loading . . . Pre-fertilization-origin preservation of brown fat-mediated energy expenditure in humans Takeshi Yoneshiro,
Mami Matsushita,
Sayuri Fuse-Hamaoka,
Miyuki Kuroiwa,
Yuko Kurosawa,
Yosuke Yamada,
Yuchen Wei,
Makoto Iida,
Kenichi Kuma,
Toshimitsu Kameya,
Tomoya Harada,
Yoshihiro Matsumura,
Tsuyoshi Osawa,
Yoshiko Aoki,
Hisashi Nakamura,
Takafumi Hamaoka,
Juro Sakai &
Masayuki Saito
Abstract
Environmental thermal stress substantially affects cellular plasticity of thermogenic adipocytes and energy balance through transcriptional and epigenetic mechanisms in rodents. However, roles of cold-adaptive epigenetic regulation of brown adipose tissue (BAT) in systemic energy metabolism in humans remained poorly understood. Here we report that individuals whose mothers conceived during cold seasons exhibit higher BAT activity, adaptive thermogenesis, increased daily total energy expenditure and lower body mass index and visceral fat accumulation. Structural equation modelling indicated that conception during the cold season protects against age-associated increase in body mass index through BAT activation in offspring. Meteorological analysis revealed that lower outdoor temperatures and greater fluctuations in daily temperatures during the fertilization period are key determinants of BAT activity. These findings suggest that BAT metabolic fate and susceptibility of metabolic diseases are preprogrammed by the epigenetic inheritance of cold exposure before the fertilization in humans.
Main
Obesity is the major risk factor for the development of type 2 diabetes, dyslipidaemia and cardiovascular diseases, increasing all-cause mortality1,2. While exercise habits and eating behaviours are important for energy homeostasis, environmental stimuli also influence the risk of obesity due to dynamic fluctuations in energy expenditure (EE)3. Cold exposure is a well-established environmental determinant of whole-body EE, triggering adaptive thermogenesis through the activation of metabolic organs, including brown adipose tissue (BAT)3,4. Individual differences in BAT activity are associated with age-related increases in body mass index (BMI), visceral fat accumulation, insulin resistance and cardiometabolic risks5,6,7,8. Despite the potential of BAT as a therapeutic target, underlying factors contributing to individual differences in BAT activity remain poorly understood, particularly in humans.
Environmental regulation of adipose tissue thermogenesis differs between acute and chronic activation. In small rodents, acute cold exposure (for a few minutes) evokes immediate heat production in BAT through activation of the sympathetic nervous system and ÎČ-adrenergic signalling pathways9. Conversely, prolonged cold exposure (several weeks) induces de novo brown adipocyte differentiation and the appearance of beige adipocytes, which require chromatin conformation changes and histone modifications orchestrated by epigenetic regulators such as JMJD1A, limiting metabolic disorders10,11. Consistently, in humans, acute cold exposure for a few hours elicits BAT thermogenesis and substrate utilization, which correlate with sympathetic innervation12,13,14,15,16. Moreover, repeated cold exposure or chronic administration of an adrenergic receptor agonist for several weeks increases BAT activity and mass, ultimately improving adiposity and insulin sensitivity12,17,18. Transcriptomic analyses revealed that BAT in the supraclavicular depots of human adults resembles mouse beige adipocytes; in addition, expression of the epigenetic regulator JMJD1A in adipose tissue negatively correlates with BMI19,20,21. These consistent findings across species suggest that epigenetic mechanisms contribute to the recruitment of BAT in humans as it does in mice22,23.
Genetic inheritance of risk alleles in thermogenic genes can also influence BAT activity as evidenced by the accelerated age-related decline in BAT and visceral fat accumulation in human adults with single nucleotide polymorphisms on uncoupling protein 1 (UCP1) and ÎČ3 adrenergic receptor (ADRB3)24. Compelling evidence also suggests that epigenetic predisposition induced by specific nutritional and environmental stresses such as temperature during gestation and lactation can be inherited across generations, resulting in various physiological alterations in experimental animals25,26,27,28.
Building on these findings, we hypothesized that meteorological exposures experienced by parents before and during gestation influence BAT metabolic fate and regulate energy homeostasis in human offspring. This hypothesis is supported by a recent study in mice, demonstrating that preconception cold exposure, particularly within the paternal lineage, enhances adipose tissue thermogenesis in offspring through mechanisms possibly involving DNA methylation in sperm28. We present evidence that BAT metabolic activity, as well as adaptive EE, are elevated in healthy human individuals conceived during a cold season compared with those conceived during a warm season. Moreover, we performed a spatiotemporal meteorological analysis and identified a lower ambient temperature and larger diurnal temperature variation in the period before conception as critical determinants of BAT activity. Additionally, BAT activation resulting from preconception cold exposure was associated with a reduced BMI and waist circumference independent of age, sex and the season of birth. These findings provide comprehensive evidence suggesting that thermal stress experienced just before conception plays an intergenerational role in facilitating the activation and sustenance of BAT and modulating systemic energy homeostasis in humans.
Results
Fertilization season influences BAT metabolism in adulthood
To investigate the lifelong effects of early-life environmental exposure on BAT function, we examined the association of cold-induced BAT activity with the seasons of fertilization or birth in 356 healthy young male volunteers (Cohort 1; Fig. 1a, Extended Data Fig. 1a and Supplementary Fig. 1a). The presence of detectable BAT and its quantitative metabolic activity were assessed with fluorodeoxyglucose-positron emission tomography and computed tomography (FDG-PET/CT) following cold exposure at 19 °C for 2 h (Fig. 1b)5,8,12,14. Based on the participantâs day of birth, the seasons of birth and fertilization were categorized as either warm season (16 April through 16 October) or cold season (the remainder) (Extended Data Fig. 1b,c). Note that the seasonality of meteorological factors is distinct and rather coherent regardless of the latitude in the main islands of Japan, and the participantâs place of birth distributed throughout Japan (Extended Data Fig. 1b,d). The prevalence of detectable BAT (high BAT) was indistinguishable between participants born in the cold season (cold birth group, 71.8%) and those born in the warm season (warm birth group, 73.8%, P = 0.381; Fig. 1c and Extended Data Fig. 2a). In contrast, BAT prevalence was significantly higher in those conceived in the cold season (cold fertilization group, 78.2%) than in those conceived in the warm season (warm fertilization group, 66.0%, P = 0.007; Fig. 1d and Extended Data Fig. 2b). Similarly, quantitative BAT activity was notably higher in the cold fertilization group than in the warm fertilization group (P = 0.017; Fig. 1e), whereas BAT activity was not associated with the season of birth (P = 0.144). The association between the fertilization season and BAT activity (adjusted odds ratio (OR) = 1.949, P = 0.007) was irrespective of age, BMI and the season of birth (Extended Data Fig. 2c). The effect of fertilization season is not specific to the supraclavicular BAT because fertilization in cold period was associated with increased number of active BAT depots, including thoracic, cervical, axillary, mediastinal and perirenal regions (Extended Data Fig. 2dâg). These suggest an intergenerational activation of BAT by preconception perceived environmental stress in young healthy men. These findings are not dependent on our method to define the seasons because the difference between our unbiased definition and another definition based on the median of outdoor temperature was only 2.7% of the year (10 days), having a negligible impact on the results.
a, Study protocol for investigating intergenerational control of BAT in humans. BAT activity and density were assessed by 18F-FDG-PET/CT following acute cold exposure and NIR-TRS, respectively. The day of fertilization was estimated based on the day of birth. Seasons of birth and fertilization were determined according to the participantâs days of birth and fertilization as depicted in Extended Data Fig. 1b. b, Cold-induced BAT activity assessed as FDG uptake value (standardized uptake value; SUV) of the high and low BAT groups in Cohort 1. High BAT group (n = 259), low BAT group (n = 97). c, Association of the prevalence of cold-activated BAT and the season of birth in b. Warm birth group (n = 188), cold birth group (n = 168). d, Association of prevalence of cold-activated BAT and the season of fertilization in b. Warm fertilization group (n = 159), cold fertilization group (n = 197). e, Cold-induced BAT activity assessed as FDG uptake value (SUV) in c and d. f, BAT density (BAT-d) of the high and low BAT-d groups assessed as total haemoglobin concentration, [total Hb], in the supraclavicular region in Cohort 2. High BAT-d group (n = 143), low BAT-d group (n = 143). g, Association of the percentage of participants with high BAT-d and season of birth in f. Warm group (n = 144), cold birth group (n = 142). h, Association of the percentage of participants with high BAT-d and season of fertilization in f. Warm fertilization group (n = 153), cold fertilization group (n = 133). i, BAT-d assessed as [total Hb] in the supraclavicular region in g,h. Biologically independent samples (bâi). Number of participants (n) is indicated on the graph. Data are mean ± s.e.m.; two-tailed P values by unpaired Studentâs t-test (b,e,f,i). Data are percentage; one-tailed P values by Fisherâs exact test (c,d,g,h).
To address whether BAT activation by preconception environmental stress extends to the general population, we recruited 286 independent healthy male and female volunteers aged 20â78 years (Cohort 2). In this cohort, BAT density (BAT-d) was estimated noninvasively by measuring the total haemoglobin concentration ([total Hb]) in the supraclavicular region using near-infra-red time-resolved spectroscopy (NIR-TRS) (Fig. 1a). BAT-d measured by NIR-TRS exhibits a strong correlation with cold-induced FDG uptake by PET/CT, making it a reliable alternative index of BAT functionality29,30. We categorized the participants into high and low BAT-d groups based on the median of BAT-d (Fig. 1f and Extended Data Fig. 3a). Consistent with findings from Cohort 1, the season of birth did not affect the percentage of high BAT-d (Fig. 1g and Extended Data Fig. 3b,c) or quantitative BAT-d (Fig. 1i). In contrast, the percentage of high BAT-d was significantly greater in the cold fertilization group (56.4%) than in the warm fertilization group (44.4%, P = 0.029; Fig. 1h and Extended Data Fig. 3b,d). Additionally, we found a significant increase in BAT-d ([total Hb] at the supraclavicular BAT deposits), in the cold fertilization group compared with the warm fertilization group (P = 0.020; Fig. 1i). This increase in [total Hb] was specific to the supraclavicular BAT region and was not due to a general increase in blood flow because [total Hb] in other regions such as the abdominal subcutaneous white adipose tissue and the deltoid skeletal muscle remained consistent regardless of the season of birth or fertilization (Extended Data Fig. 3e,f). A disaggregated analysis for sex revealed an enhanced BAT-d in the cold fertilization group in male and female participants (Extended Data Fig. 3g). These results were consistent with those in Cohort 1 and support the idea that the metabolic fate of BAT in adulthood is predetermined by parental exposure to meteorological environments during the period around fertilization in healthy humans.
BAT thermogenesis increased by fertilization in the cold season
It is widely recognized that cold-activated BAT has a crucial role in EE, especially in nonshivering cold-induced thermogenesis (CIT), which fluctuates seasonally in accordance with the recruitment of BAT31. We hypothesized that the intergenerational activation of BAT might influence CIT in a season-dependent manner. To test this, we analysed the resting EE at 27 °C and after 2-h cold exposure at 19 °C measured by indirect calorimetry in 42 young healthy males who underwent FDG-PET/CT31 (Cohort 3; Extended Data Fig. 4aâc). This test was conducted during both the summer (July to September) and winter (December to March) in a randomized crossover design. As expected, no notable difference in BAT activity was observed between the warm and cold birth seasons (Extended Data Fig. 4d, left). Whole-body EE adjusted for fat-free mass (FFM) at 27 °C and 19 °C and CIT were comparable between the warm and cold birth groups regardless of season of the measurements (Fig. 2a and Extended Data Fig. 4e,f).
a, Whole-body resting EE and CIT of the warm (n = 23) and cold birth groups (n = 19) (Cohort 3) measured in winter. Left, resting EE adjusted for FFM at thermoneutral condition (27 °C) and after 2-h cold exposure (19 °C). Right, CIT. b, Whole-body resting EE and CIT of the warm (n = 14) and cold fertilization groups (n = 28) (Cohort 3) measured in winter. Resting EE adjusted for FFM at thermoneutral condition (27 °C) and after 2-h cold exposure (19 °C) (left). CIT (right). c, Postprandial changes in resting EE adjusted for FFM (left) and DIT calculated as incremental area under the curve (iAUC, right) for the warm (n = 10) and cold birth groups (n = 13) (Cohort 4). d, Postprandial changes in resting EE adjusted for FFM (left) and DIT calculated as iAUC (right) for the warm (n = 6) and cold fertilization groups (n = 17) (Cohort 4). Biologically independent samples (aâd). Data are mean ± s.e.m.; two-tailed P values by unpaired Studentâs t-test or by two-way repeated measures ANOVA (a,b and c,d, right) with post hoc unpaired Studentâs t-test (c,d left).
Conversely, BAT activity was significantly higher in the cold fertilization group than in the warm fertilization group (Extended Data Fig. 4d, right), consistent with findings observed in Cohorts 1 and 2. While there was no group difference in EE at 27 °C, the response of the adjusted EE to cold exposure seemed to be increased in the cold fertilization group compared with the warm fertilization group in the winter (Fig. 2b, left), but not in the summer (Extended Data Fig. 4g). Indeed, CIT was significantly higher in the cold fertilization group than in the warm fertilization group specifically in the winter (Fig. 2b, right) but not in the summer (Extended Data Fig. 4h). Given that neither shivering, as assessed by electromyography nor FDG uptake into the skeletal muscle is induced in our mild cold exposure protocol14,32, the observed increase in CIT is likely due to BAT activation. This aligns with the observation that BAT activity was positively correlated with CIT in the winter, but not in the summer (Extended Data Fig. 4i), FFM did not associate with CIT regardless of the season (Extended Data Fig. 4j). These findings suggest that BAT activation due to fertilization in the cold season enhances cold-induced BAT thermogenesis and contributes to seasonal cold adaptation in humans.
We next examined the effects of the fertilization season on diet-induced thermogenesis (DIT), another component of BAT-associated adaptive thermogenesis33,34,35. In this study, whole-body EE was measured in 23 healthy adult males (Cohort 4; Extended Data Fig. 5a,b) by indirect calorimetry over a period of 2 h following the ingestion of a meal (Extended Data Fig. 5c,d)35. All DIT measurements were carried out in the winter months (December through March). We ensured that age and anthropometric parameters were comparable between the warm and cold birth groups and between the warm and cold fertilization groups. Postprandial thermogenesis and DIT, calculated as the incremental area under the curve, did not differ significantly between the warm and cold birth groups (Fig. 2c and Extended Data Fig. 5e). Increased BAT activity was observed in the cold fertilization group of Cohort 4 compared with the warm fertilization group (Extended Data Fig. 5b, right), as observed in Cohorts 1 and 3. It is noteworthy that postprandial thermogenesis and DIT were significantly higher in the cold fertilization group than in the warm fertilization group (Fig. 2d and Extended Data Fig. 5f).
Finally, we asked whether fertilization in the cold season is sufficient to enhance total energy expenditure (TEE) under the free-living conditions in Cohort 5 (Extended Data Fig. 6a). To this end, we measured TEE using the doubly labelled water (DLW) method and physical activity levels using a validated triaxial accelerometer36,37. The DLW method is an accurate way of determining metabolic rate with the advantage that the participants need not be confined, allowing us to investigate TEE in free-living conditions. TEE assessed using DLW was associated with age, anthropometric parameters, physical activity levels and step count per day (Fig. 3a and Extended Data Fig. 6b). Multivariate regression analysis revealed that FFM and steps per day were significant predicting factors of TEE (model 1, R2 value = 0.794; Extended Data Fig. 6c). Of note, FFM- and step-independent TEE as residuals in model 1 was significantly higher in the cold fertilization group than in the warm fertilization group, whereas it was indistinguishable between the warm and cold birth groups (Extended Data Fig. 6d). Thus, adjusted TEE for FFM and steps was significantly higher in the cold fertilization group than in the warm fertilization group (Fig. 3b). A similar increase in adjusted TEE was found in a disaggregated analysis for sex (Extended Data Fig. 6e). Multivariate regression analysis revealed that the TEE-increasing effect of fertilization season was independent of body size- and physical activity-related parameters, including FFM and step count (Fig. 3c). Together, we demonstrate a critical role of the season of fertilization in adaptive CIT, DIT and TEE, suggesting that the pre-fertilization-origin activation of BAT is sufficient to modulate whole-body EE.
a, Association of daily TEE measured by the DLW method with FFM, fat mass, step counts and physical activity level in Cohort 5 (n = 41). b, TEE adjusted for FFM and steps per day using an equation according to the multivariate regression analysis (model 1; Extended Data Fig. 6c) for predicting body size and physical activity-independent TEE of each participant. Warm birth group (n = 22) and cold birth group (n = 19) (left). Warm fertilization group (n = 20) and cold fertilization group (n = 21) (right). c, Univariate and multivariate regression analysis for estimating independent effects of birth and fertilization seasons on TEE. The warm and cold birth seasons were coded as 1 and 2, respectively. The warm and cold fertilization seasons were coded as 1 and 2, respectively. Model 2, R2 = 0.804, P < 0.001. Biologically independent samples (aâc). Pearsonâs correlation coefficient (r) and two-tailed P values (a). Data are mean ± s.e.m.; two-tailed P values by unpaired Studentâs t-test (b). Data are correlation coefficient by univariate Pearsonâs (for age, height, weight, FFM, fat mass, steps and physical activity) or Kendallâs rank correlation analysis (for sex and seasons) (c, left). Error bars indicate 95% CIs. Data are unstandardized ÎČ (middle) and standardized ÎČ (right) by multivariate regression model with backward stepwise method (model 2); two-tailed P values (middle and right). Error bars indicate 95% CIs.
The above observations led us to hypothesize that fertilization in the cold season may reduce the risk of obesity and metabolic diseases in offspring by protecting against an age-related decline in BAT. Clinical cross-sectional studies demonstrated that BAT activity is inversely correlated with adiposity and blood glucose, preventing age-related obesity, insulin resistance and cardiovascular diseases7,8. In Cohort 1, the inverse association between BAT activity and BMI was relatively weak (Fig. 4a and Extended Data Fig. 7a), likely due to the younger age and lower adiposity levels among participants6,38. Conversely, more pronounced effects of BAT-d on obesity-related traits such as BMI, body fat content, visceral fat area and waist circumference were observed in Cohort 2, which included a wider range of ages and adiposity levels (Fig. 4b and Extended Data Fig. 7b).
a, Associations of the season of fertilization and BAT activity with adiposity-related parameters in healthy young males (Cohort 1). b, Associations of the season of fertilization and BAT-d with adiposity-related parameters in the healthy participants with a wide range of ages (Cohort 2). SBP, systolic blood pressure; DBP, diastolic blood pressure. c, Impact of the seasons of birth and fertilization on adiposity-related parameters including BMI, body fat content, visceral fat area and waist circumference in b. d, Structural equation modelling for predicting the factors associated with BMI in a. e, Structural equation modelling for predicting the factors associated with BMI in b. Biologically independent samples (aâe). Pearsonâs (for age, BAT activity and BAT-d) or Kendallâs rank correlation analysis (for seasons) and two-tailed P value by correlation analysis (a,b). Data are mean ± s.e.m.; two-tailed P value by unpaired Studentâs t-test. Numbers of participants (n) are indicated on the graph (c). Structural equation modelling: standardized ÎČ and two-tailed P values (d,e). NS, not significant.
Next, we investigated whether the fertilization season is associated with adiposity. In Cohort 1, with a limited-range of ages and adiposity, BMI and related parameters did not exhibit a direct correlation with either the birth season or the fertilization season (Fig. 4a and Extended Data Figs. 1c and 8a). In Cohort 2, however, we detected modest yet significant associations of the cold fertilization season with decreased BMI, visceral fat area and waist circumference (Fig. 4b). Similarly, BMI, body fat content, visceral fat area and waist circumference were significantly lower in the cold fertilization group than in the warm fertilization group (Fig. 4c). Conversely, the season of birth had a negligible effect on adiposity in both Cohort 1 and Cohort 2 (Fig. 4a,b). Additionally, parameters such as skeletal muscle mass and cardiovascular function, including blood pressure and heart rate, remained unaffected by either the birth or fertilization season (Extended Data Fig. 8b,c).
To examine more deeply the intricate relationship between fertilization season, BAT and adiposity, we performed structural equation modelling based on the structural model hypothesized39. This analysis revealed that the fertilization season (coded as warm, 0 and cold, 1) positively correlated with BAT activity (Cohort 1: ÎČ = 0.145, P = 0.005, Fig. 4d; Cohort 2: ÎČ = 0.132, P = 0.022, Fig. 4e), which in turn negatively correlated with BMI (Cohort 1: ÎČ = â0.175, P < 0.001; Cohort 2: ÎČ = â0.547, P < 0.001). The direct association between the fertilization season and BMI, however, was not statistically significant (Cohort 1: ÎČ = â0.022, P = 0.671; Cohort 2: ÎČ = â0.041, P = 0.365). Based on these findings, we concluded that a decrease in BMI associated with the cold fertilization season is due to increased BAT activity.
Meteorological parameters, including temperature, vary seasonally, as depicted in Extended Data Fig. 9, and some of these parameters, along with temperature, can modulate BAT activity40,41. For example, studies in rodents demonstrated that photoperiod regulates BAT thermogenesis via the sympathetic nervous system or ÎŒ-opioid receptor signalling42,43. To pinpoint the environmental factor responsible for the intergenerational BAT activation, we performed an unbiased spatiotemporal meteorological analysis. This analysis involved examining associations between the participantâs BAT activity and environmental parameters before, during, and after pregnancy periods within their specific region of residence (Fig. 5a).
a, Schematic of the study design for the meteorological survey and the definition of pregnancy periods. The pregnancy period was divided into five periods: preconception (â12 to â9 months), the first trimester (â9 to â6 months), second trimester (â6 to â3 months), third trimester (â3 to 0 months) and postpartum (0 to 3 months). b, Schematic showing the extraction of meteorological data for pregnancy periods from Japan created by the JMA, the NARO and the NAOJ. Data were obtained for birth and fertilization regions. c, Multivariate logistic regression using the backward stepwise method to predict BAT activity in young male volunteers (n = 93, model 1). Model 1 was adjusted for age, BMI, medical history and lifestyle factors (smoking and shift work). Variables included BAT activity (binary: 1 or 0), age and BMI: (the tertile values: 1, 2 or 3), low birth weight (yes, 1 and no, 0), smoking status (never, former, current: 1, 2, 3) and shift work (never, former, current: 1, 2, 3). d, Independent effects of diurnal temperature variation on BAT activity in c (model 2). Diurnal variation, calculated as the difference between dairy maximum and minimum temperatures, was added alongside daily mean temperature and other meteorological parameters. Model 2 adjustments were identical to model 1. e, Participants were categorized by seasonal birth and fertilization conditions: (1) warm birth/warm fertilization (n = 77), (2) warm birth/cold fertilization (n = 111), (3) cold birth/warm fertilization (n = 82) and (4) cold birth and cold fertilization (n = 86). f,g, Combined effects of the seasons of birth and fertilization on BAT prevalence (f) and activity (g). Numbers of participants with active BAT/total participants are indicated on the bars. Biologically independent samples (câg). Data are adjusted ORs with 95% CIs as error bars: two-tailed P values by multivariate logistic regression (c,d); percentage: one-tailed P values by Fisherâs exact test (f); mean ± s.e.m.; two-tailed P values by unpaired Studentâs t-test (g).
A total of 93 participants in Cohort 1 completed a questionnaire regarding their regions of birth and conception, their lifestyles and meteorological questionnaire survey. We collected meteorological parameters, including outdoor temperature, humidity, precipitation, sunshine duration, daytime length and atmospheric pressure, at the region residence during five pregnancy periods: before conception (â12 to â9 months from the birth), the first trimester (â9 to â6 months), the second trimester (â6 to â3 months), the third trimester (â3 to 0 months) and after delivery (0 to 3 months) (Fig. 5a,b). A multivariate logistic regression analysis with adjustments for age, BMI, smoking status and shift work status (referred to as model 1) revealed that the daily mean outdoor temperature before fertilization correlated inversely with BAT activity (adjusted OR = 0.335, 95% CI 0.138â0.809, P = 0.015; Fig. 5c), while humidity, precipitation, sunshine duration and daytime length showed no significant association with BAT activity. Additionally, we discovered a positive association between BAT activity and daily maximum temperature (adjusted OR = 3.033, 95% CI 1.27â7.25, P = 0.013), which contradicted the negative association with the daily mean temperature. We hypothesize that diurnal temperature variation might serve as a latent environmental stimulus for activating BAT in the offspring. To test this, we calculated the diurnal range of temperature and incorporated it into model 1 as an independent variable (referred to as model 2). Notably, this parameter emerged as the most significant determining factor of BAT activity (adjusted OR = 1.61, 95% CI 1.028â2.519, P = 0.037; Fig. 5d). A similar association was found when diurnal temperature gap between daily maximum and mean temperature was incorporated in to the multivariate regression model (model 3; Extended Data Fig. 10a). These results suggest exposure to a lower outdoor temperature and larger diurnal temperature variation before conception preserves BAT activity of the offspring in adulthood.
Furthermore, the multivariate regression model revealed no association of BAT activity with outdoor temperature or its diurnal variation in the first, second and third trimesters (Fig. 5c,d and Extended Data Fig. 10a), suggesting a negligible effect of maternal thermal stress during gestation, consistent with earlier studies in mice28,43. We also observed an inverse association between the daily mean outdoor temperature and diurnal temperature fluctuation after delivery and BAT activity (Fig. 5c and Extended Data Fig. 10a). We then tested the potential synergistic effect of thermal stress before conception and after delivery on BAT. To this end, we categorized the participants into four groups: (1) those with warm birth and warm fertilization seasons, (2) those with warm birth and cold fertilization seasons, (3) those with cold birth and warm fertilization seasons, and (4) those with cold birth and cold fertilization seasons (Fig. 5e). Increases in the BAT prevalence and activity by the cold fertilization season were clearly confirmed in the cold birth group whereas they were only marginal in the warm birth group (Fig. 5f,g). It is possible that cold exposure after delivery enhances the BAT-activating effect of preconception cold exposure although this requires further investigation. However, thermal exposure before conception is likely the primary stimulus because a clear linear-by-linear association between diurnal temperature gap and BAT prevalence was detected specifically in a period before conception (Extended Data Fig. 10b).
In summary, the spatiotemporal meteorological analysis revealed that a low outdoor temperature and a large diurnal temperature variation just before conception preserves BAT activity and energy homeostasis in human offspring (Fig. 6).
Preconception exposure to low outdoor temperature and temperature gap affects offspringâs metabolic phenotype, promoting higher EE in humans. Our findings propose a conceptional theory, named PfOHaD. This concept suggests that environmental factors, such as temperature exposure before conception, can programme physiological traits in offspring, potentially influencing their health outcomes across generations.
Discussion
Our study demonstrated that the season of fertilization significantly influences BAT development and metabolic health in adulthood. Utilizing healthy volunteers and employing three distinct methods (FDG-PET/CT, NIR-TRS and DLW), we observed a potential link between EE and the season of fertilization, which may be mediated by BAT activity. These findings extend the work by Sun et al.28, who reported that individuals conceived during colder months were 3.2% more likely to possess active BAT, whereas those conceived in warmer months were more likely to lack active BAT. They suggested that the season of conception is linked to BAT development in humans, proposing an intergenerational activation of BAT through paternal lineage, potentially involving sperm DNA methylation as demonstrated in mouse models. Incorporating meteorological analyses, we found that not only outdoor temperature but also diurnal temperature variations impact BAT activity, with effects observed based on the season of fertilization rather than birth season. These findings support and expand the theory of Developmental Origins of Health and Disease (DOHaD) to encompass the concept of Pre-fertilization Origins of Health and Disease (PfOHaD), as discussed in later sections.
A major strength of our study is the thorough assessment of BAT using the gold-standard method and a large sample size of healthy participants. Prospective studies demonstrated that BAT remains metabolically inactive in warm conditions regardless of its maximal thermogenic capacity5,32,44,45. Moreover, BAT activity varies seasonally depending on age, sex and adiposity5,8,40,46. These factors impose a major limitation on retrospective assessment of BAT using clinical scans. In the present study, we ensured a sensitive evaluation of BAT functionality with season-matched FDG-PET/CT and acute nonshivering cold exposure. This analysis included over 350 young, lean male participants to minimize potential fluctuations in BAT activity. While FDG-PET may underestimate BAT activity in conditions such as insulin resistance47, our all participants of Cohort 1 were healthy individuals. Our well-designed approach and the largest sample size in this field, allow us to certify the intergenerational influence of cold stress on BAT activity in humans.
Of note, we validated the fertilization season-specific activation of BAT in an independent population of Cohort 2 with no discernible effect of the birth season. Despite a wide range of ages and adiposity in Cohort 2 and including both males and females, multivariate regression revealed that BAT activation by the cold fertilization season is independent of age, sex and adiposity. Moreover, the difference in the BAT prevalence between the warm and cold fertilization groups was 11.9% for Cohort 2, consistent with Cohort 1 (14.6%). Notably, these differences were four-times higher than those reported in the previous retrospective analysis of oncology scans (3.2%)28. Our results demonstrate the extent to which preconception seasonal acclimatization enhances BAT activity in offspring.
Previous studies demonstrated BATâs role in nonshivering adaptive thermogenesis (CIT and DIT)12,34,35,48. Our study is the first to show that preconception environmental exposure enhances whole-body EE and adaptive thermogenesis due to a significant increase in BAT activity. CIT for the cold fertilization group was 1.5-times higher than the warm group. In contrast, resting EE at 27 °C was unaffected by the fertilization season, as BAT remains inactive in thermoneutral conditions. The impact of the fertilization season on CIT was negligible in the summer, when BAT activity is low31. These results suggest that preconception cold exposure is sufficient to increase adaptive thermogenesis and whole-body EE. Notably, TEE under free-living conditions increased by ~5.8% in the cold fertilization group compared with the warm group, indicating that intergenerational inheritance of cold adaptation enhances whole-body EE in humans. Future study employing 15O-O2 and 11C-acetate PET/CT is required for validating the contribution of BAT to intergenerational control of whole-body EE.
Fertilization during the cold season was linked to a lower BMI in Cohort 2, which encompassed a wider range of age and adiposity levels. Structural equation model suggested that the BMI-decreasing effect is mediated by BAT activation. Given the absence of a significant impact of the fertilization season on BMI in young lean individuals (Cohort 1), we propose that the beneficial metabolic effect of preconception cold exposure is greater in middle-aged and older populations. Consistent with this notion, a cross-sectional study involving over 4,500 middle-aged males reported that the prevalence of metabolic syndrome is high in those born in the spring (with an estimated summer fertilization season) compared with those born in the fall (with an estimated winter fertilization season)49. Similar trends were reported by a large cohort study that included male and female participants50,51. The current study highlights the need of investigating the hypothesis that global warming may be contributing to disruptions in energy homeostasis and the ongoing obesity pandemic. Together, our present findings suggest a crucial role of intergenerational regulation of BAT in the systemic energy homeostasis, reducing the propensity for age-associated metabolic diseases. Our findings align with earlier studies reporting protective roles of BAT against insulin resistance and cardiovascular diseases7,38,40.
The mechanisms of intergenerational activation of human BAT have remained unclear because the key meteorological factors remained undetermined. Our unique spatiotemporal meteorological analysis, however, identified the daily mean outdoor temperature before conception as a significant determinant of BAT metabolic fate in adult offspring. This finding is compatible with findings in mice28. Other parameters, such as daytime length, sunshine duration, humidity and precipitation, before conception were not associated with BAT activity. Multivariate logistic regression suggested that diurnal temperature variations serve as a novel meteorological factor that mediates BAT activation. Although the mechanisms remain unclear, the sympathetic nervous system (SNS) may be involved in this temperature gap-induced activation. For example, the cold pressor test (cold water immersion of the limbs) increases SNS activity in humans without altering core body temperature52. Further investigations are needed to confirm whether the SNS plays a role in passing on BAT activity to the next generation in humans.
Potential factors that may influence the intergenerational activation of BAT include individual differences in dietary and behavioural pattern, genetic variants, and other unmeasurable variables such as living indoor temperature and gut microbiota composition24,53,54,55,56,57,58,59,60. The use of pharmacological agents such as adrenomimetics may also confound the association of preconception thermal stress with BAT44,61,62,63. We note, however, that all our participants were healthy individuals who did not regularly take medications, and neither smoking history nor shift work status influenced the BAT activation.
A notable limitation of our study is the use of a fixed cold exposure at 19 °C to assess BAT activity with FDG-PET/CT. We cannot rule out the possibility that our fixed cold exposure might be insufficient for some participants with low cold perception, resulting in low BAT activity. An alternative approach would be FDG-PET/CT with personalized cold exposure protocol64. The use of only FDG as a radiotracer is also a limitation because FDG uptake does not directly reflect total heat production in BAT and its anatomical presence47, although it is proportional to nonshivering CIT. However, fixed protocols like ours have been recognized as suitable for evaluating BATâs thermogenic responsiveness to a certain stimulus, which is one of the most physiologically important readouts64,65. Nevertheless, our findings need to be validated by future studies employing PET/CT with various tracers in combination with personalized cold protocols13,61,64,65. Moreover, our correlational results do not exclude possibility that other tissues, including skeletal muscle66,67, contribute to the intergenerational cold adaptation and metabolic improvement. Additional investigations are required to establish BAT as a causative driver of metabolic improvements due to pre-fertilization cold exposure. Another limitation of this study is its geographically focus on Japan. Future studies should test the effects of different geographic regions of living and races of individuals on the intergenerational activation of BAT. In addition, our meteorological approach faces challenges in determining whether the intergenerational regulation of BAT originates from the paternal lineage, as reported in mice28. In this regard, two independent study groups reported that maternal cold exposure does not activate BAT in mice28,43. We also emphasize that no meteorological parameter during the first, second and third trimesters correlated with BAT activity, suggesting minimal effects of maternal lineage during gestation. Although it may not be feasible to experimentally validate whether it is paternal lineage in humans, alternative approaches may involve investigations of cold-induced epigenetic changes in human sperm68,69, as discussed in the below paragraph. It seems likely that the intergenerational control of BAT by preconception cold exposure is linked to the paternal lineage.
In future studies, it would be useful to identify the molecular mechanisms underlying intergenerational epigenetic inheritance of cold exposure and to examine the effect of cold exposure on sperm, ovum, fertilized eggs and embryos in humans. Earlier studies in small rodents suggested that certain molecular mechanisms in sperm govern the intergenerational inheritance of paternal metabolic stress68,69. First, a previous DNA pyrosequencing study revealed that paternal preconception cold exposure induces alterations of DNA methylation in mouse sperm28. The specific genes or sites targeted by cold-induced DNA methylation, as well as their causal link to the BAT activation remain undetermined. Second, although sperm chromatin undergoes massive epigenetic reprogramming through histoneâprotamine exchange, minimal histones (~1% in mice and ~10% in humans) are retained in mature sperm70. The retained histones carry epigenetic marks, for example methylation, which are transferred to the embryo and play a role in intergenerational physiological adaptation71,72,73. Third, transfer RNA-derived small RNAs (tsRNA) have the potential to be transmitted to the zygote and are proposed as a molecule responsible for germline epigenetic transmission27,74,75,76. Future studies should investigate the roles of these mechanisms in the intergenerational inheritance of cold exposure.
Together, these findings provide evidence of a discernible impact of preconception cold exposure on BAT metabolic fate, adaptive thermogenesis and systemic energy homeostasis in human offspring. Our findings indicate a perspective beyond the DOHaD theory, which acknowledges that environmental stress, for example poor nutritional conditions, during pregnancy or lactation triggers predictive adaptation during embryonic and postnatal development via the maternal lineage77. Because the intergenerational activation of BAT is initiated before fertilization, we propose a conceptional theory, named PfOHaD, which can theoretically originate from both maternal and paternal lineages. The PfOHaD-based activation of BAT may be a sophisticated predictive cold adaptation through specific mechanisms, enabling offspring to survive despite cold climates and thereby contributing to species conservation. Understanding the molecular mechanisms of epigenetic transmission between generations, as well as how cellular memories stored in specific cell types operate, will open research opportunities to develop therapeutic approaches aimed at attenuating an ageing-associated decline in BAT activity and achieving cardiometabolic benefits in humans.
Methods
Participants
Healthy adult volunteers were provided information about the study and gave their written informed consent for participation in the FDG-PET/CT examination and the meteorological questionnaire survey. The FDG-PET/CT and NIR-TRS data obtained in our previous studies8,30 were enrolled in the analysis by obtaining the written informed consent or through an opt-out process. The data of the CIT, DIT, and TEE obtained in our previous studies31,35,37 were enrolled in this study through an opt-out process. Inclusion criteria included age (Cohorts 1, 3 and 4, 18 years and older8,31,35; Cohort 2, 20 years and older30 and Cohort 5, 3â6 years37). Exclusion criteria included individuals with metabolic diseases, those who regularly take medications for diabetes, hyperlipidaemia or hypertension, heavy smokers (>21 cigarettes per day), those drinking more than the average amount of alcohol (30 g alcohol per day or more), those who are pregnant or breastfeeding, and those who are considered unsuitable for the study by investigators (Supplementary Table 1 and Supplementary Fig. 1aâf). In addition, only male participants aged 18â29 years old were included in the analysis for Cohort 1 to minimize the confound effects of age and sex on the results, whereas both sexes were included in Cohorts 2 and 5 to test whether our findings can be generalized regardless of sex. The written informed consent for participation was obtained from 242 participants of Cohort 1 (68.0%) and all 268 participants of Cohort 2 (100%). Data of the FDG-PET/CT examination from the remaining 114 participants of Cohort 1 (32.0%) and data of CIT/DIT obtained in our previous studies were utilized under an opt-out basis. CIT was measured both in summer and in winter in a randomized crossover design31, whereas the other experiments were performed in observational study designs with no randomization. The FDG-PET/CT, NIR-TRS, CIT, DIT measurements and the DLW examination as well as measurements of anthropometric parameters were performed by experienced physicians/radiologists/investigators who were blinded to the experimental group and study hypothesis. Meteorological parameters were obtained by two research assistants who were blinded to the experimental groups.
The study protocols were approved by the Institutional Research Ethics Review Boards of the University of Tokyo (23-585) and Tohoku University (35632). The trial was registered at http://www.umin.ac.jp/ctr/ (UMIN000050690). The primary outcomes were BAT activity/density, EE and adiposity (BMI, visceral fat area, waist circumference) and secondary outcomes included FFM, fat mass, heart rate and blood pressure.
FDG-PET/CT
Cold-activated BAT was assessed by FDG-PET/CT following standardized nonshivering cold exposure, as reported previously8,12. All FDG-PET/CT examinations were performed in the winter (December through March). After fasting for ~12 h, participants remained in a room at 19 °C for 2 h. The participants received 18F-FDG (1.7 MBq kgâ1 body weight) intravenously after 1 h of cold exposure and remained in the same cold conditions for another hour. PET/CT was performed using a PET/CT system (Aquiduo, Toshiba Medical Systems), Biograph 16 (Siemens Medical Solutions) or Discovery PET/CT 600 (GE Healthcare). Cold-induced BAT activity was quantified by the standardized uptake value (SUV) of FDG in the supraclavicular adipose deposits with Hounsfield Units from â300 to â10. On the basis of the presence of detectable BAT activity greater than SUV 2.0, participants were divided into high and low BAT groups8,12. Experienced investigators monitored participantâs condition during cold exposure and no adverse effect of cold exposure was reported.
NIR-TRS
The participants relaxed in a room at 23â27 °C and 3-cm probes were placed at the skin of the supraclavicular region. Total haemoglobin concentration ([total Hb]) in the supraclavicular region was measured based on the optical properties evaluated by NIR-TRS29,30,78. Because the abundance of capillaries in BAT helps to distinguish it from other tissues, including white adipose tissue, the supraclavicular [total Hb] is an indicator of BAT-d, which is correlative to SUV by FDG-PET/CT. NIR-TRS data were extracted every 10 s and averaged over 1 min using a NIR-TRS system (TRS-20; Hamamatsu Photonics K.K.). The obtained NIR-TRS parameters were adjusted for the thickness of subcutaneous fat in the region by B-mode ultrasound (Vscan Dual Probe; GE Vingmed Ultrasound AS). Based on the median value of the supraclavicular [total Hb], participants were divided into high and low BAT-d groups. As control regions, [total Hb] in the deltoid skeletal muscle and in the abdominal subcutaneous adipose tissue were measured.
Anthropometric parameters
BMI was calculated as the body weight in kilograms divided by the square of the height in metres (kg mâ2) and the per cent of body fat was estimated using the multifrequency bioelectric impedance method (InBody 320 and 720 Body Composition Analyser; Biospace) or a dual-energy X-ray absorptiometry scan (Lunar Prodigy; GE Healthcare). In concurrence with FDG-PET/CT, the visceral and subcutaneous fat areas at the abdominal level of L4âL5 were estimated from the CT images. The visceral fat area of participants undergoing the NIR-TRS examination was estimated using a bioelectrical impedance analysis (EW-FA90; Panasonic). Waist circumference was measured using a flexible narrow, nonstretchable tape. Systolic blood pressure, diastolic blood pressure, and heart rate were measured by an automated sphygmomanometer (HBP-9020; Omron Healthcare). The FFM of participants of the DLW procedure was calculated from the total body water (TBW) using the hydration factor by the International Atomic Energy Agency36,37. Fat mass was calculated by subtracting the FFM (kg) from the body weight.
Cold-induced thermogenesis
Whole-body EE and CIT were measured by indirect calorimetry in the summer (July to September) and winter (December to March) in a randomized crossover design31. After fasting for ~12 h, participants relaxed in a sitting position in a room at 27 °C for at least 30 min. Oxygen consumption (VO2) and carbon dioxide production (VCO2) were then recorded for 20â30 min using a respiratory gas analyser connected to a ventilated hood (AR-1, Arco System). After 2 h cold exposure at 19 °C, VO2 and VCO2 were recorded again. The stable value of the last 10-min period was used to calculate EE. Whole-body EE was adjusted for FFM, the major component of the individual variation of EE. CIT was calculated as the difference in the adjusted EE between before and after cold exposure.
Diet-induced thermogenesis
Whole-body EE before and after meal ingestion was measured using a respiratory gas analyser (AR-1, Arco System) in winter (December to March)35. After fasting for ~12 h, participants relaxed on a bed in a room at 27 °C, and VO2 and VCO2 were recorded for ~30 min. The participants were then given a test meal with a total energy of 7.9 kcal kgâ1 body weight in 10 min. The energy ratio of the meal was 11% protein, 38% fat and 51% carbohydrate. After 15, 45, 75 and 105 min, VO2 and VCO2 were measured for 20 min. The stable value in the last 10-min period was used to calculate the EE. Whole-body EE was adjusted for FFM. Postprandial thermogenesis was calculated as the change of adjusted EE from baseline and the incremental area under the curve of postprandial thermogenesis over 2 h was calculated for estimating DIT.
Total energy expenditure
Daily TEE was measured over 1 week using the DLW procedure, the gold-standard method to determine whole-body EE under free-living conditions36,37. All DLW procedures were performed in the winter (December). Upon accessing the preschool, a urine specimen was collected to measure the baseline 2H and 18O before administering DLW (day 0). Each participant was given a beverage containing a premixed dose of ~0.12 g kgâ1 estimated TBW of 2H2O (99.8 at.%, Taiyo Nippon Sanso) and 2.5 g kgâ1 estimated TBW of H218O (10.0 at.%, Taiyo Nippon Sanso). The urine samples were then collected on the following day (day 1) and on day 8. The urine specimens were analysed using isotope ratio mass spectrometry (Hydra 20â20; Sercon). The dilution space of 2H and 18O (Nd and No, respectively) was determined by dividing the amount of the given tracer using the intercept method in two specimens from day 0 and two samples from the days 1 and 8. The TBW was calculated as the mean of Nd (mol) divided by 1.043 and No (mol) divided by 1.007 (ref. 79). Carbon dioxide production rates (rCO2, mol dâ1) were calculated using the following equation: rCO2 (mol dâ1) = 0.4554 Ă TBW (mol) Ă (1.007 Ă ko â 1.043 Ă kd), where ko and kd represent the 18O and 2H elimination rates per day, respectively. TEE was determined using a modified Wier equation based on rCO2 and 24-h respiratory quotient (RQ) estimated as 0.87 (ref. 37): TEE (kcal dâ1) = 1.106 Ă rCO2 (l dâ1) + 3.94 Ă (rCO2/RQ).
Physical activity
In concurrence with the DLW procedure for 1 week, the participants were asked to wear a previously validated triaxial accelerometer (Actimarker, Panasonic) over the waist80 from morning till night, except during bathing and bedtime. The obtained data during the experimental period were used for calculating daily mean steps and daily mean physical activity levels.
Meteorological parameters during periods of birth and fertilization
Day of fertilization was estimated as the day 266 days before the due date (day of birth). To define seasons of birth and fertilization, the year was divided into the cold season (1 January through 15 April and 17 October through 31 December) and warm season (16 April through 16 October) by splitting spring and autumn. To examine the effects of meteorological exposure before, during and after pregnancy on BAT of the offspring, five pregnancy periods were defined based on the days of birth and fertilization; before conception (â12 to â9 months from the birth), the first trimester (â9 to â6 months), the second trimester (â6 to â3 months), the third trimester (â3 to 0 months) and after delivery (0â3 months). Meteorological parameters in these periods were obtained from the meteorological and climatic big databases for Japan: the Agro-Meteorological Grid Square Data by the National Agriculture and Food Research Organization (NARO) for outdoor temperature, its diurnal variation, precipitation and sunshine duration81; the Japan Meteorological Agency (JMA) database for humidity and atmospheric pressure; the National Astronomical Observatory of Japan (NAOJ) database for sunrise and sunset times for 2012â2020 to calculate the daytime length for each calendar day.
Statistical analysis
Data are expressed as the mean with s.e.m. or the proportion unless otherwise specified. Statistical analysis was carried out using a statistical software package (IBM SPSS Statistics v.29.0 and Amos 29.0, IBM Japan; Microsoft Office Excel 2016, Microsoft Japan). The difference in continuous variables between two experimental groups was analysed using an unpaired Studentâs t-test. The effect of the birth or fertilization seasons on the BAT prevalence, as well as differences in the proportion of female participants between the experimental groups, was analysed by Fisherâs exact test or linear-by-linear association chi-squared test. Subgroup analysis for sex was performed in Cohorts 2 and 5 to test whether intergenerational effect of seasons of birth and fertilization was consistent across males and females. Simple correlations were assessed using a univariate linear regression analysis and Pearsonâs or Kendallâs correlation coefficient. Whole-body EE measured by indirect calorimetry was adjusted for FFM using a linear regression equation31. The adjusted EE before and after 2-h cold exposure was compared using a paired t-test with three outliers by the SmirnovâGrubbsâ test removed31. Postprandial EE over 2 h with one noncompliance of fasting and one outlier removed35 was tested by two-way repeated measures analysis of variance (ANOVA) on a within-subject factor (time) and a between-subject factor (season), with a post hoc unpaired t-test performed when the interaction effect (time Ă season) was statistically significant. TEE was adjusted for FFM and step counts by means of the stepwise multivariate regression model to estimate body size- and physical activity-independent TEE. The magnitude of the independent associations between BAT and the birth/fertilization seasons was estimated by calculating ORs and 95% CIs using a multivariate logistic regression model with adjustments for age and BMI. The association between BAT and meteorological parameters were estimated by means of multivariate logistic regression models with the backward method for the adjustment for age, BMI, smoking status, shift work status and a medical history of low birth weight. Direct and indirect, BAT-mediated inferences of the birth/fertilization seasons on BMI were estimated by employing structural equation model39. No statistical methods were used to predetermine sample sizes, but our sample sizes are similar to those reported in previous publications31,35,82. A P value <0.05 was considered statistically significant.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Full individual data are not publicly available due to them containing information that could compromise research participant privacy or consent. De-identified and processed data can be requested from the corresponding authors for academic purposes after completing a signed data access form and obtaining the approvals of the Institutional Research Ethics Review Boards. The study protocol is provided with the publication. Meteorological parameters were available in the following meteorological and climatic big databases for Japan: (1) The Agro-Meteorological Grid Square Data by NARO, https://amu.rd.naro.go.jp/wiki_open/doku.php?id=start; (2) the JMA database, https://www.data.jma.go.jp/stats/etrn/index.php; and (3) the NAOJ database, https://eco.mtk.nao.ac.jp/koyomi/dni/. Source data are provided with this paper.
References
BlĂŒher, M. Metabolically healthy obesity. Endocr. Rev. 41, bnaa004 (2020).
Whitlock, G. et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 373, 1083â1096 (2009).
Cohen, P. & Kajimura, S. The cellular and functional complexity of thermogenic fat. Nat. Rev. Mol. Cell Biol. 22, 393â409 (2021).
Sellers, A. J., Khovalyg, D., Plasqui, G. & van Marken Lichtenbelt, W. High daily energy expenditure of Tuvan nomadic pastoralists living in an extreme cold environment. Sci. Rep. 12, 20127 (2022).
Saito, M. et al. High incidence of metabolically active brown adipose tissue in healthy adult humans: effects of cold exposure and adiposity. Diabetes 58, 1526â1531 (2009).
Pfannenberg, C. et al. Impact of age on the relationships of brown adipose tissue with sex and adiposity in humans. Diabetes 59, 1789â1793 (2010).
Becher, T. et al. Brown adipose tissue is associated with cardiometabolic health. Nat. Med. 27, 58â65 (2021).
Matsushita, M. et al. Impact of brown adipose tissue on body fatness and glucose metabolism in healthy humans. Int. J. Obes. 38, 812â817 (2014).
Morrison, S. F. & Nakamura, K. Central mechanisms for thermoregulation. Annu. Rev. Physiol. 81, 285â308 (2019).
Inagaki, T., Sakai, J. & Kajimura, S. Transcriptional and epigenetic control of brown and beige adipose cell fate and function. Nat. Rev. Mol. Cell Biol. 17, 480â495 (2016).
Takahashi, H. et al. MYPT1-PP1ÎČ phosphatase negatively regulates both chromatin landscape and co-activator recruitment for beige adipogenesis. Nat. Commun. 13, 5715 (2022).
Yoneshiro, T. et al. Recruited brown adipose tissue as an antiobesity agent in humans. J. Clin. Invest. 123, 3404â3408 (2013).
Orava, J. et al. Different metabolic responses of human brown adipose tissue to activation by cold and insulin. Cell Metab. 14, 272â279 (2011).
Yoneshiro, T. et al. BCAA catabolism in brown fat controls energy homeostasis through SLC25A44. Nature 572, 614â619 (2019).
Muzik, O., Mangner, T. J., Leonard, W. R., Kumar, A. & Granneman, J. G. Sympathetic innervation of cold-activated brown and white fat in lean young adults. J. Nucl. Med. 58, 799â806 (2017).
Bornstein, M. R. et al. Comprehensive quantification of metabolic flux during acute cold stress in mice. Cell Metab. 35, 2077â2092 e2076 (2023).
van der Lans, A. A. et al. Cold acclimation recruits human brown fat and increases nonshivering thermogenesis. J. Clin. Invest. 123, 3395â3403 (2013).
OâMara, A. E. et al. Chronic mirabegron treatment increases human brown fat, HDL cholesterol, and insulin sensitivity. J. Clin. Invest. 130, 2209â2219 (2020).
Sharp, L. Z. et al. Human BAT possesses molecular signatures that resemble beige/brite cells. PLoS ONE 7, e49452 (2012).
Wu, J. et al. Beige adipocytes are a distinct type of thermogenic fat cell in mouse and human. Cell 150, 366â376 (2012).
Ito, R. et al. Mitochondrial biogenesis in white adipose tissue mediated by JMJD1A-PGC-1 axis limits age-related metabolic disease. iScience 27, 109398 (2024).
Abe, Y. et al. Histone demethylase JMJD1A coordinates acute and chronic adaptation to cold stress via thermogenic phospho-switch. Nat. Commun. 9, 1566 (2018).
Inoue, S. I. et al. Short-term cold exposure induces persistent epigenomic memory in brown fat. Cell Metab. https://doi.org/10.1016/j.cmet.2024.05.011 (2024).
Yoneshiro, T. et al. Impact of UCP1 and ÎČ3AR gene polymorphisms on age-related changes in brown adipose tissue and adiposity in humans. Int. J. Obes. 37, 993â998 (2013).
Ăst, A. et al. Paternal diet defines offspring chromatin state and intergenerational obesity. Cell 159, 1352â1364 (2014).
Ng, S. F. et al. Paternal high-fat diet consumption induces common changes in the transcriptomes of retroperitoneal adipose and pancreatic islet tissues in female rat offspring. FASEB J. 28, 1830â1841 (2014).
Yoshida, K. et al. ATF7-dependent epigenetic changes are required for the intergenerational effect of a paternal low-protein diet. Mol. Cell 78, 445â458.e446 (2020).
Sun, W. et al. Cold-induced epigenetic programming of the sperm enhances brown adipose tissue activity in the offspring. Nat. Med. 24, 1372â1383 (2018).
Nirengi, S., Yoneshiro, T., Sugie, H., Saito, M. & Hamaoka, T. Human brown adipose tissue assessed by simple, noninvasive near-infrared time-resolved spectroscopy. Obesity 23, 973â980 (2015).
Tanaka, R. et al. Vigorous-intensity physical activities are associated with high brown adipose tissue density in humans. Int. J. Environ. Res. Public Health 17, 2796 (2020).
Yoneshiro, T. et al. Brown adipose tissue is involved in the seasonal variation of cold-induced thermogenesis in humans. Am. J. Physiol. Regul. Integr. Comp. Physiol. 310, R999âR1009 (2016).
Yoneshiro, T. & Saito, M. Activation and recruitment of brown adipose tissue as anti-obesity regimens in humans. Ann. Med. 47, 133â141 (2015).
Vosselman, M. J. et al. Brown adipose tissue activity after a high-calorie meal in humans. Am. J. Clin. Nutr. 98, 57â64 (2013).
Hibi, M. et al. Brown adipose tissue is involved in diet-induced thermogenesis and whole-body fat utilization in healthy humans. Int. J. Obes. 40, 1655â1661 (2016).
Aita, S. et al. Brown fat-associated postprandial thermogenesis in humans: different effects of isocaloric meals rich in carbohydrate, fat, and protein. Front. Nutr. 9, 1040444 (2022).
Yamada, Y. et al. Variation in human water turnover associated with environmental and lifestyle factors. Science 378, 909â915 (2022).
Yamada, Y. et al. Association between water and energy requirements with physical activity and fat-free mass in preschool children in Japan. Nutrients 13, 4169 (2021).
Yoneshiro, T. et al. Age-related decrease in cold-activated brown adipose tissue and accumulation of body fat in healthy humans. Obesity 19, 1755â1760 (2011).
Calis, J. C. et al. Severe anemia in Malawian children. N. Engl. J. Med. 358, 888â899 (2008).
Au-Yong, I. T., Thorn, N., Ganatra, R., Perkins, A. C. & Symonds, M. E. Brown adipose tissue and seasonal variation in humans. Diabetes 58, 2583â2587 (2009).
Kooijman, S. et al. Prolonged daily light exposure increases body fat mass through attenuation of brown adipose tissue activity. Proc. Natl Acad. Sci. USA 112, 6748â6753 (2015).
Sun, L. et al. [(11)C]carfentanil PET imaging for studying the peripheral opioid system in vivo: effect of photoperiod on mu-opioid receptor availability in brown adipose tissue. Eur. J. Nucl. Med. Mol. Imaging 50, 266â274 (2023).
Oelkrug, R. et al. Maternal brown fat thermogenesis programs glucose tolerance in the male offspring. Cell Rep. 33, 108351 (2020).
Cypess, A. M. et al. Activation of human brown adipose tissue by a ÎČ3-adrenergic receptor agonist. Cell Metab. 21, 33â38 (2015).
Blondin, D. P. et al. Human brown adipocyte thermogenesis is driven by ÎČ2-AR stimulation. Cell Metab. 32, 287â300.e287 (2020).
Blondin, D. P. et al. Brown adipose tissue metabolism in women is dependent on ovarian status. Am. J. Physiol. Endocrinol. Metab. https://doi.org/10.1152/ajpendo.00077.2024 (2024).
Blondin, D. P. et al. Selective impairment of glucose but not fatty acid or oxidative metabolism in brown adipose tissue of subjects with type 2 diabetes. Diabetes 64, 2388â2397 (2015).
Chen, K. Y. et al. Brown fat activation mediates cold-induced thermogenesis in adult humans in response to a mild decrease in ambient temperature. J. Clin. Endocrinol. Metab. 98, E1218âE1223 (2013).
Lee, S. R. et al. High prevalence of metabolic syndrome in middle-aged men born in spring. Metab. Syndr. Relat. Disord. 19, 543â548 (2021).
Lv, J. et al. The associations of month of birth with body mass index, waist circumference, and leg length: findings from the China Kadoorie Biobank of 0.5 million adults. J. Epidemiol. 25, 221â230 (2015).
Phillips, D. I. & Young, J. B. Birth weight, climate at birth and the risk of obesity in adult life. Int. J. Obes. Relat. Metab. Disord. 24, 281â287 (2000).
Allison, E. Y. et al. Effects of single- and bilateral limb immersion on systemic and cerebral hemodynamic responses to the cold pressor test. J. Appl. Physiol. 137, 873â882 (2024).
Laurila, S. et al. Secretin activates brown fat and induces satiation. Nat. Metab. 3, 798â809 (2021).
Herz, C. T. et al. Characterization of endogenous bile acid composition in individuals with cold-activated brown adipose tissue. Mol. Cell. Endocrinol. 536, 111403 (2021).
Stroh, A. M. & Stanford, K. I. Exercise-induced regulation of adipose tissue. Curr. Opin. Genet. Dev. 81, 102058 (2023).
Matsushita, M. et al. Diurnal variations of brown fat thermogenesis and fat oxidation in humans. Int. J. Obes. 45, 2499â2505 (2021).
Yue, K., Rensen, P. C. & Kooijman, S. Circadian control of white and brown adipose tissues. Curr. Opin. Genet. Dev. 80, 102056 (2023).
Seoane-Collazo, P. et al. Central nicotine induces browning through hypothalamic Îș opioid receptor. Nat. Commun. 10, 4037 (2019).
Ishida, Y. et al. Genetic evidence for involvement of ÎČ2-adrenergic receptor in brown adipose tissue thermogenesis in humans. Int. J. Obes. https://doi.org/10.1038/s41366-024-01522-6 (2024).
Cani, P. D. & Van Hul, M. Gut microbiota in overweight and obesity: crosstalk with adipose tissue. Nat. Rev. Gastroenterol. Hepatol. 21, 164â183 (2024).
Blondin, D. P. et al. Human brown adipocyte thermogenesis is driven by ÎČ2-AR stimulation. Cell Metab. 32, 287â300 e287 (2020).
Dozio, E. et al. Epicardial adipose tissue GLP-1 receptor is associated with genes involved in fatty acid oxidation and white-to-brown fat differentiation: a target to modulate cardiovascular risk? Int. J. Cardiol. 292, 218â224 (2019).
Bu, T., Sun, Z., Pan, Y., Deng, X. & Yuan, G. Glucagon-like peptide-1: new regulator in lipid metabolism. Diabetes Metab. J. 48, 354â372 (2024).
Chen, K. Y. et al. Brown adipose reporting criteria in imaging studies (BARCIST 1.0): recommendations for standardized FDG-PET/CT experiments in humans. Cell Metab. 24, 210â222 (2016).
Cypess, A. M. et al. Emerging debates and resolutions in brown adipose tissue research. Cell Metab. https://doi.org/10.1016/j.cmet.2024.11.002 (2024).
Sellers, A. J. et al. Cold acclimation with shivering improves metabolic health in adults with overweight or obesity. Nat. Metab. 6, 2246â2253 (2024).
Remie, C. M. E. et al. Metabolic responses to mild cold acclimation in type 2 diabetes patients. Nat. Commun. 12, 1516 (2021).
Matsumura, Y., Wei, F. Y. & Sakai, J. Epitranscriptomics in metabolic disease. Nat. Metab. 5, 370â384 (2023).
Sun, W., von Meyenn, F., Peleg-Raibstein, D. & Wolfrum, C. Environmental and nutritional effects regulating adipose tissue function and metabolism across generations. Adv. Sci. 6, 1900275 (2019).
Okada, Y. & Yamaguchi, K. Epigenetic modifications and reprogramming in paternal pronucleus: sperm, preimplantation embryo, and beyond. Cell. Mol. Life Sci. 74, 1957â1967 (2017).
Le BlĂ©vec, E., MuroĆovĂĄ, J., Ray, P. F. & Arnoult, C. Paternal epigenetics: mammalian sperm provide much more than DNA at fertilization. Mol. Cell. Endocrinol. 518, 110964 (2020).
Yamaguchi, K. et al. Re-evaluating the localization of sperm-retained histones revealed the modification-dependent accumulation in specific genome regions. Cell Rep. 23, 3920â3932 (2018).
Yoshida, K. et al. Mapping of histone-binding sites in histone replacement-completed spermatozoa. Nat. Commun. 9, 3885 (2018).
Chen, Q. et al. Sperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder. Science 351, 397â400 (2016).
Sharma, U. et al. Biogenesis and function of tRNA fragments during sperm maturation and fertilization in mammals. Science 351, 391â396 (2016).
Zhang, Y., Shi, J., Rassoulzadegan, M., Tuorto, F. & Chen, Q. Sperm RNA code programmes the metabolic health of offspring. Nat. Rev. Endocrinol. 15, 489â498 (2019).
Reynolds, R. M. et al. Maternal obesity during pregnancy and premature mortality from cardiovascular event in adult offspring: follow-up of 1 323 275 person years. Brit. Med. J. 347, f4539 (2013).
Fuse, S. et al. Relationships between plasma lipidomic profiles and brown adipose tissue density in humans. Int. J. Obes. 44, 1387â1396 (2020).
Speakman, J. R. et al. A standard calculation methodology for human doubly labeled water studies. Cell Rep. Med. 2, 100203 (2021).
Murakami, H. et al. Accuracy of wearable devices for estimating total energy expenditure: comparison with metabolic chamber and doubly labeled water method. JAMA Int. Med. 176, 702â703 (2016).
Ohno, H., Sasaki, K., Ohara, G. & Nakazono, K. O. U. Development of grid square air temperature and precipitation data compiled from observed, forecasted, and climatic normal data. Clim. Biosphere 16, 71â79 (2016).
Fuse, S. et al. Brown adipose tissue density measured by near-infrared time-resolved spectroscopy in Japanese, across a wide age range. J. Biomed. Opt. 23, 1â9 (2018).
This work was supported by the Japan Agency for Medical Research and Development (JP20gm1310007 to J.S., Y.M. and T.Y.), the Japan Science and Technology Agency (JPMJFR2014 to T.Y. and JPMJPF2013 to H.N.), the JSPS KAKENHI (JP21K08548 and JP20K22647 to T.Y.; JP16H06390, JP20H04835, JP20K21747, JP21H04826, JP22K18411 and JP24H00065 to J.S.; and JP22590227 and JP18K11013 to M.S.), the SECOM Science and Technology Foundation to J.S. and the Naito Foundation to T.Y. We thank Y. Yamamoto and T. Yamaoka for technical assistance in meteorological survey and acquiring data and M. Yoshio and Y. Ono for secretary assistance.
Authors and Affiliations
T.Y., M.M., T.K., T. Harada and M.S. carried out FDG-PET/CT. S.F.H., M.K. and Y.K. performed NIR-TRS. T.Y. and S.F.H. performed the meteorological survey and statistical analysis with technical assistance from M.A., Y.W., M.I., K.K., Y.M., T.O. and H.N. Y.Y. and Y.A. performed the DLW experiment. T.Y., T. Hamaoka, J.S. and M.S. conceived the research and wrote the paper. All authors approved the final version of the paper.
Peer review information
Nature Metabolism thanks Raffaele Teperino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Revati Dewal, Jean Nakhle in collaboration with the Nature Metabolism team.
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Extended data
(a) Participant profiles in the high (n = 259) and low (n = 97) BAT groups of Cohort 1. BAT activity was evaluated as SUV of FDG assessed by the FDG-PET/CT examination combined with acute cold exposure. Number of males/females in parentheses. (b) Climatological annual cycle of outdoor temperature in northern (Sapporo, latitude 43ËN), eastern (Tokyo, 36ËN), and western areas (Kagoshima, 31ËN) of Japan. Daily mean outdoor temperature for 11 years (2010â2020) was obtained from the JMA database and 11-day moving average was calculated for each calendar day. For the sake of simplicity, the year was divided into the cold season (January 1st â April 15th, and October 17th â December 31st) and warm season (April 16th â October 16th). (c) Participant profiles of the warm (n = 188) and cold (n = 168) birth groups and the warm (n = 159) and cold (n = 197) fertilization groups in Cohort 1. (d) A plot of the place of birth/fertilization of the participants of Cohort 1 who completed the birthplace survey (n = 237). The pie chart represents distribution of the participants to the eight major regions of Japan. (a, c, d) Biologically independent samples. Data are mean ± s.e.m.; two-tailed P values by unpaired Studentâs t-test.
(a) Fluctuation of the prevalence of cold-activated BAT by month of birth. Numbers of participants with high and low BAT are indicated on the graph. (b) Fluctuation of the prevalence of cold-activated BAT by month of fertilization. Number of participants with high and low BAT are indicated on the graph. (c) Logistic regression analysis of BAT activity in Cohort 1. (d) Percentage of detection of cold-induced BAT in various regions in high BAT subjects (n = 259). (e) Percentage of the number of depots with active BAT in high BAT subjects (n = 259). (f) Effect of birth season on the number of active BAT depots in Cohort 1 (n = 356). (g) Effects of fertilization season on the number of active BAT depots in Cohort 1 (n = 356). (a-g) Biologically independent participants. (a, b, d-g) Data are percentage, and one-tailed P values by Fisherâs exact test (f, g). (c) Multivariate logistic regression analysis: adjusted odds ratios (ORs) with 95% confidence intervals (CIs) as error bars and two-tailed P values. * P < 0.05, ** P < 0.01. High BAT coded as 1 and low BAT coded as 0, as the dependent variable. Age: †22 years old, 23 years old, and â„ 24 years old were coded as 1, 2, and 3, respectively. BMI: †20.3 kg/m2, 20.4 to 22.0 kg/m2, and > 22.0 kg/m2 were coded as 1, 2, and 3, respectively. The warm season and cold season of the birth and fertilization are coded as 0 and 1, respectively.
Extended Data Fig. 3 Fertilization in cold season does not correlate with [total Hb] concentration in the abdominal WAT and deltoid skeletal muscle in male and female adults.
(a) Participant profiles of Cohort 2. BAT-d was evaluated using NIR-TRS. Participants were divided into two groups: high and low BAT-d groups. All participants: n = 286 except visceral fat area (n = 264), systolic (SBP, n = 282), and diastolic blood pressure (DBP, n = 282). High BAT-d: n = 143 except visceral fat area (n = 124), SBP (n = 140), and DBP (n = 140). Low BAT: n = 143 except visceral fat area (n = 140), SBP (n = 142), and DBP (n = 142). Number of males/females shown in parentheses. (b) Participant profiles of the warm (n = 144) and cold (n = 142) birth groups and the warm (n = 153) and cold (n = 133) fertilization groups in Cohort 2. (c) Fluctuation of the percentage of participants with high BAT-d by month of birth. Number of participants with high and low BAT-d are indicated on the graph. (d) Fluctuation of the percentage of participants with high BAT-d by month of fertilization. Number of participants with high and low BAT-d are indicated on the graph. (e) Total haemoglobin concentration, [total Hb], in the abdominal subcutaneous white adipose tissue: Comparisons between the warm (n = 136) and cold (n = 138) birth groups and between the warm (n = 147) and cold (n = 127) fertilization groups. (f) The [total Hb] in the deltoid skeletal muscle: Comparisons between the warm birth (n = 79) and cold birth (n = 77) groups and between the warm fertilization (n = 86) and cold fertilization (n = 70) groups. (g) A disaggregated analysis of BAT-d at the supraclavicular region for sex. Left: male, n = 108. Right: female, n = 178. (a-g) Biologically independent participants. Data are mean ± s.e.m.; two-tailed P values (a, b, e, f) or one-tailed P values (g) by unpaired Studentâs t-test. Percentage of sex in (a, b); one-tailed P values by Fisherâs exact test.
(a) Schematic illustration of the crossover study design to measure CIT in summer (Jul., Aug., Sep.) and winter (Dec., Jan., Feb., Mar.) in Cohort 3 (n = 42). (b) Correlations between fat-free mass (FFM) and whole-body energy expenditure (EE) at thermoneutral 27°C and after cold exposure at 19°C for 2 hr in a. Left, EE measured in summer. Right, EE measured in winter. (c) Participant profile of the warm birth and cold birth groups and the warm fertilization and cold fertilization groups of Cohort 3. (d) BAT activity as the SUV of FDG for subjects in c. Left, the warm birth group (n = 23) and cold birth group (n = 19). Right, the warm fertilization group (n = 14) and cold fertilization group (n = 28). (e) Whole-body EE adjusted for FFM of the warm and cold birth groups at thermoneutral condition (27°C) and after 2-hr cold exposure (19°C) measured in summer. Warm birth group (n = 23); cold birth group (n = 19). (f) CIT of the warm and cold birth groups measured in summer in e. (g) Whole-body EE adjusted for FFM of the warm and cold fertilization groups at thermoneutral condition (27°C) and after 2-hr cold exposure (19°C) measured in summer. Warm fertilization group (n = 14); cold fertilization group (n = 28). (h) CIT of the warm and cold fertilization groups measured in summer in g. (i) Correlations of BAT activity with CIT measured in summer (left) and in winter (right). (j) Correlations of FFM with CIT measured in summer (left) and in winter (right). (b-j) Biologically independent samples. (c-h) Data are mean ± s.e.m.; two-tailed P values by unpaired Studentâs t-test. (b, i, j) Pearsonâs correlation coefficient (r) and two-tailed P values.
(a) Participant profiles of the warm birth and cold birth groups and the warm fertilization and cold fertilization groups in Cohort 4 (n = 23). (b) BAT activity as SUV of FDG for subjects who participated in DIT measurement (Cohort 4). Left, the warm birth group (n = 10) and cold birth group (n = 13). Right, the warm fertilization group (n = 6) and cold fertilization group (n = 17). (c) Nutrient composition of the test meal. Participants ingested the nutritionally balanced food and liquid containing 500 kcal/63 kg body weight (BW). (d) Correlation between FFM and resting EE at thermoneutral 27°C. (e) Postprandial whole-body EE adjusted for FFM in the warm birth (n = 10) and cold birth (n = 13) groups. (f) Postprandial whole-body EE adjusted for FFM in the warm fertilization (n = 6) and cold fertilization (n = 17) groups. (a, b, d-f) Biologically independent samples. (a, b, e, f) Data are mean ± s.e.m.; two-tailed P values by unpaired Studentâs t-test (a, b) or two-way repeated measures ANOVA (e, f). (d) Pearsonâs correlation coefficient (r) and two-tailed P value.
(a) Participant profile, physical activity, and TEE of the warm birth (n = 22) and cold birth groups (n = 19) and the warm fertilization (n = 20) and cold fertilization groups (n = 21) in Cohort 5. (b) Correlations of daily TEE measured by the DLW method with age and anthropometric parameters. (c) Multivariate regression analysis for predicting daily TEE (n = 41, model 1, R2 = 0.794, P < 0.001). The TEE as dependent variable. Age, height, weight, FFM, fat mass, step count, physical activity level as independent variables. (d) Normalized daily TEE. Residual EE in multivariate regression model 1 in c was calculated for each subject as body size- and physical activity-independent TEE. Warm birth (n = 22); cold birth (n = 19); warm fertilization (n = 20); cold fertilization (n = 21). (e) A disaggregated analysis of TEE adjusted for FFM and step count for sex. Left: male, n = 19. Right: female, n = 22. (a-e) Biologically independent samples. (a, d, e) Data are mean ± s.e.m.; two- (a, d) or one-tailed (e) P values by unpaired Studentâs t-test. Percentage of sex: one-tailed P values by Fisherâs exact test. (b) Pearsonâs correlation coefficient (r) and two-tailed P values. (c) Unstandardized ÎČ with 95% CIs as error bars, standardized ÎČ, and two-tailed P values by multivariate regression analysis with backward stepwise method (model 1).
(a) Correlations between BAT activity by FDG-PET/CT and adiposity-related parameters including BMI, body fat content, body fat mass, FFM, abdominal total, subcutaneous, and visceral fat areas, and waist circumference in healthy male participants (Cohort 1). (b) Correlations between BAT-d by NIR-TRS and adiposity-related parameters including BMI, body fat content, skeletal muscle mass, visceral fat area, waist circumference, SBP, DBP, and heart rate in participants with wide range of age (Cohort 2). (a, b) Biologically independent samples. Pearsonâs correlation coefficient (r) and two-tailed P value by correlation analysis. Numbers of participants (n) are indicated on the panels.
(a) Impacts of the seasons of birth and fertilization on abdominal total, subcutaneous, and visceral fat areas and waist circumference in Cohort 1. (b) Impacts of the seasons of birth and fertilization on skeletal muscle mass in Cohort 2. (c) Impacts of the seasons of birth and fertilization on SBP, DBP and heart rate in Cohort 2. (a-c) Biologically independent samples. Data are mean ± s.e.m.; two-tailed P value by unpaired Studentâs t-test. Numbers of participants (n) are indicated on the graph.
Climatological annual cycle of meteorological parameters in Tokyo, Japan. Daily maximum (max.) and minimum (min.) outdoor temperatures (temp.), precipitation, humidity, sunshine duration, atmospheric pressure, and diurnal range of outdoor temperature for 11 years (2010â2020) were obtained from the climate databases constructed by the JMA and NARO. Sunrise time and sunset time for 7 years (2012â2020) were obtained from the database constructed by the NAOJ to calculate daytime length. Diurnal temperature fluctuation was calculated as differences between maximum and minimum temperature and between maximum and mean temperature. For all meteorological parameters, 11-day moving average was calculated for each calendar day. Data are mean ± s.e.m., n = 11 except daytime length (n = 9).
(a) Multivariate logistic regression analysis for predicting independent effect of diurnal temperature gap, calculated as difference between maximum and mean outdoor temperature, on BAT activity (n = 93, model 3). The calculated diurnal temperature gap was added in the model in addition to daily mean temperature and the other meteorological parameters. Age, BMI, the medical history of low birth weight, and lifestyle factors such as smoking and shift work status were included in the model as potential confounding factors. (b) Association between BAT prevalence and diurnal temperature gap in a. The participants were divided into three groups according to the tertile of diurnal temperature gap in the five pregnancy periods. The number of participants of high and low BAT subjects were indicated on the graph. (a, b) Biologically independent participants. (a) Data are presented as adjusted ORs with 95% CIs as error bars and two-tailed P values by multivariate logistic regression analysis with backward stepwise method. High and low BAT coded as 1 and 0, respectively, as the dependent variable. The models were adjusted for the tertile values for age and BMI, the medical history of low birth weight, smoking status, shift work status. (b) Data are percentage with two-tailed P values by Chi-squared linear-by-linear association test.
Source data
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