TABLE 2.
Differences between Chronotype and Body Composition or Biomarkers1
| Reference | Body composition distribution | Differences between types | |||
|---|---|---|---|---|---|
| MT | IT | ET | P value (ET vs. IT/MT) and other analysis | ||
| BMI, weight, and height | |||||
| Xiao et al., 2019 (77) |
Normal BMI: Women: 65.5% Men: 34.5% Overweight BMI: Women: 40.1% Men: 59.9% Obese BMI: Women: 52.8% Men: 47.2% |
—2 | — | Overweight (3.1 ± 1.0 h) and obese (3.2 ± 1.1 h) participants had on average a later chronotype in comparison with those with a normal BMI (2.9 ± 1.0 h)3 | P < 0.007 |
| — | — | Overweight (3.5 ± 0.9)/obese (3.6 ± 1.0) had a later midpoint of time in bed during weekends (3.6 ± 1.0) h in comparison with those with a normal BMI (3.3 ± 1.0)3 | P < 0.001 | ||
| Sato-Mito et al., 2011 (56) |
Height: 158 ± 5.3 cm Weight: 52.2 ± 7.6 kg BMI: 20.9 ± 2.8 |
MT421.2 ± 3.1 Underweight:14.2%Normal: 77.3%Overweight: 8.4% | IT5,6,720.9 ± 2.8Underweight: 14.6% Normal: 78.9%Overweight: 6.6%520.8 ± 2.5Underweight: 13.8%Normal: 79.4% Overweight: 6.9%620.9 ± 2.7Underweight: 15.3%Normal: 77.8%Overweight: 6.9%7 | ET820.9 ± 2.7 Underweight: 14.0%Normal: 79.0%Overweight and obese: 7.0% | P-trend = 0.30 |
| Vera et al., 2018 (71) | BMI: 31.3 ± 5.41 | 40.0 ± 0.16 | — | 31.3 ± 0.16ET showed a linear association toward higher BMI | P = 0.16P-trend = 0.02 |
| Najem et al., 2020 (76) | BMI: 22.3 ± 3.61 Range: 15.6–38.6 | — | — | — | Other analysis:No correlation (r= 0.025 , P = 0.54) between BMI and ME scores |
| Lázár et al., 2012 (73) | BMI: 23.7 ± 2.8 Range: 18–30 | — | — | — | — |
| Yoshizaki et al., 2018 (59) | BMI day workers:21.2 ± 2.7 | 21.2 ± 2.79 | 21.2 ± 2.610 | 21.1 ± 2.811 | P-trend = 0.33 |
| Silva et al., 2016 (60) | BMI: 22.8 ± 3.2Overweight and obese: n = 47 (23%) | — | — | — | — |
| Lai et al., 2013 (74) | BMI:Underweight: n = 270Normal: n = 585Overweight: n = 181Obese: n = 82 | — | — | — | Other analysis: BMI not correlated (r = 0.04; P = 0.15) with MES scores |
| Muñoz, 2020 (57) | Range: BMI >25Chrono group: 30.37 ± 2.56 | BMI changes: –3.4 ± 1.0 | — | BMI changes: –2.9 ± 0.6 | P = 0.219 |
| Lucassen et al., 2013 (62) | BMI: 38.5 ± 6.4Range: 30–55 | 38.2 ± 6.3 | — | 39.1 ± 6.6 | P = 0.47Other analysis:Scores toward ET were associated with an increase in BMI (P = 0.05, R2 = 0.06)Effect size: 10-unit change in chronotype score was associated with a change of 1.2 in BMI |
| Mota et al., 2016 (63) |
BMI: 22.9 ± 3.4 BMI ≥25: 33.4% |
Chronotype scores not associated with BMI (β-coefficient = −0.01) | P = 0.98 | ||
| Zerón-Rugerio et al., 2019 (64) | BMI: n = 21.7 (3.1%)Underweight: n = 54 (10.1%)Normal weight: n = 413 (77.3%) Overweight: n = 56 (10.5%)Obese: n = 11 (2.1%) | — | — | ET had a higher BMI (β-coefficient = –0.03) | P = 0.04 |
| Maukonen et al., 2019 (78) | BMI: MT: 26.5 (0.2)IT: 26.6 (0.2)ET: 26.7 (0.4) | No increase in BMI over 7-y follow-up period | — | Mean increase in BMI over 7- year follow-up period: 0.4 (0.2) | P = 0.23 |
| Proportion of subjects with BMI increases of ≥5% over the 7-y follow-up period: 22% | — | Higher proportion of subjects (33%) with BMI increases of ≥5% over the 7-y follow-up period | P > 0.05 | ||
| Obese at end of the follow-up: 17% of subjects | — | Obese at end of the follow-up: 26% of subjects | P = 0.061 | ||
| Increase in BMI of MT women: −0.1 | — | ET women had a greater increase in BMI (0.7) than MT women | P = 0.024 | ||
| Maukonen et al., 2017 (79) | — | 27.1 ± 0.2 | 26.7 ± 0.2 | 27.6 ± 0.3 | P = 0.44 |
| Not associated with chronotype score | P-trend = 0.66 | ||||
| Maukonen et al., 2016 (65) | BMI:MT: 27.2 (SE 0.13)IT: 27.1 (SE 0.09)ET: 26.9 (SE 0.16) | No difference in both sexes | P > 0.05 | ||
| Chronotype score was positively associated with BMI in menMTs were associated with a higher BMI in men (β-coefficient = 0.05) | — | — | P = 0.04 | ||
| Teixeira et al., 2018 (66) | — | 22.6 ± 3.2 | 22.3 ± 3.8 | 22.2 ± 3.6 | P = 0.71 |
| Overweight: n = 14.6 (22%) | Overweight: n = 18.8 (84%) | Overweight: n = 20.2 (25%) | P = 0.41 | ||
| Li et al., 2018 (74) | Weight:Underweight: n = 158 (20.1%)Normal: n = 585 (74.2%)Overweight: n = 32 (4.1%)Obese: n = 13 (1.6%) | — | — | — | Other analysis:Positive correlation between chronotype and BMI. MT was associated with a higher BMI (r = 0.51, P < 0.01) |
| De Amicis et al., 2020 (67) | — | 29.7 ± 5.6 | 29.1 ± 6.1 | 29.4 ± 6.1 | P > 0.05 |
| Culnan et al., 2013 (72) | Weight—baseline: 139 ± 28.8 kgWeight—follow-up: 143 ± 29.5 kgBMI—baseline: 22.0 ± 3.26BMI—follow-up: 22.9 ± 3.41 | Baseline: Chronotype not associated with weight (unstandardized β = –1.70) | P > 0.05 | ||
| Baseline: Chronotype not associated with BMI (unstandardized β = – 0.26) | P > 0.05 | ||||
| — | — | 8-wk follow-up: increase in BMI of 0.50 BMI points (unstandardized β = 0.50; 95% CI: 0.04, 0.95) | P = 0.03 | ||
| Baron et al., 2011 (75) | BMI:IT12: 23.7 ± 3.2ET13: 26.0 ± 6.9 | — | 2 of 27 ITs12 reported BMI ≥30 | 6 of 22 ETs13 reported BMI ≥30 | P = 0.15Other analysis:BMI positively correlated with ET13 (P < 0.01) |
| Baron et al., 2013 (68) | BMI:IT12: 23.7 ± 3.2ET13: 26.0 ± 6.9 | — | — | — | Other analysis:BMI moderately positive correlated with midpoint of sleep (r = 0.35, P < 0.05) |
| Beaulieu et al., 2020 (69) | BMI: 24.5 ± 3.2 | 24.1 ± 2.7 | — | 24.9 ± 3.6 | P = 0.01Other analysis:Inverse relation between MEQ score and BMI (ET showing a lower BMI r = −0.37, P = 0.01) |
| Weight: 72.9 ± 11.4 kg | 73.4 ± 10.3 kg | — | 72.4 ± 12.7 kg | P > 0.05 | |
| Muscogiuri et al., 2020 (70) | BMI: (32.1 ± 6.3)Normal BMI: 18 (10.5%)Overweight BMI: 47 (27.3%)Obesity: Class I: 58 (33.7%)Class II: 29 (16.9%)Class III: 20 (11.6) | 31.4 ± 5.8Normal BMI: 10 (10.0%)Overweight BMI: 33 (33.0%)Obesity:Class I: 32 (32.0%)Class II: 15 (15.0%)Class III:10 (10.0%) | 33.1 ± 7.3Normal BMI: 7 (14.0%)Overweight BMI: 9 (18.0%)Obesity:Class I: 15 (30.0%)Class II: 11 (22.0%)Class III: 8 (16.0%) | 32.6 ± 5.5Normal BMI:1 (4.5%)Overweight BMI: 5 (22.7%)Obesity:Class I: 11 (50.0%)Class II: 3 (13.6%)Class III: 2 (9.1%) | P = 0.27Other analysis:Chronotype was inversely correlated to BMI (r = −0.16, P = 0.04). MTs were associated with a lower BMI |
| Weight | 82.9 ± 19.0 kg | 88.1 ± 20.6 kg | 83.7 ± 12.5 kg | P = 0.29 | |
| Zerón-Rugerio et al., 2020 (58) | BMI: 23.7 ± 4.0 | 25.4 ± 4.014 | 23.8 ± 4.51523.0 ± 3.016 | 22.5 ± 3.817 | P = 0.02P-trend = 0.002 |
| Associated with increased BMI 2.315 | — | — | P < 0.05 | ||
| Body fat percentage, abdominal, visceral, and subcutaneous adipose tissue | |||||
| Vera et al., 2018 (71) | BF%: 37.2 ± 6.71 | BF%: 37.0 (0.19) | — | BF%: 37.0 (0.19) | P = 0.85P-trend = 0.54 |
| Muñoz et al., 2020 (57) | — | BF% changes between baseline and end point: – 4.2 ± 2.3 | — | BF% changes between baseline and end point: – 3.2 ± 2.1 | P = 0.28 |
| Maukonen et al., 2016 (65) | — | BF%: 35.2 (0.23) | BF%: 35.2 (0.15) | BF%: 35.3 (0.24) | P = 0.92 |
| Teixeira et al., 2018 (66) | — | Inadequate abdominal fat: n = 17.9 (27%) | Inadequate abdominal fat: n = 23.5 (105%) | Inadequate abdominal fat: n = 25.8 (32%) | P = 0.24 |
| De Amicis et al., 2020 (67) | — | SAT: 2.6 ± 1.3 cm | SAT: 2.5 ± 1.1 cm | SAT: 2.5 ± 1.3 cm | P > 0.05 |
| VAT: 5.1 ± 2.3 cmLower abdominal VAT for every 1 point of rMEQ scoreMTs were associated with lower VAT of −0.06 (–0.11, –0.01) cm | VAT: 5.1 ± 2.5 cm | VAT: 5.2 ± 2.9 cm | P > 0.05P < 0.05 | ||
| Beaulieu et al., 2020 (69) | BF%: 27.7 ± 8.3 | BF%: 27.3 ± 8.4 | — | BF%: 28.2 ± 8.4 | P > 0.05 |
| Zerón-Rugerio et al., 2020 (58) | — | Fat mass, %: 32.2 ± 7.414 | Fat mass, %: 31.5 ± 7.81530.5 ± 5.316 | Fat mass, %: 29.5 ± 6.417 | P = 0.39P-trend = 0.08 |
| Waist circumference | |||||
| Silva et al., (60) | Abdominal obesity: 31 (15%) | — | — | — | — |
| Muñoz et al., 2020 (57) | — | Changes between end point and baseline: –9.8 ± 2.7 cm | — | Changes between end point and baseline: –8.8 ± 3.6 cm | P = 0.44 |
| Lucassen et al., 2013 (62) | — | 113 ± 13.6 cm | — | 115 ± 11.5 cm | P = 0.51 |
| Mota et al., 2016 (63) | WC >94 cm in males and >30 cm in females: 33.3% | Chronotype scores were not associated with WC (β-coefficient = 0.09) | P = 0.41 | ||
| Maukonen et al., 2019 (78) | MT: 89.8 (SE 0.5) cmIT: 90.8 (SE 0.6) cmET: 92.3 (SE 1.1) cm | Mean increase: 2.2 cm for both types over the 7-y follow-up period | P = 1.00 | ||
| Proportion of subjects whose WC increased by ≥5% over 7-y follow-up period: 33% | — | Proportion of subjects whose WC increased by ≥5% over 7-y follow-up period: 39% | P > 0.05 | ||
| Maukonen et al., 2016 (65) | MT: 86 (SE 0.42) cmIT: 86.5 (SE 0.27) cmET: 86.9 (SE 0.43) cm | No difference in both sexes | P > 0.05 | ||
| Teixeira et al., 2018 (66) | — | 78.3 ± 8.3 cm | 79.0 ± 11.3 cm | 79.0 ± 11.6 cm | P = 0.75 |
| De Amicis et al., 2020 (67) | — | 98.4 ± 13.2 cmWC decreases by –0.19 as rMEQ score increasesMT associated with a lower WC | 97.8 ± 14.5 cm | 99.6 ± 13.5 cm | P > 0.05P < 0.01 |
| Beaulieu et al., 2020 (69) | 84.3 ± 7.9 cm | 84.2 ± 6.2 | — | 84.3 ± 7.9 cm | P > 0.05 |
| Muscogiuri et al., 2020 (70) | — | 103 ± 16.4 cm | 103 ± 17.3 cm | 105 ± 11.8 cm | P = 0.89Other analysis:Chronotype not correlated with WC (r = −0.04, P = 0.57) |
| Zerón-Rugerio et al., 2020 (58) | — | 98.4 ± 6.9 cm14 | 76.2 ± 9.7 cm1574.9 ± 8.4 cm16 | 72.8 ± 7.4 cm17 | P = 0.06P-trend = 0.01 |
| Associated with increased WC of 5.2 cm14 | P-trend < 0.05 | ||||
| Hip circumference | |||||
| Beaulieu et al., 2020 (69) | 98.4 ± 6.9 cm | 99.2 ± 4.8 cm | — | 97.6 ± 8.6 cm | P > 0.05 |
| Zerón-Rugerio et al., 2020 (58) | — | 99.5 ± 7.7 cm14 | 97.3 ± 10.7 cm1596.3 ± 6.8 cm16 | 95.2 ± 7.3 cm17 | P = 0.19P-trend = 0.03 |
| Waist-to-hip ratio | |||||
| Beaulieu et al., 2020 (69) | 0.86 ± 0.06 | 0.85 ± 0.07 | — | 0.86 ± 0.06 | P > 0.05 |
| Neck circumference | |||||
| Lucassen et al., 2013 (62) | — | 38.8 ± 3.8 cm | — | 39.6 ± 3.8 cm | P = 0.34Other analysis:Scores toward eveningness were associated with a larger NC (P = 0.03)Effect size: a 10-unit change in chronotype score was associated with a change of 0.6 cm in NC |
| Weight loss/gain | |||||
| Muñoz et al., 2020 (57) | — | Total weight loss, %: 10.2 ± 2.6 | — | Total weight loss, %: 9.6 ± 1.8 | P = 0.52 |
| Mota et al., 2016 (63) | — | — | — | Chronotype scores (MT, IT, ET) not associated with weight gain after the beginning of residency (β-coefficient = −0.10) | P = 0.48 |
| Maukonen et al., 2019 (78) | — | Mean weight gain: 0.6 kg | — | Mean weight gain: 1.4 kg | P = 0.35 |
| — | Proportion of subjects who gained weight of ≥5% over the 7-y follow-up period: 22% | — | Proportion of subjects who gained weight of ≥5% over the 7-y follow-up period: 37% | P > 0.05 | |
| — | Weight gain in MT women over the 7-y follow-up period: 0.3 kg | — | Weight gain in ET women over the 7-y follow-up period: 2.4 kg | P = 0.02 | |
| Culnan et al.,2013 (72) | — | — | — | 8-wk follow-up: weight gain of 2.35 pounds (1.07 kg) (unstandardized β = 2.35 pounds; 95% CI: –1.62, 4.87) | P = 0.07 |
| Biomarkers | |||||
| Vera et al., 2018 (71) | Fasting glucose: glucose oxidase methodTriglycerides and HDL cholesterol: commercial kits Arterial pressure: mercury sphygmomanometerMetS score: IDF criteria; summing MetS components Fasting insulin: solid-phase, 2-site chemiluminescent immunometric assay Insulin resistance: (HOMA-IR; fasting glucose × fasting insulin/22.5)Blood samples via standard procedures: DNA isolation and genotyping and GRS | Triglyceride concentrations: 101 ± 1.71 mg/dLMetS scores: 2.06 ± 0.04 HDL cholesterol concentrations: 57.1 ± 0.46 mg/dLInsulin concentrations: 7.40 ± 0.22 μUI/mLHOMA-IR concentrations: 1.61 ± 0.05 Not reported | — | Triglyceride concentrations: 105 ± 1.79 mg/dLMetS scores: 2.16 ± 0.04HDL cholesterol concentrations: 55.6 ± 0.48 mg/dLInsulin concentrations: 7.62 ± 0.23 μUI/mLHOMA-IR concentrations: 1.68 ± 0.06 Higher evening genetic risk score | P = 0.01P = 0.01P = 0.03P-trend < 0.001P-trend = 0.002P = 0.04 |
| Lázár et al., 2012 (73) | Genotyping of the PER3 VNTR was performed according to standard procedure | Frequency of PER35/5 genotype: 15.4% | — | Frequency of PER35/5 genotype: 7.5% | _ |
| Genotype: effect on diurnal preference measured by MEQ (F2, 619 = 4.43) | P = 0.01 | ||||
| Genotype: marginal effect on diurnal preference measured by MCTQ | P = 0.06 | ||||
| The main effect of genotype was significant for MEQ (F2, 636 = 5.97) | P = 0.003 | ||||
| The main effect of genotype was significant for the self-assessment question from the MCTQ (F2, 642 = 4.12) | P = 0.02 | ||||
| Lucassen et al., 2013 (62) | 24-h urinary epinephrine concentrations 3 (2–5) μg/24 h | — | 24-h urinary epinephrine concentrations: 4 (3–7) μg/24 h; 0–30% higher | P = 0.04 | |
| HDL cholesterol: 48 (42–58) mg/dL | — | HDL cholesterol: 49 (41–52) mg/dL | P = 0.51 | ||
| Resting heart rates: 68.4 ± 10.1 beats/min | — | Resting heart rates: 74.0 ± 10.1 beats/min | P = 0.01 | ||
| Plasma ACTH: 17 (12–24) pg/mL | — | Plasma ACTH: 21 (16–32) pg/mL | P = 0.02 | ||
| 24-h urinary norepinephrine: 39 (28–56) μg/24h | — | 24-h urinary norepinephrine: 45 (37–61) μg/24 h | P = 0.05 | ||
1
Values are reported as mean ± SD unless stated otherwise. BMI is reported in kg/m2 with the following categories: underweight, <18.5; normal, <18.5 to <25; overweight and obese, ≥25. OR (95% CI), P-trend refers to the continuous association between the MEQ or MCTQ score and exposures of interest. ACTH, adrenocorticotropic hormone; BF%, body fat percentage; ET, evening type; GRS, genetic risk score; IDF, International Diabetes Federation; IT, intermediate type; MCTQ, Munich Chronotype Questionnaire; MEQ, Morning–Eveningness Questionnaire; MES, Morningness-Eveningness Scale; MetS, metabolic syndrome; MT, morning type; NC, neck circumference; PER3, PERIOD3 clock gene; rMEQ, reduced Morning-Eveningness Questionnaire; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; VNTR, variable number tandem repeat ; WC, waist circumference.
2
Early chronotype was defined as midsleep earlier than the median midsleep (03:04 h).
3
Later chronotype was defined as midsleep later than the median midsleep (03:04 h).
4
Based on earliest midpoint of sleep quintiles.
5
Based on midpoint of sleep quintile 2.
6
Based on midpoint of sleep quintile 3.
7
Based on midpoint of sleep quintile 4.
8
Based on latest midpoint of sleep quintiles.
9
Based on MEQ score tertile 1: 34–53.
10
Based on MEQ score tertile 2: 54–59.
11
Based on MEQ score tertile 3: 60–76.
12
Based on normal sleep timing (midpoint 04:08 h).
13
Based on late sleep timing (midpoint of sleep 07:15 h).
14
Based on wakeup time <07:52 h and early bedtime <23:48 h and defined as early bedtime/early rise (EE).
15
Based on early bedtime (<23:48 h) and late rise (wakeup time ≥07:12 h) and defined as early bedtime/late rise (EL).
16
Based on late bedtime (≥23:48 h) and wakeup time (<07:52 h) and defined as late bedtime/early rise (LE).
17
Based on late bedtime (≥23:48 h) and late rise (wakeup time ≥07:12 h) and defined as late bedtime/late rise (LL).