical inactivity + smoking) [0.70 (0.49–0.99)] had a negative association. Conclusions: Several socio-demographic factors have been identified to play a role in LBRs clustering among Algerian adolescents. Results of the study suggest the development of intervention aiming to tackle different LBRs rather than focusing on a single LBR.

Clustering of lifestyle risk factors among adult population in India: A cross-sectional analysis from 2005 to 2016.

IntroductionIndividual’s early life style and health behaviors are directly linked to chronic non-communicable diseases. Considering the increased burden of NCDs during the last two decades, the aim of this study is to assess co-occurrence/clustering of lifestyle risk factors and its association with different socio-demographic and economic characteristics among adult men and women in India from 2005–2016.MethodsThis study utilized the data from the National Family Health Survey 2005–06 and 2015–16 survey rounds. Multinomial logistic regression is employed to evaluate co-occurrence of multiple risk factors among adult men and women of different socio-economic and demographic characteristics to identify the subgroups with elevated risk of clustering of multiple unhealthy lifestyle risk factors.ResultsMore adult men in India tend to exhibit clustering of multiple non-communicable disease risk factors than females. Individuals between 30–49 years of age, residing in urban areas, the population with no education, separated couples and those from poor economic strata are the specific population subgroups show higher prevalence of co-occurrence of multiple risk factors. The regional pattern of clustering of risk factors shows that the prevalence of co-occurrence of multiple risk factors is higher among men and women from the North-Eastern part of India compared to the other regions of the country.ConclusionThe prevalence of clustering of multiple risk factors associated with chronic NCDs is substantially high and has increased between 2005–06 to 2015–16. India may therefore experience a significant increase in the burden of chronic non-communicable diseases in the coming years. We therefore conclude that appropriate strategies should be implemented by policy makers and the government to reduce the overall health burden of NCDs due to lifestyle habits.

Clustering of lifestyle risk factors for non-communicable diseases in 304,779 adolescents from 89 countries: A global perspective

The precursors of non-communicable diseases (NCDs) are often manifested during childhood and adolescence with little knowledge about co-occurrence of their related lifestyle risk factors. To address this deficit, we estimated the prevalence and clustering of six major NCD-risk factors in adolescents around the world. Data from the Global School-based Student Health Survey, collected between 2007 and 2016, were analysed in 304,779 adolescents aged 11–17 years (52.2% females) from 89 countries. We compared the observed (O) to expected (E) prevalence ratios of 64 possible combinations of six risk factors to determine their clustering patterns. Overall, 82.4% (95% CI 82.1–82.7) of adolescents had ≥2 risk factors, while 34.9% (34.6–35.3) had ≥3. Adolescents aged 16–17 years, compared to those aged 11–13 years, had higher odds (OR 1.33; 95% CI 1.31–1.36) of reporting ≥3 risk factors. Risk factors clustered in multiple combinations and differed by sex. The clustering of physical inactivity and low fruit and vegetable intake was evident in both males (O/E 1.10; 95% CI 1.07–1.12) and females (1.08; 1.06–1.10). The co-occurrence of cigarette smoking, alcohol drinking, physical inactivity, and low fruit and vegetable intake was 165% greater in females (2.65; 2.28–3.07) and 110% greater in males (2.10; 1.90–2.32) than expected. Globally, adolescents exhibit multiple modifiable risk factors for future development of NCDs. Early gender-specific prevention strategies targeting clusters of lifestyle risk factors should be prioritised to help mitigate future burden of NCDs globally. Periodical collection of behavioural risk factor data should be encouraged to facilitate a sustainable global surveillance.

Serum 25(OH) Vitamin D Deficiency and Risk of Breast Cancer in Malaysia: A Case-Control Study

Background: Despite the emerging literature supporting the beneficial role of sufficient vitamin D level and its association with various cancers, current evidence on the vitamin D and the risk of getting breast cancer is still inconsistent. Furthermore, the study between vitamin D status and breast cancer risk among south east Asian women is limited. Aim: We aimed to investigate the association between vitamin D deficiency and the risk of getting breast cancer among Malaysian women. Methods: We conducted a retrospective nested case control study which consisted of Malaysian Breast Cancer Cohort Study (MyBCC) subjects as the cases and UMMC Mammogram Cohort Study subjects as the hospital control. We also used a secondary data from the Cohort Study on Clustering of Lifestyle Risk Factors and understanding its association with stress on health and well-being among school teachers in Malaysia (CLUSTer), as we only included the data from the schools in Kuala Lumpur as the population control. We measured and compared serum 25-hydroxyvitamin D (25(OH)D) between newly diagnosed breast cancer patients (n= 231), female without cancer who came for mammogram checkup (n= 462) and female teachers without cancer from schools in Kuala Lumpur (n=231). Vitamin D deficiency was defined as serum (25(OH)D) < 50 nmol/L. Results: Median serum (25(OH)D) for cases and controls were 42.43 nmol/L and 44.30 nmol/L, respectively. We examined the association between serum (25(OH)D) and breast cancer risk stratified by age < 50 and ≥ 50 years old. The crude ORs for breast cancer risk in cases and controls did not show statistically significant association with serum (25(OH)D) for both age groups ( P = 0.463 and P = 0.650, respectively). After adjustment for age, ethnicity, education level and BMI the ORs (95% CI) for breast cancer risk among both groups were 1.183 (0.591-2.366) ( P = 0.635) and 1.237 (0.801-1.911) ( P = 0.338) respectively for women with serum (25(OH)D) < 50 nmol/L. Conclusion: Our results suggest that there is lack of association between vitamin D deficiency and the risk of getting breast cancer in Malaysian women.

Clustering of Lifestyle Risk Factors in Acute Coronary Syndrome: Prevalence and Change after the First Event.

Healthy lifestyles are modifiable risk factors for acute coronary syndrome (ACS) onset and recurrence. While unhealthy lifestyles tend to cluster together within the general healthy population, little is known about the prevalence and clustering of these behaviours in people with ACS before and after the first acute event. The aim of this study was to identify lifestyle profiles of patients with ACS and to explore their change after their first coronary event. Three hundred and fifty-six patients completed self-report measures of healthy habits at the beginning of cardiac rehabilitation and 6months later. By adopting a person-oriented approach, we analysed lifestyle clustering and its change over time. Differences in depression, anxiety, and negative illness perception among lifestyle profiles were assessed. We identified seven profiles, ranging from more maladaptive to healthier clusters. Findings showed a strong interrelation among unhealthy habits in patients. We highlighted a moderate individual and group stability of cluster membership over time. Moreover, unhealthier lifestyle profiles were associated with higher levels of depression, anxiety, and negative illness perception. These results may have implications for the development and implementation of multimodal interventions addressing wider-ranging improvement in lifestyles by targeting multiple unhealthy behaviours in patients with ACS.

Cardiovascular disease lifestyle risk factors in people with psychosis: a cross-sectional study

BackgroundPeople with psychosis die on average 25 years earlier than those in the general population, with cardiovascular disease (CVD) contributing to much of the excess mortality. This cross-sectional study aimed to identify the relationship between lifestyle risk factors for CVD – poor nutrition, smoking and low physical activity levels – and dyslipidaemia, hypertension and hyperglycaemia while controlling for potential confounders in 1825 people from the Survey of High Impact Psychosis (SHIP) in Australia. We also aimed to identify clustering patterns of lifestyle risk factors and associated demographic variables.MethodsThree logistic regressions were used to predict the effect of nutrition, smoking and physical activity on dyslipidaemia, hypertension and hyperglycaemia while controlling for clozapine use, sex and age. Clustering patterns of nutrition, smoking and physical activity were examined using the two-step cluster method which is based on hierarchical cluster analysis. Demographic variables associated with different clusters were identified using measures of association.ResultsSmoking status had a positive association with dyslipidaemia (adjusted odds ratio = 0.50; 95% confidence interval = 0.32–0.78; p = 0.002). Other cardiovascular disease lifestyle risk factors did not have a significant relationship with dyslipidaemia, hypertension and hyperglycaemia. Clustering patterns of lifestyle risk factors showed that younger men, with low education levels, and relying on a government pension, were most likely to display the poorest lifestyle risk behaviours. The largest cluster (42%) of participants was characterised by a mixed demographic profile and were most likely to display poor nutrition and low physical activity levels but less likely to smoke.ConclusionsOnly smoking status had a significant positive association with dyslipidaemia which could indicate that there are additional factors affecting the relationship between other cardiovascular lifestyle risk factors and dyslipidaemia, hypertension and hyperglycaemia in people with psychosis. Unknown confounders and traditional lifestyle risk factors may explain the high rates of CVD in this group. Clustering of lifestyle risk factors and their demographic profiles could help the design of intervention programs in people with psychosis.

Consistency and Generalizability of Dietary Patterns in a Multiethnic Working Population

BackgroundDietary pattern analysis is a complementary method to nutrient analysis in evaluating overall diet–disease hypotheses. Although studies have been conducted to derive dietary patterns among Malaysians, their consistency across subgroups has not been examined. ObjectiveThe study aimed to derive dietary patterns empirically and to examine the consistency and generalizability of patterns across sex, ethnicity, and urban status in a working population. DesignThis was a cross-sectional study using data from the Clustering of Lifestyle Risk Factors and Understanding its Association with Stress on Health and Well-Being among School Teachers in Malaysia study collected between August 2014 and November 2015. Dietary intake was assessed using a food frequency questionnaire, and dietary patterns were derived using factor analysis. Participants/settingParticipants were teachers from selected public schools from three states in Peninsular Malaysia (n=4,618). Main outcome measuresDietary patterns derived using factor analysis. Statistical analyses performedSeparate factor analysis was conducted by sex, ethnicity, and urban status to identify dietary patterns. Eigenvalue >2, scree plot, Velicer’s minimum average partial analysis, and Horn’s parallel analysis were used to determine the number of factors to retain. The interpretability of each dietary pattern was evaluated. The consistency and generalizability of dietary patterns across subgroups were assessed using the Tucker congruence coefficient. ResultsThere was no subgroup-specific dietary pattern found. Thus, dietary patterns were derived using the pooled sample in the final model. Two dietary patterns (Western and Prudent) were derived. The Western dietary pattern explained 15.4% of total variance, characterized by high intakes of refined grains, animal-based foods, added fat, and sugar-sweetened beverages as well as fast food. The Prudent dietary pattern explained 11.1% of total variance and was loaded with pulses, legumes, vegetables, and fruits. ConclusionsThe derived Western and Prudent dietary patterns were consistent and generalizable across subgroups of sex, ethnicity, and urban status. Further research is needed to explore associations between these dietary patterns and chronic diseases.

Clustering of lifestyle risk factors in employees: A quasi-experimental study in Sousse, Tunisia

Background:An increased understanding of the prevalence and clustering patterns of multiple lifestyle-related health factors may support efforts to reduce incidence of disease, management of existing chronic disease, and improve overall health outcomes. This study aimed to evaluate the effectiveness of three years based intervention in the workplace on clustering of non communicable diseases’ risk factors.Methods:We based our study on a quasi-experimental intervention study (pre and post assessments with intervention and control groups) in two districts in the governorate of Sousse. The evaluation before and after the intervention focused on attitudes and behaviors of participants. The intervention program team conducted several actions for the employees at workplace days focusing on free physical activity sessions and free smoking cessation consultations.Results:Participants who had no risk factor increased significantly from 5.9% to 10.3% (pl0.001) in intervention group but not significantly in control group. Those who had only one risk factor increased in intervention group but decreased in control group.Conclusion:Intervening on two or more risk behavior simultaneously might be more effective than intervening on them separately.

Risk of second primary malignancies among 1537 melanoma patients and risk of second primary melanoma among 52 354 cancer patients in Northern Italy.

The number of melanoma survivors has been increasing for decades due to early diagnosis and improved survival. These patients have an increased risk of developing a second primary cancer (SPC); also, melanoma is frequently diagnosed among patients firstly diagnosed with an extracutaneous malignancy. We evaluated the risk of developing a SPC among 1537 melanoma patients, and the risk of second primary melanoma (SPM) in 52 354 extracutaneous cancer patients, who were treated at the European Institute of Oncology in Milan, Italy, during 2000-2010. We calculated standardized incidence ratios (SIR) by applying gender-, age-, year- and region-specific reference rates to the follow-up time accrued between the diagnosis of the first and the second primary malignancies. Seventy-six SPC were diagnosed during a median follow-up of 4 years, of which 49 (64%) during the first 2 years upon melanoma diagnosis. The SIR was increased for cancer of breast (4.10, 95% CI 2.79-6.03), thyroid (4.67, 95% CI 1.94-11.22), brain (6.13, 95% CI 2.30-16.33) and for non-Hodgkin lymphoma (3.12, 95% CI 1.30-7.50). During a median follow-up of 4 years, 127 SPM were diagnosed: thick lesions were less frequent than for melanoma diagnosed as first cancer. The SIR was increased for cancer of breast (5.13, 95%CI 3.91-6.73), thyroid (16.2, 95%CI: 5.22-50.2), head and neck (5.62, 95%CI 1.41-22.50), soft tissue (8.68, 95%CI 2.17-34.70), cervix (12.5, 95% CI 3.14-50.20), kidney (3.19, 95%CI 1.52-6.68), prostate (4.36, 95%CI 2.63-7.24) and acute myeloid leukaemia (6.44, 95%CI 2.42-17.20). The most likely causes of these associations are the clustering of lifestyle risk factors in the same subgroups of population, mainly on a sociocultural basis and surveillance bias. This raises important questions about how to best follow cancer survivors by avoiding an inefficient use of resources and an excessive medicalization of these patients' lives.

Prevalence, Co-Occurrence and Clustering of Lifestyle Risk Factors Among UK Men

Objective: Men – more than women - engage in unhealthy lifestyle practices that place them at greater risk of developing non-communicable disease. This paper aims to explore the prevalence, co-occurrence and clustering of four core lifestyle risk factors and examine the socio demographic variation of their distribution, among men living in two central London boroughs. Method: A stratified street survey was undertaken with N=859 men. Prevalence odds ratios calculated risk factor clustering and a multinomial logistic regression model examined the socio-demographic variation. Results: Over 72% of men presented with combinations of lifestyle risk factors. Physical inactivity combined with a lack of fruit and vegetables was the most common combination. Co-occurrence was more prominent for unemployed, widowed, divorced/separated and white British men. Clustering was evident for adherence and non-adherence to UK health recommendations. Conclusion: Men may benefit from targeted health interventions that address multiple – rather than single – health related behaviours.

044. Clustering of Lifestyle Risk Factors and Low Bone Density in Older Adults: The Hertfordshire Cohort Study

044. Clustering of Lifestyle Risk Factors and Low Bone Density in Older Adults: The Hertfordshire Cohort Study

Cohort study on clustering of lifestyle risk factors and understanding its association with stress on health and wellbeing among school teachers in Malaysia (CLUSTer)--a study protocol.

BackgroundThe study on Clustering of Lifestyle risk factors and Understanding its association with Stress on health and wellbeing among school Teachers in Malaysia (CLUSTer) is a prospective cohort study which aims to extensively study teachers in Malaysia with respect to clustering of lifestyle risk factors and stress, and subsequently, to follow-up the population for important health outcomes.Method/designThis study is being conducted in six states within Peninsular Malaysia. From each state, schools from each district are randomly selected and invited to participate in the study. Once the schools agree to participate, all teachers who fulfilled the inclusion criteria are invited to participate. Data collection includes a questionnaire survey and health assessment. Information collected in the questionnaire includes socio-demographic characteristics, participants’ medical history and family history of chronic diseases, teaching characteristics and burden, questions on smoking, alcohol consumption and physical activities (IPAQ); a food frequency questionnaire, the job content questionnaire (JCQ); depression, anxiety and stress scale (DASS21); health related quality of life (SF12-V2); Voice Handicap Index 10 on voice disorder, questions on chronic pain, sleep duration and obstetric history for female participants. Following blood drawn for predefined clinical tests, additional blood and urine specimens are collected and stored for future analysis. Active follow up of exposure and health outcomes will be carried out every two years via telephone or face to face contact. Data collection started in March 2013 and as of the end of March 2014 has been completed for four states: Kuala Lumpur, Selangor, Melaka and Penang. Approximately 6580 participants have been recruited. The first round of data collection and blood sampling is expected to be completed by the end of 2014 with an expected 10,000 participants recruited.DiscussionOur study will provide a good basis for exploring the clustering of lifestyle risk factors and stress and its association with major chronic medical conditions such as obesity, hypertension, impaired glucose tolerance, diabetes mellitus, coronary heart diseases, kidney failure and cancers among teachers.

Clustering of Lifestyle Risk Factors and Poor Physical Function in Older Adults: The Hertfordshire Cohort Study

To examine the relationship between number of lifestyle risk factors (out of low physical activity, poor diet, obesity, smoking) and physical function in older community-dwelling men and women. Cross-sectional study, Hertfordshire, United Kingdom. Men (n = 1,682) and women (n = 1,540) aged 59 to 73. Physical activity was assessed using an administered questionnaire with a score from 0 to 100; low activity was defined as a score of 50 or less. Diet was assessed using a food frequency questionnaire; diet quality was assessed according to a score for a principal component analysis-defined "healthy" dietary pattern. Poor diet was categorized as a dietary pattern score in the lowest quarter of the distribution. Obesity was defined as a body mass index of 30.0 kg/m(2) or more. Physical function was assessed according to self-report (SF-36); poor function was defined as a score in lowest quarter of the distribution. A subgroup of participants had objective assessments of physical function (Timed Up-and-Go Test, timed 3-m walk, chair rises, one-legged standing balance). There was a graded increase in prevalence of poor self-reported physical function in men and women with increasing number of risk factors (men, adjusted odds ratio (AOR) for 3 or 4 risk factors vs none = 3.79, 95% confidence interval (CI) = 2.31-6.21; women, AOR = 5.37, 95% CI = 2.66-10.84). With the exception of balance, the objective assessments also showed graded relationships with number of risk factors, such that more risk factors was associated with poorer physical function. These modifiable lifestyle risk factors are linked to marked differences in risk of poorer physical function in older adults. Efforts to encourage healthy lifestyles have the potential to improve physical function and to promote healthier ageing.

Clustering of Lifestyle Risk Factors in a General Adult Population

Background. The objective of the study was to evaluate the degree of clustering of common lifestyle risk factors in a general adult population and to define subgroups with elevated clustering. Methods. Data on lifestyle risk factors (smoking, low vegetable and fruit consumption, excessive alcohol intake, and low physical activity), sociodemographics, and health perception were collected by questionnaire from 16,789 men and women aged 20 to 59. Results. About 20% of the subjects had at least three lifestyle risk factors. Prevalence of risk factors was higher among unemployed, low-educated subjects and those who had experienced health deterioration. All lifestyle risk factors showed significant clustering, except for low physical activity and excessive alcohol consumption. The strongest association was observed for alcohol and smoking (prevalence odds ratio (POR): 2.38; 95% confidence interval: 2.18–2.61). Clustering of smoking and alcohol consumption was strongest among the young subjects (POR: 3.78) and, although moderately, clustering of lifestyle risk factors was elevated in subjects who had experienced a deterioration in health. Conclusions. These findings suggest that common lifestyle risk factors cluster among adult subjects. The tendency for risk factors to aggregate has important implications for health promotion. Information on high-risk groups will help in planning future preventive strategies.

Clustering of Lifestyle CVD Risk Factors and Its Relationship with Biological CVD Risk Factors

The purpose of this study was (a) to investigate whether lifestyle risk factors cluster and (b) to investigate the influence of this clustering on biological CVD risk factors. This study was part of the Amsterdam Growth and Health Study (AGHS), an observational longitudinal study in which 6 repeated measurements were carried out on 181 13-year-old subjects over a period of 15 years. A longitudinal analysis (carried out with generalized estimating equations) showed no significant clustering of lifestyle risk factors at the population level. For each subject at each separate measurement period, lifestyle risk factors were summed to form a cluster score. A longitudinal linear regression analysis showed no significant relationship between the cluster score and biological CVD risk factors, except for a significant inverse relationship with cardiopulmonary fitness. In general, however, the results did not support the assumption that clustering of unhealthy lifestyle is related to biological CVD risk factors.