Lifelines Cohort Research Articles

Blood lipids-related dietary patterns derived from reduced rank regression are associated with incident type 2 diabetes

Blood lipids play a critical role in the pathogenesis of type 2 diabetes, and they are closely related to dietary factors. However, the associations between blood lipids-related dietary patterns and risk of type 2 diabetes are controversial and not fully clear. In this study, we aimed to derive dietary patterns that explained variation in blood lipids and to investigate their associations with incident type 2 diabetes. The analysis was based on 39,000 women and 25,777 men participating in the Lifelines cohort study (aged 18-65 years, mean 43.2 years for women and 43.5 years for men). Dietary intake was measured using a 110-item semi-quantitative food frequency questionnaire. Reduced rank regression was used to derive dietary patterns with blood lipids (HDL-cholesterol, LDL-cholesterol, triglycerides, total cholesterol, and total cholesterol:HDL-cholesterol ratio) as response variables for women and men separately. The first dietary pattern identified for each sex was selected because they explained the largest variance in blood lipids. The associations between the identified dietary patterns and incident type 2 diabetes were subsequently investigated using multivariate logistic regression models. All analyses were performed separately for women and men. During an average follow-up of 43 months, 479 new cases (incidence 0.74%) of type 2 diabetes were identified. Using reduced rank regression, we identified two sex-specific blood lipids-associated dietary patterns characterized by high intake of sugary beverages, added sugar, and low intake of vegetables, fruits, tea, and nuts/seeds. These two sex-specific dietary patterns were similar in food groups but differed in factor loadings. High dietary pattern scores were associated with increased risk of type 2 diabetes after adjustment for age, total energy intake, body mass index, waist-hip ratio, and blood pressure (ORs for the fifth quintile [Q5] using the first quintile [Q1] as reference, 1.87 [95% CI 1.23, 2.83] for women [P-trend<0.001], and 1.72 [95% CI 1.11, 2.66] for men [P-trend=0.018]). The associations were attenuated but remained significant after further adjustment for lifestyle and socio-economic factors. Dietary patterns associated with adverse blood lipids are associated with incidence of type 2 diabetes. The present study provides new insights in optimizing blood lipids for the prevention of type 2 diabetes through dietary approaches.

Persistent somatic symptoms after a SARS-CoV-2 infection: Long COVID in the Dutch Lifelines Cohort study

Persistent somatic symptoms after a SARS-CoV-2 infection: Long COVID in the Dutch Lifelines Cohort study

Prediction of Incident Cancers in the Lifelines Population-Based Cohort.

Simple SummaryThe accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. The main outcome was an incident cancer (excluding skin cancer) during follow-up assessment in a population-based cohort. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. An overall area under the receiver operator curve (AUC) < 0.75 was obtained; the highest AUC was for prostate cancer AUC > 0.80. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

Separate and combined effects of individual and neighbourhood socio-economic disadvantage on health-related lifestyle risk factors: a multilevel analysis.

BackgroundSocio-economic disadvantage at both individual and neighbourhood levels has been found to be associated with single lifestyle risk factors. However, it is unknown to what extent their combined effects contribute to a broad lifestyle profile. We aimed to (i) investigate the associations of individual socio-economic disadvantage (ISED) and neighbourhood socio-economic disadvantage (NSED) in relation to an extended score of health-related lifestyle risk factors (lifestyle risk index); and to (ii) investigate whether NSED modified the association between ISED and the lifestyle risk index.MethodsOf 77 244 participants [median age (IQR): 46 (40–53) years] from the Lifelines cohort study in the northern Netherlands, we calculated a lifestyle risk index by scoring the lifestyle risk factors including smoking status, alcohol consumption, diet quality, physical activity, TV-watching time and sleep time. A higher lifestyle risk index was indicative of an unhealthier lifestyle. Composite scores of ISED and NSED based on a variety of socio-economic indicators were calculated separately. Linear mixed-effect models were used to examine the association of ISED and NSED with the lifestyle risk index and to investigate whether NSED modified the association between ISED and the lifestyle risk index by including an interaction term between ISED and NSED.ResultsBoth ISED and NSED were associated with an unhealthier lifestyle, because ISED and NSED were both positively associated with the lifestyle risk index {highest quartile [Q4] ISED beta-coefficient [95% confidence interval (CI)]: 0.64 [0.62–0.66], P < 0.001; highest quintile [Q5] NSED beta-coefficient [95% CI]: 0.17 [0.14–0.21], P < 0.001} after adjustment for age, sex and body mass index. In addition, a positive interaction was found between NSED and ISED on the lifestyle risk index (beta-coefficient 0.016, 95% CI: 0.011–0.021, Pinteraction < 0.001), which indicated that NSED modified the association between ISED and the lifestyle risk index; i.e. the gradient of the associations across all ISED quartiles (Q4 vs Q1) was steeper among participants residing in the most disadvantaged neighbourhoods compared with those who resided in the less disadvantaged neighbourhoods.ConclusionsOur findings suggest that public health initiatives addressing lifestyle-related socio-economic health differences should not only target individuals, but also consider neighbourhood factors.

Prevalence, predictors, and outcomes of clonal hematopoiesis in individuals aged ≥80 years

AbstractClonal hematopoiesis (CH), characterized by a fraction of peripheral blood cells carrying an acquired genetic variant, emerges with age. Although in general CH is associated with increased mortality and morbidity, no higher risk of death was observed for individuals ≥80 years. Here, we investigated CH in 621 individuals aged ≥80 years from the population-based LifeLines cohort. Sensitive error-corrected sequencing of 27 driver genes at a variant allele frequency ≥1% revealed CH in the majority (62%) of individuals, independent of gender. The observed mutational spectrum was dominated by DNMT3A and TET2 variants, which frequently (29%) displayed multiple mutations per gene. In line with previous results in individuals ≥80 years, the overall presence of CH did not associate with a higher risk of death (hazard ratio, 0.91; 95% confidence interval, 0.70-1.18; P = .48). Being able to assess the causes of death, we observed no difference between individuals with or without CH, except for deaths related to hematological malignancies. Interestingly, comparison of mutational spectra confined to DNMT3A and TET2 vs spectra containing other mutated genes, showed a higher risk of death when mutations other than DNMT3A or TET2 were present (hazard ratio, 1.48; 95% confidence interval, 1.06-2.08; P = .025). Surprisingly, no association of CH with cardiovascular morbidity was found, irrespective of clone size. Further, CH associated with chronic obstructive pulmonary disease. Data on estimated exposure to DNA damaging toxicities (ie, smoking, a history of cancer [as a proxy for previous genotoxic therapy], and job-related pesticide exposure) showed an association with spliceosome and ASXL1 variants, but not with DNMT3A and TET2 variants.

Grandmaternal smoking, asthma and lung function in the offspring: the Lifelines cohort study

Background/objectiveLimited research exists regarding the association between grandmaternal smoking during pregnancy and the risk for asthma and altered lung function in grandchildren. This study aimed to investigate this association in...

Prevalence of internalizing disorders, symptoms, and traits across age using advanced nonlinear models.

Most epidemiological studies show a decrease of internalizing disorders at older ages, but it is unclear how the prevalence exactly changes with age, and whether there are different patterns for internalizing symptoms and traits, and for men and women. This study investigates the impact of age and sex on the point prevalence across different mood and anxiety disorders, internalizing symptoms, and neuroticism. We used cross-sectional data on 146 315 subjects, aged 18-80 years, from the Lifelines Cohort Study, a Dutch general population sample. Between 2012 and 2016, five current internalizing disorders - major depression, dysthymia, generalized anxiety disorder, social phobia, and panic disorder - were assessed according to DSM-IV criteria. Depressive symptoms, anxiety symptoms, neuroticism, and negative affect (NA) were also measured. Generalized additive models were used to identify nonlinear patterns across age, and to investigate sex differences. The point prevalence of internalizing disorders generally increased between the ages of 18 and 30 years, stabilized between 30 and 50, and decreased after age 50. The patterns of internalizing symptoms and traits were different. NA and neuroticism gradually decreased after age 18. Women reported more internalizing disorders than men, but the relative difference remained stable across age (relative risk ~1.7). The point prevalence of internalizing disorders was typically highest between age 30 and 50, but there were differences between the disorders, which could indicate differences in etiology. The relative gap between the sexes remained similar across age, suggesting that changes in sex hormones around the menopause do not significantly influence women's risk of internalizing disorders.

From evidence to practice: development of web-based Dutch lipid reference values.

IntroductionIn the Netherlands, the total number of yearly measured lipid profiles exceeds 500,000. While lipid values are strongly affected by age and sex, until recently, no up-to-date age- and sex-specific lipid reference values were available. We describe the translation of big-cohort lipid data into accessible reference values, which can be easily incorporated in daily clinical practice.MethodsLipid values (total cholesterol, LDL cholesterol, HDL cholesterol and triglycerides) from all healthy adults and children in the LifeLines cohort were used to generate age- and sex-specific percentiles. A combination of RStudio, Cascading Style Sheets and HyperText Markup Language was used to interactively display the percentiles in a responsive web layout.ResultsAfter exclusion of subjects reporting cardiovascular disease or lipid-lowering therapy at baseline, 141,611 subjects were included. On the website, input fields were created for age, sex and all main plasma lipids. Upon input of these values, corresponding percentiles are calculated, and output is displayed in a table and an interactive graph for each lipid. The website has been made available in both Dutch and English and can be accessed at www.lipidtools.com.ConclusionWe constructed the first searchable, national lipid reference value tool with graphical display in the Netherlands to use in screening for dyslipidaemias and to reduce the underuse of lipid-lowering therapy in Dutch primary prevention. This study illustrates that data collected in big-cohort studies can be made easily accessible with modern digital techniques and preludes the digital health revolution yet to come.

Understanding socioeconomic differences in incident metabolic syndrome among adults: What is the mediating role of health behaviours?

BackgroundThe incidence of metabolic syndrome (MetS) strongly varies by socioeconomic position (SEP), but little is known about the mediating role of health behaviours in this association. This study examines the associations between the SEP measures, education, income and occupational prestige, and incident MetS and whether the associations are mediated by health behaviours, including physical activity, smoking, alcohol intake and diet quality. MethodsA subsample (n = 85,910) of the adult Lifelines Cohort Study without MetS at baseline was used. MetS was measured at the second assessment (median follow-up time 3.8 years) defined according to the NCEP-ATPIII criteria. Direct associations between SEP, health behaviours and incident MetS were estimated using multivariable logistic regression analyses. The mediating percentages of health behaviours explaining the associations between SEP and incident MetS were estimated using the Karlson-Holm-Breen method. Analyses were independent of age, sex, the other SEP measures and follow-up time. ResultsEducation and occupational prestige were inversely associated with MetS. Income was not associated with MetS. Health behaviours explained only partly (13.8%) the association between education and MetS, with smoking as the strongest mediating factor (8.8%). Health behaviours played also a minor role (2.7%) in explaining occupational MetS differences, with physical activity as the strongest suppressing factor (−9.4%). ConclusionIndividuals with more years of education or a higher occupational prestige had a lower risk to develop MetS. This was mainly because of non-smoking, less excessive alcohol intake and a higher diet quality. However, individuals with a higher SEP were more often physically inactive.

Geriatric Syndromes and Incident Chronic Health Conditions Among 9094 Older Community-Dwellers: Findings from the Lifelines Cohort Study.

To determine the association between geriatric syndromes and any specific incident chronic health conditions among older community-dwellers. Population-based cohort study over a median follow-up period of 43months. Participants from the Lifelines Cohort Study aged 60years and older without presence of the studied chronic health conditions at baseline (n=9094). Baseline assessment took place between November 2006 and December 2013 and included information on socioeconomic (age, sex, level of education and income), social contact, and health-related factors [eg, self-rated health, body mass index, chronic health conditions, and health behavior (alcohol consumption and smoking)]. Participants also reported the presence of geriatric syndromes (ie, included falls, incontinence, vision impairment, hearing impairment, depressive symptoms, and frailty at baseline). Three follow-up questionnaires were used to examine the incidence of any and specific chronic health conditions (ie, pulmonary and cardiovascular diseases, diabetes, cancer, and neurological diseases). Cox regression was used to analyze the longitudinal associations between geriatric syndromes and incident chronic health conditions. Older community-dwelling individuals with at least one geriatric syndrome (44.7%, n=4038) had an increased risk of developing any new chronic health condition [hazard ratio (HR) 1.35; 95% confidence interval (CI) 1.21-1.51]. The association was attenuated but remained significant after adjustment for socioeconomic factors, social contact, health status, and health behavior (HR 1.27; 95% CI 1.12-1.43). Analyses for specific chronic health conditions showed that compared with older community-dwellers without geriatric syndromes, those with geriatric syndromes had an increased risk to develop a cardiovascular health condition (HR 1.42; 95% CI 1.13-1.79) or diabetes (HR 1.53; 95% CI 1.11-2.11). They had no increased risk to develop pulmonary conditions, cancer, or neurological conditions. The presence of geriatric syndromes is associated with incident chronic health conditions, specifically cardiovascular conditions and diabetes. Increased awareness is needed among older people with geriatric syndromes and their physicians. Comprehensive assessments of geriatric syndromes may help to prevent or at least delay the development of chronic health conditions.

Data-driven assessment, contextualisation and implementation of 134 variables in the risk for type 2 diabetes: an analysis of Lifelines, a prospective cohort study in the Netherlands

Aims/hypothesisWe aimed to assess and contextualise 134 potential risk variables for the development of type 2 diabetes and to determine their applicability in risk prediction.MethodsA total of 96,534 people without baseline diabetes (372,007 person-years) from the Dutch Lifelines cohort were included. We used a risk variable-wide association study (RV-WAS) design to independently screen and replicate risk variables for 5-year incidence of type 2 diabetes. For identified variables, we contextualised HRs, calculated correlations and assessed their robustness and unique contribution in different clinical contexts using bootstrapped and cross-validated lasso regression models. We evaluated the change in risk, or ‘HR trajectory’, when sequentially assigning variables to a model.ResultsWe identified 63 risk variables, with novel associations for quality-of-life indicators and non-cardiovascular medications (i.e., proton-pump inhibitors, anti-asthmatics). For continuous variables, the increase of 1 SD of HbA1c, i.e., 3.39 mmol/mol (0.31%), was equivalent in risk to an increase of 0.53 mmol/l of glucose, 19.8 cm of waist circumference, 8.34 kg/m2 of BMI, 0.67 mmol/l of HDL-cholesterol, and 0.14 mmol/l of uric acid. Other variables required an increase of >3 SD, which is not physiologically realistic or a rare occurrence in the population. Though moderately correlated, the inclusion of four variables satiated prediction models. Invasive variables, except for glucose and HbA1c, contributed little compared with non-invasive variables. Glucose, HbA1c and family history of diabetes explained a unique part of disease risk. Adding risk variables to a satiated model can impact the HRs of variables already in the model.ConclusionsMany variables show weak or inconsistent associations with the development of type 2 diabetes, and only a handful can reliably explain disease risk. Newly discovered risk variables will yield little over established factors, and existing prediction models can be simplified. A systematic, data-driven approach to identify risk variables for the prediction of type 2 diabetes is necessary for the practice of precision medicine.Graphical abstract

How is sport participation related to mortality, diabetes and prediabetes for different body mass index levels?

This study examined the association of sport participation with health outcomes and whether this relation differs between body mass index (BMI)‐level subpopulations. Research outcomes for sport participation were compared with other types of leisure‐time physical activity (PA). We used the Cox proportional hazards regression models to assess the associations of sport participation, and four other PA types (cycling, gardening, doing odd jobs, and walking), with the risk of prediabetes, type 2 diabetes mellitus (T2DM), and all‐cause mortality in 97,212 individuals (58.4% women; mean age: 46.5 years) in the Dutch LifeLines cohort. Outcomes were stratified by three BMI levels: healthy weight (BMI: 18.5‐24.9 kg/m2), overweight (BMI: 25.0‐29.9 kg/m2), and obesity (BMI: 30.0 kg/m2 or above). Sport participation was associated with lower health risks, but only significantly so for prediabetes (HR = 0.86, 95% CI: 0.81‐0.92). For healthy weight persons, sport participation was associated with the largest risk reductions, with significantly lower risks of prediabetes (HR = 0.78, 95% CI: 0.68‐0.90) and all‐cause mortality (HR = 0.79, 95% CI 0.65‐0.96). Other PA types were not associated with significantly lower health risks, with the exception of cycling, for which significantly lower health risks for persons with overweight were found. Our findings show that sport participation is associated with lower health risks, especially prediabetes, but the effect varies between BMI levels, with the strongest link for persons with a healthy weight. Sport participation, together with cycling, is likely to be more effective in reducing health risks than other types of PA.

Association between socioeconomic status and self-reported, tested and diagnosed COVID-19 status during the first wave in the Northern Netherlands: a general population-based cohort from 49 474 adults

ObjectivesStudies in clinical settings showed a potential relationship between socioeconomic status (SES) and lifestyle factors with COVID-19, but it is still unknown whether this holds in the general population. In...

Course of frailty stratified by physical and mental multimorbidity patterns: a 5-year follow-up of 92,640 participants of the LifeLines cohort study

BackgroundThe frailty index (FI) is a well-recognized measurement for risk stratification in older people. Among middle-aged and older people, we examined the prospective association between the FI and mortality as well as its course over time in relation to multimorbidity and specific disease clusters.MethodsA frailty index (FI) was constructed based on either 64 (baseline only) or 35 health deficits (baseline and follow-up) among people aged ≥ 40 years who participated in LifeLines, a prospective population-based cohort living in the Northern Netherlands. Among 92,640 participants, multivariable Cox proportional hazard models were fitted to study the hazard ratio (HR) of the FI at baseline, as well as for 10 chronic disease clusters for all-cause mortality over a 10-year follow-up. Among 55,426 participants, linear regression analyses were applied to study the impact of multimorbidity and of specific chronic disease clusters (independent variables) on the change of frailty over a 5-year follow-up, adjusted for demographic and lifestyle characteristics.ResultsThe FI predicted mortality independent of multimorbidity and specific disease clusters, with the highest impact in people with either endocrine, lung, or heart diseases. Adjusted for demographic and lifestyle characteristics, all chronic disease clusters remained independently associated with an accelerated increase of frailty over time.ConclusionsFrailty may be seen as a final common pathway for premature death due to chronic diseases. Our results suggest that initiating frailty prevention at middle age, when the first chronic diseases emerge, might be relevant from a public health perspective.

Ambient air pollution exposure and chronic bronchitis in the Lifelines cohort

BackgroundFew large studies have assessed the relationship of long-term ambient air pollution exposure with the prevalence and incidence of symptoms of chronic bronchitis and cough.MethodsWe leveraged Lifelines cohort data on...

Alternative pediatric metabolic syndrome definitions impact prevalence estimates and socioeconomic gradients.

There is no consensus regarding the definition of pediatric metabolic syndrome (MetS). This study assessed the impact of alternative definitions on the prevalence, children identified, and association with socioeconomic status (SES). Data were from the prospective multigenerational Dutch Lifelines Cohort Study. At baseline, 9754 children participated, and 5085 (52.1%) with average follow-up of 3.0 (SD = 0.75) years were included in the longitudinal analyses; median ages were 12 (IQR = 10-14) and 14 years (IQR = 12-15), respectively. We computed MetS prevalence according to five published definitions and measured the observed proportion of positive agreement. We used logistic regression to assess the SES-MetS association, adjusted for age and sex. Longitudinal models were also adjusted for baseline MetS. MetS prevalence and positive agreement varied between definitions, from 0.7 to 3.0% and from 0.34 (95% CI: 0.28; 0.41) to 0.66 (95% CI: 0.58; 0.75) at baseline, respectively. We consistently found a socioeconomic gradient; in the longitudinal analyses, each additional year of parental education reduced the odds of having MetS by 8% (95% CI: 1%; 14%) to 19% (95% CI: 7%; 30%). Alternative MetS definitions had differing prevalence estimates and agreed on 50% of the average number of cases. Additionally, regardless of the definition, low SES was a risk factor for MetS. Little is known about the impact of using different definitions of pediatric metabolic syndrome on study results. Our study showed that the choice of pediatric metabolic syndrome definition produces very different prevalence estimates. We also showed that the choice of definition influences the socioeconomic gradient. However, low socioeconomic status was consistently a risk factor for having pediatric metabolic syndrome. In conclusion, studies using different definitions of metabolic syndrome could be reasonably compared when investigating the association with socioeconomic status but not always validly when comparing prevalence studies.

Skin autofluorescence predicts new cardiovascular disease and mortality in people with type 2 diabetes

BackgroundSkin autofluorescence (SAF) is a non-invasive marker of tissue accumulation of advanced glycation endproducts (AGE). Recently, we demonstrated in the general population that elevated SAF levels predict the development of type 2 diabetes (T2D), cardiovascular disease (CVD) and mortality. We evaluated whether elevated SAF may predict the development of CVD and mortality in individuals with T2D.MethodsWe included 2349 people with T2D, available baseline SAF measurements (measured with the AGE reader) and follow-up data from the Lifelines Cohort Study. Of them, 2071 had no clinical CVD at baseline. 60% were already diagnosed with diabetes (median duration 5, IQR 2–9 years), while 40% were detected during the baseline examination by elevated fasting blood glucose ≥7.0 mmol/l) and/or HbA1c ≥6.5% (48 mmol/mol).ResultsMean (±SD) age was 57 ± 12 yrs., BMI 30.2 ± 5.4 kg/m2. 11% of participants with known T2D were treated with diet, the others used oral glucose-lowering medication, with or without insulin; 6% was using insulin alone. Participants with known T2D had higher SAF than those with newly-detected T2D (SAF Z-score 0.56 ± 0.99 vs 0.34 ± 0.89 AU, p < 0.001), which reflects a longer duration of hyperglycaemia in the former group. Participants with existing CVD and T2D had the highest SAF Z-score: 0.78 ± 1.25 AU. During a median follow-up of 3.7 yrs., 195 (7.6%) developed an atherosclerotic CVD event, while 137 (5.4%) died. SAF was strongly associated with the combined outcome of a new CVD event or mortality (OR 2.59, 95% CI 2.10–3.20, p < 0.001), as well as incidence of CVD (OR 2.05, 95% CI 1.61–2.61, p < 0.001) and death (OR 2.98, 2.25–3.94, p < 0.001) as a single outcome. In multivariable analysis for the combined endpoint, SAF retained its significance when sex, systolic blood pressure, HbA1c, total cholesterol, eGFR, as well as antihypertensive and statin medication were included. In a similar multivariable model, SAF was independently associated with mortality as a single outcome, but not with incident CVD.ConclusionsMeasuring SAF can assist in prediction of incident cardiovascular disease and mortality in individuals with T2D. SAF showed a stronger association with future CVD events and mortality than cholesterol or blood pressure levels.

Prevalence of sarcopenic obesity and sarcopenic overweight in the general population: The lifelines cohort study

Sarcopenic obesity (SO) is defined by a relatively low muscle mass in combination with obesity. Sarcopenic obesity was first noted as a health risk in geriatric populations but has recently been recognized as a scientific and clinical priority that may extend beyond geriatric settings. Obesity is generally preceded by overweight, so the prevalence and health risks of sarcopenia in those with overweight (SOW) is of interest for preventive purposes. The aim of this study, therefore, was to assess the prevalence and determinants of SO and SOW in a general population. Participants (n=119,494), aged 18-90 years were included from the Dutch Lifelines cohort study. Muscle mass was assessed by 24-h urine creatinine excretion and stratified for gender for analysis, and obesity was defined as a Body Mass Index (BMI) ≥30kg/m2 and overweight ≥25kg/m2. Multivariate logistic regression models were applied to assess the relevant determinants of SO and SOW. Respectively for men and women the prevalence of SO was 0.9% and 1.4%, and prevalence of SOW 6.5% and 6.0%. In subjects with sarcopenia, BMI was ≥25kg/m2 in 45.5% and ≥30kg/m2 in 6.1%. Overall females had a higher prevalence of SOW and SO in all age groups except for SOW in males between ages 40-59. Also, age was a significant determinant of SO and SOW, with a rise in prevalence as of age 50. Of all subjects with SO and SOW, respectively 82.5% and 80.4% were below the age of 70. Compared to those with no morbidities, the odds ratio of SO and SOW among participants with >3 comorbidities was 2.71 (95% CI: 1.62-4.54) and 1.33 (95% CI: 1.07-1.65) among males and 1.14 (95% CI: 0.79-1.65) and 1.28 (95% CI: 1.06-1.54) among females, independent of other determinants. Overall, an inverse association was found between SOW and SO and physical activity and macronutrient intake. The results support the need for more awareness of SO beyond the field of geriatrics, in particular in subjects with comorbidities. SOW is more prevalent than SO and may provide opportunities for preventive strategies for the general population.

Genetic pre-screening for glaucoma in population-based epidemiology: protocol for a double-blind prospective screening study within Lifelines (EyeLife)

BackgroundEarly detection of glaucoma is paramount to maintain patients’ eyesight, however glaucomatous vision loss tends to begin in the periphery with up to 50% of patients unaware they are affected. Because glaucomatous vision loss is permanent, screening appears attractive, but currently is not cost-effective. Therefore we aim to investigate the utility of genetic pre-screening for glaucoma in a population-based setting, called EyeLife.MethodsEyeLife adopts a double blind prospective design with contrasting groups. Selected participants (n = 1600) from the Lifelines cohort are 55 years of age or older, and of either the highest or lowest 20% of the genetic risk distribution for glaucoma. We obtained a highly curated list of genetic variants from the literature to obtain each participants’ genetic risk for glaucoma. Participants will undergo comprehensive ophthalmic screening. The primary outcome is the relative risk of glaucoma given a high genetic risk compared to a low genetic risk.DiscussionIf genetic pre-screening is successful, it will increase the yield of a glaucoma screening program by focusing on high-risk individuals. This, in turn, may improve long-term visual health of middle-aged and elderly people.Trial registrationEthics approval was obtained on January 31, 2019, and the study was retrospectively registered with the Netherlands Trial Register (NL8718) on the 17th of June, 2020.

The relationship between dry eye and sleep quality

PurposeSleep is an important determinant of health and quality of life. This study aimed to clarify the association between dry eye and sleep quality using a large population-based cohort. Methods71,761 participants (19–94 yrs, 59.4% female) from the Lifelines cohort in the Netherlands were assessed for dry eye using the Women's Health Study Dry Eye Questionnaire. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Logistic regression was used to examine the relationship between poor sleep quality (PSQI score >5) and dry eye, while correcting for age, sex, BMI, education, income, and 51 possible confounding comorbidities, including autoimmune diseases and psychiatric disorders. ResultsOverall, 8.9% of participants had dry eye. Of these, 36.4% had poor sleep quality compared to 24.8% of controls (OR 1.52 (95%CI 1.44–1.60), P < 0.0001, corrected for age and sex). After correcting for all comorbidities, dry eye was still associated with poor sleep (OR 1.20 (95%CI 1.11–1.28), P < 0.0001). This relationship was seen across all ages and sexes. Patients with dry eye scored worse on all subcomponents of the PSQI. Almost one-in-two (44.9%) persons with dry eye symptoms “often” or “constantly” had poor sleep quality. This proportion was similar to participants with sleep apnea and osteoarthritis. Additionally, increasing symptom frequency was tied to increased prevalence of poor sleep quality. ConclusionsAll components of sleep quality were significantly reduced in participants with dry eye, even after correcting for comorbidities. These results indicate the substantial impact of dry eye on patients' lives, especially for those with frequent symptoms.