Antti Jula

The Special Turku Coronary Risk Factor Intervention Project (STRIP) is a prospective infancy-onset randomized dietary intervention trial targeting dietary fat quality and cholesterol intake, and favoring consumption of vegetables, fruit, and whole-grains. Diet (food records) and circulating metabolites were studied at six time points between the ages of 9–19 years (n = 549–338). Dietary targets for this study were defined as (1) the ratio of saturated fat (SAFA) to monounsaturated and polyunsaturated fatty acids (MUFA + PUFA) < 1:2, (2) intake of SAFA < 10% of total energy intake, (3) fiber intake ≥ 80th age-specific percentile, and (4) sucrose intake ≤ 20th age-specific percentile. Metabolic biomarkers were quantified by high-throughput nuclear magnetic resonance metabolomics. Better adherence to the dietary targets, regardless of study group allocation, was assoiated with higher serum proportion of PUFAs, lower serum proportion of SAFAs, and a higher degree of unsaturation of fatty acids. Achieving ≥ 1 dietary target resulted in higher low-density lipoprotein (LDL) particle size, lower circulating LDL subclass lipid concentrations, and lower circulating lipid concentrations in medium and small high-density lipoprotein subclasses compared to meeting 0 targets. Attaining more dietary targets (≥2) was associated with a tendency to lower lipid concentrations of intermediate-density lipoprotein and very low-density lipoprotein subclasses. Thus, adherence to dietary targets is favorably associated with multiple circulating fatty acids and lipoprotein subclass lipid concentrations, indicative of better cardio-metabolic health.

Systemic low-grade inflammation is associated with obesity. Our aim was to examine the association between obesity and salivary biomarkers of periodontitis. Salivary interleukin (IL)-1-receptor antagonist (IL-1Ra), IL-6, IL-8, IL-10, and tumor necrosis factor (TNF)-α concentrations were measured from 287 non-diabetic obese (body mass index (BMI) of >35 kg/m²) individuals and 293 normal-weight (BMI of 18.5–25 kg/m²) controls. Periodontal status was defined according to a diagnostic cumulative risk score (CRS) to calculate the risk of having periodontitis (CRS I, low risk; CRS II, medium risk; CRS III, high risk). In the whole population, and especially in smokers, higher IL-8 and lower IL-10 concentrations were detected in the obese group compared to the control group, while in non-smoking participants, the obese and control groups did not differ. IL-1Ra and IL-8 concentrations were higher in those with medium or high risk (CRS II and CRS III, p < 0.001) of periodontitis, whereas IL-10 and TNF-α concentrations were lower when compared to those with low risk (CRS I). In multivariate models adjusted for periodontal status, obesity did not associate with any salivary cytokine concentration. In conclusion, salivary cytokine biomarkers are not independently associated with obesity and concentrations are dependent on periodontal status.

Reduced nocturnal fall (non-dipping) of blood pressure (BP) is a predictor of cardiovascular target organ damage. No genome-wide association studies (GWAS) on BP dipping have been previously reported. To study genetic variation affecting BP dipping, we conducted a GWAS in Genetics of Drug Responsiveness in Essential Hypertension (GENRES) cohort (n = 204) using the mean night-to-day BP ratio from up to four ambulatory BP recordings conducted on placebo. Associations with P < 1 × 10− 5 were further tested in two independent cohorts: Haemodynamics in Primary and Secondary Hypertension (DYNAMIC) (n = 183) and Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic Syndrome (DILGOM) (n = 180). We also tested the genome-wide significant single nucleotide polymorphism (SNP) for association with left ventricular hypertrophy in GENRES. In GENRES GWAS, rs4905794 near BCL11B achieved genome-wide significance (β = − 4.8%, P = 9.6 × 10− 9 for systolic and β = − 4.3%, P = 2.2 × 10− 6 for diastolic night-to-day BP ratio). Seven additional SNPs in five loci had P values < 1 × 10− 5. The association of rs4905794 did not significantly replicate, even though in DYNAMIC the effect was in the same direction (β = − 0.8%, P = 0.4 for systolic and β = − 1.6%, P = 0.13 for diastolic night-to-day BP ratio). In GENRES, the associations remained significant even during administration of four different antihypertensive drugs. In separate analysis in GENRES, rs4905794 was associated with echocardiographic left ventricular mass (β = − 7.6 g/m2, P = 0.02). rs4905794 near BCL11B showed evidence for association with nocturnal BP dipping. It also associated with left ventricular mass in GENRES. Combined with earlier data, our results provide support to the idea that BCL11B could play a role in cardiovascular pathophysiology.

Body mass index (BMI) has been found to associate with different parameters of chronic periodontal disease in previous studies. It is reasonable to expect that central adiposity measures, such as waist circumference and waist-to-height ratio, which indirectly takes into account visceral fat, are more accurate measures of obesity-related oral health risks than BMI. The aim of this study was to examine whether central obesity is associated with periodontal pocketing, an indication of infectious chronic periodontal disease. The study was based on a subpopulation from the national Health 2000 Survey in Finland. It included dentate, non-diabetic, never-smoking subjects aged 30–49 (n = 1287). The outcome variable was the number of teeth with deepened periodontal pockets (4 mm or more) and the number of teeth with deep periodontal pockets (6 mm or more). Central obesity was measured by means of waist circumference (WC) and waist-to-height ratio (WHtR). Poisson regression models were used to estimate prevalence rate ratios (PRR) and their 95% confidence intervals. Our main finding was that both WC and WHtR were associated with the number of teeth with deeper (4 mm or more) periodontal pockets; the PRR for the fifth quintile in WC was 1.5, CI: 1.2–1.9 and in WHtR 1.4, CI: 1.1–1.7, when compared to the lowest quintile. Corresponding figures for deep (6 mm or more) periodontal pockets were 2.3, CI: 0.9–6.1 for WC and 1.9, CI: 0.8–4.4 for WHtR. There were no essential differences in the strengths of the associations between WC and WHtR and the number of teeth with deepened periodontal pockets. Both central adipose measures—WC and WHtR—seem to be associated with periodontal pocketing in non-diabetic, never-smoking subjects aged 30–49 years old.

Our aim is to describe the study recruitment and baseline characteristics of the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) study population. Potential study participants (age 60–77 years, the dementia risk score ≥6) were identified from previous population-based survey cohorts and invited to the screening visit. To be eligible, cognitive performance measured at the screening visit had to be at the mean level or slightly lower than expected for age. Of those invited (n = 5496), 48% (n = 2654) attended the screening visit, and finally 1260 eligible participants were randomized to the intervention and control groups (1:1). The screening visit non-attendees were slightly older, less educated, and had more vascular risk factors and diseases present. The mean (SD) age of the randomized participants was 69.4 (4.7) years, Mini-Mental State Examination 26.7 (2.0) points, systolic blood pressure 140.1 (16.2) mmHg, total serum cholesterol 5.2 (1.0) mmol/L for, and fasting glucose 6.1 (0.9) mmol/L for, with no difference between intervention and control groups. Several modifiable risk factors were present at baseline indicating an opportunity for the intervention. The FINGER study will provide important information on the effect of lifestyle intervention to prevent cognitive impairment among at risk persons.

Not all obese subjects have an adverse metabolic profile predisposing them to developing type 2 diabetes or cardiovascular disease. The BioSHaRE-EU Healthy Obese Project aims to gain insights into the consequences of (healthy) obesity using data on risk factors and phenotypes across several large-scale cohort studies. Aim of this study was to describe the prevalence of obesity, metabolic syndrome (MetS) and metabolically healthy obesity (MHO) in ten participating studies. Ten different cohorts in seven countries were combined, using data transformed into a harmonized format. All participants were of European origin, with age 18–80 years. They had participated in a clinical examination for anthropometric and blood pressure measurements. Blood samples had been drawn for analysis of lipids and glucose. Presence of MetS was assessed in those with obesity (BMI ≥ 30 kg/m2) based on the 2001 NCEP ATP III criteria, as well as an adapted set of less strict criteria. MHO was defined as obesity, having none of the MetS components, and no previous diagnosis of cardiovascular disease. Data for 163,517 individuals were available; 17% were obese (11,465 men and 16,612 women). The prevalence of obesity varied from 11.6% in the Italian CHRIS cohort to 26.3% in the German KORA cohort. The age-standardized percentage of obese subjects with MetS ranged in women from 24% in CHRIS to 65% in the Finnish Health2000 cohort, and in men from 43% in CHRIS to 78% in the Finnish DILGOM cohort, with elevated blood pressure the most frequently occurring factor contributing to the prevalence of the metabolic syndrome. The age-standardized prevalence of MHO varied in women from 7% in Health2000 to 28% in NCDS, and in men from 2% in DILGOM to 19% in CHRIS. MHO was more prevalent in women than in men, and decreased with age in both sexes. Through a rigorous harmonization process, the BioSHaRE-EU consortium was able to compare key characteristics defining the metabolically healthy obese phenotype across ten cohort studies. There is considerable variability in the prevalence of healthy obesity across the different European populations studied, even when unified criteria were used to classify this phenotype.