Tag: cohort

Body composition and physical fitness in adults born small for gestational age at term: a prospective cohort study

Research design and style

This research is a portion of the NTNU Small Beginning Weight in a Lifetime Perspective analyze. The current review included two teams of adults born in 1986–1988 one particular group born SGA at phrase, and a person team born non-SGA at phrase with start weight ≥ 10th percentile, which serves as a management team. The individuals took component in a more substantial knowledge assortment at 32 years of age. In addition to bodily health and fitness tests, examinations bundled anthropometric measurements, evaluation of lung operate, visible perform as very well as fine and gross motor perform. Assessments were being carried out from September 2019 to Oct 2020.

Members

Members ended up originally integrated in a multicentre study investigating the aetiology and outcomes of intrauterine development restriction29,30. Pregnant gals living in the Trondheim location were enrolled before week 20 of being pregnant primarily based on referral from standard practitioners and obstetricians. Ladies had been suitable if they experienced a singleton being pregnant and experienced been expecting just one or two moments just before (n = 1249). A 10{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} random sample of these girls were being chosen to serve as a regulate team (n = 132), employing a sealed envelope strategy. A team of females at large possibility of supplying birth to an SGA infant were chosen for adhere to-up if they experienced a person or additional outlined chance criteria for SGA birth a former low delivery weight boy or girl, reduced pre-pregnancy weight (< 50 kg), previous perinatal death, presence of chronic maternal disease (chronic renal disease, essential hypertension, or heart disease), or maternal cigarette smoking at conception (n = 390). Women in the control group and the high-risk group were thoroughly followed during pregnancy and their infants were examined at birth. The rest of the women (n = 727) were not followed during pregnancy (Fig. 1).

Figure 1
figure 1

Flow of participants. SGA small for gestational age.

At birth, all SGA infants born to mothers in either group were included in the SGA group (Fig. 1). An infant was defined as being born SGA if the birth weight was < 10th percentile for gestational age (GA), corrected for sex and parity, according to a reference standard using data from the Norwegian Medical Birth Registry29. Non-SGA infants born to mothers in the random sample were included in the control group. They were born with a birth weight ≥ 10th percentile. GA was based on the first day of the mother’s last menstrual period if this was accurately recalled ± 3 days. Ultrasound based GA was used if the last menstrual period was not recalled, or if there was a discrepancy of more than 14 days. Both groups were born at term (GA ≥ 37 weeks)29,30.

The total sample included 104 participants born SGA and 120 controls (Fig. 1). Three individuals born SGA and two controls were excluded due to death, congenital syndrome/anomaly, or multimorbidity. Of the eligible, 15 individuals born SGA and 14 controls were not invited because they were living abroad, had no contact information or had previously refused to participate. Thus, a total of 190 were invited to the present study, 86 in the SGA group and 104 in the control group. Of these, 30 individuals born SGA and 36 controls did not consent to participate. Furthermore, 10 individuals born SGA and seven controls were not assessed clinically. Thus, 46 participants born SGA and 61 controls were assessed clinically, corresponding to 56.3{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of the invited.

Non-participants

There were no significant differences between participants and those who did not consent or were not assessed clinically regarding sex, gestational age, birth weight, head circumference, body length, ponderal index, maternal age at child’s birth or parental socioeconomic status (SES) in either group (data not shown). From the 26-year follow-up data were available on height, weight, BMI, waist and hip circumference, skinfold thickness and body composition measured by dual-energy x-ray absorptiometry (DXA). In the SGA group there were no differences, but in the control group, participants weighed 8.9 (95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI 1.0 to 16.8) kg less than those who did not consent or were not assessed clinically.

Background characteristics

At birth, the infants in both groups were weighed to the nearest 10 g on a standard scale, and crown-heel length was measured with both legs extended to the nearest mm30. Ponderal index (g/cm3) was calculated based on these measurements.

Parental socioeconomic status (SES) was calculated when participants attended the 14-year follow-up, supplemented for two participants at the 19-year follow-up, according to Hollingshead’s Two Factor Index of Social Position31, based on the parents’ education and occupation. This gives a social class rating from 1 (lowest) to 5 (highest).

Educational attainment at the 32-year follow-up was collected by self-report and classified according to the International Standard Classification of Education (ISCED) levels 1 through 8. These were recoded into three categories: Lower secondary education or lower (ISCED levels 1–2) as no more than 10th class level, intermediate education (ISCED levels 3–5) as 11th–14th class level, and lower tertiary education or higher (ISCED levels 6–8) as a bachelor’s degree or higher.

Outcome measures

Assessments were carried out at NTNU/St. Olavs Hospital in Trondheim, Norway. A brief medical interview was conducted prior to examination, including whether the participant was pregnant, had a musculoskeletal diagnosis or other conditions affecting physical functioning. If the participant had a condition that made them unable to perform a physical test or that could be worsened by testing, they did not perform that particular test. All examinations were carried out by experienced and specially trained examiners, blinded to birth weight group. Anthropometric measurements were performed by a nurse and physical fitness tests by two physiotherapists and a medical research student. The examinations were carried out in the same order for each participant.

At follow-up, the participants’ height, waist and hip circumference were measured to the nearest mm. Waist circumference was measured at the mid-point between the lowest rib and the crista iliaca, and hip circumference at the maximal circumference over the buttocks. Weight was measured by bioelectric impedance analysis using a Seca medical Body Composition Analyzer (Seca® mBCA 515) with a 100 g accuracy. Body mass index (BMI, kg/m2) and waist-to-hip ratio (waist circumference/hip circumference) was calculated. Bioelectrical impedance analysis measures included percent body fat, fat mass, fat free mass, skeletal muscle mass, total body water and extracellular water using the Seca 115 analytics software (Seca GmbH, Hamburg, Germany).

Muscular fitness was measured by the maximal isometric grip strength of the hands and forearm muscles. A Jamar (Smith and Nephew, Memphis, TN) hand dynamometer was used. The dynamometer has 5 handle positions position 3 and 4 were used for women and men, respectively. The participants were seated during the test, with shoulder abducted, a 90° angle in the elbow and a neutral position in the wrist, without support of the forearm32. Measurement was repeated three times in both dominant and non-dominant hand with 30 s recovery in between each attempt. Grip strength was measured in kg force and the maximal grip strength of the three measurements for each hand was used in the analysis. One participant in the control group could not perform the grip strength test with the dominant hand due to a hand fracture.

The 40-s modified push-up test measures the muscular strength and endurance capacity of the upper body33 and is modified to improve standardisation. The participants started laying prone on a mat with their hands close to the shoulders and feet hip-width apart with their toes on the mat33. Before every push-up they had to clasp hands behind their back before pushing themselves to a straight leg push-up. In the top position they had to touch either of their hands with the other hand before returning to the push-up position and returning to the down-position. The number of correctly performed push-ups in 40 s were registered. One participant in the control group could not perform the push-up test due to a hand fracture.

The Åstrand-Ryhming step test is a 4-min submaximal step-test that measures cardiorespiratory fitness34. The participants stepped on and off the step for four minutes paced by a metronome set to 46 beats per minute (i.e., 23 times up on the step/min). The height of the step was adapted to sex: 33 cm for women and 40 cm for men. Heart rate was observed during the test using a heart rate monitor (Firstbeat Technologies Oy) and recorded after 4 min of stepping and after being seated for 2 min. Two participants born SGA were not able to complete the test and were excluded from the analysis.

Statistical analysis

The analyses were conducted in SPSS version 27 (IBM Statistics). A p-value of less than 0.05 was considered statistically significant. Background characteristics were examined using Student’s t-test for continuous data, Exact Mann–Whitney U test for ordinal data and Pearson’s Chi square test for dichotomous variables. Group differences in outcome measures were analysed using independent samples t-test. The assumption of normally distributed variables was checked by visual inspection of histogram, boxplot, and Q–Q-plots of standardised residuals. As physical fitness differs between women and men35,36, we performed separate analyses by sex. Differences in physical fitness between groups were adjusted for height as a potential mediating factor in a univariate general linear model, since height has been consistently correlated with both being born SGA4,12,37 and physical fitness in previous literature22.

To investigate whether physical conditions affected the results, sensitivity analyses were performed by excluding participants who were pregnant, had a musculoskeletal diagnosis or other conditions affecting physical functioning, as reported by the participants in the brief medical interview.

A priori power calculations suggested, based on previous follow-up numbers in the SGA (n = 64) and control group (n = 81)38, that we would have the power to detect differences of 0.48 SD units with an alpha-level of 0.05 and a power of 80{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}, and 0.67 SD units with an alpha-level of 0.01 and desired power of 90{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}.

Ethics

The study was approved by the Regional Committee for Medical and Health Research Ethics in Central Norway (23879). Written informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations. The data was pseudonymised and stored securely on a remote server with a two-step identifier. All methods were non-invasive and entailed low risk for injury or adverse events. An appointed doctor was medically responsible during data collection. Participants in need of health services were referred as appropriate.

Associations of timing of physical activity with all-cause and cause-specific mortality in a prospective cohort study

Participants and accelerometer assessment

This large cohort study was conducted based on the UK Biobank. The UK Biobank received ethical approval from the North West Multi-center Research Ethics Committee to collect and distribute samples and data from the participants (Reference numbers: 16/NW/0274& 21/NW/0157; https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/governance/ethics-advisory-committee), which covers the work in this study under approved Application 58082. All UK Biobank participants provided informed consent. In addition, we have obtained approvals from our institutions for analyzing the UK Biobank data in this study. UK Biobank is a large population-based cohort that recruited over 500,000 participants aged 40–73 years between 2006 and 201038. Participants visited one of 22 assessment centers across England, Scotland, and Wales, and underwent detailed baseline assessments, including various sociodemographic, lifestyle, health, and physical assessments. Details of the rationale, design, and measurements for the UK Biobank are available online (www.ukbiobank.ac.uk). Between February 2013 and December 2015 (on average, approximately 5.5 years after their initial baseline recruitment), 236,519 UK Biobank participants were invited to participate in an accelerometer study. Among them, 106,053 participants agreed to participate and were provided with a wrist-worn accelerometer (Axivity AX3)39. Participants who accepted accelerometry measurement showed similar baseline demographic and health-related characteristics as those who declined the measurement40. The accelerometer was set up to start at 10 a.m. two working days after postal dispatch (to ensure that the accelerometer would not start recording during delivery), and capture triaxial acceleration data over 7 days at 100 Hz with a dynamic range of ±8 gravity. Participants were instructed to wear the device on their dominant wrist continuously for seven days while continuing with their usual activities. Participants were asked to mail the device in a pre-paid envelope back to the coordinating centers, after the seven-day monitoring period.

Using the raw accelerometer data from 103,682 participants, the UK Biobank accelerometer expert working group conducted data processing and generated physical activity intensity data (average vector magnitude in milligravity units) in 5-s epochs (field ID 90004) using the raw accelerometer data (field ID 90001). The raw acceleration signals were calibrated to gravity. Non-wear time was defined as consecutive stationary episodes lasting for at least one hour where all three axes had a standard deviation of less than 13.0 milligravity39. Epochs representing non-wear time were imputed based on all wear-time data at a similar time of the day on different days for each participant. More details about the data processing and analysis have been published39.

The exclusion criteria are as follows: (1) those who withdrew from UK Biobank; (2) those who had no PA data in any one hour of the 24-h cycle; (3) Similar to the previous study20, those who had high nocturnal activity (>10{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} PA accumulated between 01:00 and 04:00), as we focused on individuals with a diurnal lifestyle; (4) those with unreliable or invalid accelerometry data. The criteria for unreliable or invalid accelerometry data included (1) unexpectedly small or large size (Field ID: 90002); (2) less than 72 h or did not provide data for all 1-h periods within a 24-h cycle during the 7-day data collection (Field ID: 90015); (3) not well-calibrated (Field ID: 90016); (4) recalibrated using the previous accelerometer record from the same device worn by a different participant (Field ID: 90017); (5) data with a non-zero count of interrupted recording periods (Field ID: 90180); (6) data with more than 768 (Q3 + 1.5 × IQR) data recording errors (Field ID: 90182). In total 11,543 participants were excluded. Finally, 92,139 participants (88.87{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}) with valid data were included in the current study for the main analysis with the imputation of missing data, while 89,141 participants with the complete set of data were included for sensitivity analysis (Supplementary Fig. 4).

Exposure

The processed activity intensity data were further used to yield MVPA. MVPA, often defined as requiring a moderate to a large amount of effort and with a notable to substantial acceleration in heart rate, is a well-validated surrogate for PA40. More importantly, as suggested by the previous research20, focusing on high-intensity levels of PA, such as MVPA, helps to determine a clear timing effect. Light-intensity PA was not included in this study because it occurs during walking and even sitting hours, thereby obscuring the temporal distribution of the more effective PA with higher intensity20. In this study, we tried to generate PA timing grouping using the averaged acceleration data, and only 0.74{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} (n = 680), 10.1{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} (n = 9316), and 0.70{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} (n = 641) of the participants were assigned to the morning, midday-afternoon, and evening group, respectively. The remaining 88.5{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} (n = 81,502) of the participants were assigned to the mixed group. Therefore, similar to previous research20, we focused on PA at a relatively high-intensity level (i.e., MVPA) to determine a robust timing phenotype.

Moderate-intensity physical activity was collected in sessions (5-min periods where more than 80{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of 5-s epochs had a mean acceleration of 100 to 400 milligravity)40. Vigorous-intensity physical activity was defined as the 5-s epochs, where the mean acceleration was above 400 milligravity41. We included individuals with a diurnal lifestyle. Furthermore, high nocturnal activity always means sleep disturbances. Due to these reasons, we calculated the total minutes of MVPA by summing the minutes of moderate-intensity physical activity and vigorous-intensity physical activity between 05:00 and 24:00. Among 8354 (9.07{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}) participants who provided <1 week of accelerometry data, we extrapolated MPVA data to seven days.

Then, we ran the exploratory analyses to determine the appropriate boundaries for the time windows used for the main analyses. Exposure-dependent methods, such as the equally spaced intervals (which do not appear to be clinically driven), are generally arbitrary and may not be helpful in assessing a variable’s actual predictive value27. In contrast, the outcome-based methods allow an “optimal” cutoff to be estimated42,43. Therefore, this study ran an exploratory analysis for the identification of time window boundaries using the ‘outcome-based’ method. In addition, to balance sample size and accuracy, we chose the 2-h time window intervals (3-h only for the 21:00–24:00 period) in the exploratory analyses. The 50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} method of assigning timing groups was similar to that previously used20, and this method avoids the participants being assigned to multiple timing groups. If over 50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of total daily MVPA occurred during the same 2-h period, participants would be assigned to the corresponding groups. For those who spent less than 50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of the total daily MVPA in any of the 2-h time windows, we assigned them to the mixed group. As shown in Supplementary Fig. 5, which mimics the nonlinear exposure-outcome curves, two change points at 11:00 and 17:00 were consistently observed for all mortality outcomes. Compared with the mixed group, the 2-h timing groups of the morning (05:00–11:00) and evening (17:00–24:00) periods seemed to have higher mortality risks. The 2-h timing groups of the midday to afternoon period (11:00–17:00) presented comparable mortality risks with the mixed group (Supplementary Fig. 5). Finally, morning (05:00–11:00), midday to afternoon (11:00–17:00), and evening (17:00–24:00) time windows were used in subsequent MVPA timing grouping and statistical analyses.

Similar to the previous study20, if ≥50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of total daily MVPA occurred during the same time window, participants would be assigned to the corresponding MVPA timing groups: morning (05:00–11:00), midday-afternoon (11:00–17:00), and evening (17:00-24:00) groups. For those who spent <50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of the total daily MVPA in any of three time windows, we assigned them to the mixed group.

Outcomes

The outcomes were all-cause, CVD, and cancer mortality. Cause-specific mortality was ascertained using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (Supplementary Table 20). We measured specific mortality due to CVDs (codes I00–I99) and cancer (codes C00-C97) using the death registry. The date and cause of death were obtained from the death datasets of the National Health Service Information Center and the NHS Central Register. At the time of analysis, we censored the Cox regression analyses at the date of death or the date of available mortality data (12 November 2021), whichever came first.

Covariates

Data on these possible covariates were obtained using self-reported questionnaires, accelerometers, and registry records. Age (continuous) was calculated from the date of birth and the date of wearing the accelerometer. Self-reported questionnaires were used to determine sex (female/male), ethnicity (white/others), recruitment center (England/Wales/Scotland), and Townsend deprivation index (continuous) based on the postcode of residence using aggregated data on unemployment, car and home ownership, and household overcrowding. The data on the season of accelerometry wear (i.e., spring, March to May; summer, June to August; autumn, September to November; winter, for December to February; UK Meteorological Office definitions) was obtained from accelerometer data. Other covariate data including educational attainment (degree or above/any other qualification/no qualification), smoking status (never/previous/current), frequency of alcohol intake (not current/less than three times a week/three or more times a week), and diet-related factors were obtained from touchscreen questions. We calculated the healthy diet score by using the following factors: vegetable intake of at least four tablespoons each day (median), fruit intake of at least three pieces each day (median), fish intake of at least twice per week (median), unprocessed red meat intake of no more than twice per week (median), and processed meat intake of no more than two per week (median). Sleep duration (<7 h per day/7–8 h per day/> 8 h per day) and sleep midpoint (<02:30/02:30-03:30/> 03:30) were measured using the accelerometer. Obesity (body mass index ≥30 kg/m2) was obtained from touchscreen questions. Previous diagnoses of diabetes, longstanding illness, depression, CVDs, and cancer were obtained from the self-reported questionnaires, hospital records, and death registry. The values of some covariates, including education level, smoking status, alcohol consumption, healthy diet score, obesity, diabetes history, longstanding illness, and cancer history, were obtained from touchscreen questionnaires at the time-point closest to the accelerometry (Supplementary Fig. 6). Detailed information sources, assessment timeline, and missing percentages are shown in Supplementary Fig. 6 and Supplementary Tables 20–21.

Statistical analyses

The event numbers of all outcomes were sufficient as per the rule-of-thumb estimation44, which requires at least ten events per variable. We conducted multiple imputations to assign any missing covariate values using the “mice” package (v3.13.0) in R45. The overall sample and complete case sample showed similar baseline characteristics (Supplementary Table 22). Before investigating the timing effect of MVPA, the linear and nonlinear associations of total MVPA volume and MVPA within the three time windows with mortality risk were assessed using penalized cubic splines fitted in the fully adjusted Cox models. In addition, we assessed the linear and nonlinear associations between the proportions of MVPA accumulated within the three time windows and mortality risk. Based on the fully adjusted model, cumulative risk curves were generated to show the standardized risks of mortality outcomes according to MVPA timing groups. Collinearity between all covariates was examined via correlation matrix analysis, which revealed no problem of multicollinearity. Cox proportional hazard regression (using the “survival” package v3.2-11 in R) was used to examine the associations of the timing of MVPA with mortality. HRs and their 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CIs were calculated. We conducted careful adjustments. Model 1 adjusted for age and sex. Model 2 additionally adjusted for ethnicity, Townsend deprivation index, recruitment center, education level, the season of accelerometer wear, smoking status, alcohol intake, and healthy diet score. To investigate the associations independent of sleep duration, sleep phase, and total MVPA volume, model 3 further adjusted for these factors.

We performed a series of sensitivity analyses. First, we used different MVPA fraction cutoffs (55, 60, 65, and 70{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}) to assign timing groups. We did not conduct sensitivity analyses using cutoffs of >70{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} due to the small sample size for timing groups (other than mixed group). Second, Fine-Gray subdistribution hazards (using the “cmprsk” package v2.2-10 in R) were calculated, incorporating other-cause death as a competing risk for cause-specific mortality46. Third, we further adjusted for health-related variables potentially on causal pathways6, including obesity, diabetes history, longstanding illness, depression history, CVDs, and cancer. Fourth, we restricted the analyses to participants without shift work history and those without any missing covariate data, respectively. Fifth, we excluded participants who wore accelerometers during daylight-saving time transitions. Sixth, we ran the analyses by controlling for the month of accelerometer wear instead of the season of accelerometer wear. Seventh, we excluded events that occurred within one year of follow-up. In addition, we performed analyses by censoring up to 31 Dec 2019 (the start of the COVID-19 pandemic47). Finally, we repeated the analyses among those with ≥6 days of accelerometer wear.

Multiplicative and additive interaction (using the “interactionR” package v0.1.3.9000 in R) analyses and subgroup analyses were performed on age, sex, MVPA level (meeting the WHO recommendation1,3 or not), CVDs, and obesity (BMI ≥30 kg/m2). All statistical tests were two-sided, and a P value of <0.05 was regarded as statistically significant. To account for multiple testing, P values in fully adjusted models were corrected using the false-discovery rate (FDR)48. All statistical analyses were performed using R v4.0.4 and SPSS v26. R codes are available with the online version of this article (Supplementary Code) and at https://github.com/hlfeng99/Supplementary-Code.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.