Geographic and seasonal patterns and limits on the adaptive response to temperature of European Mytilus spp. and Macoma balthica populations

Seasonal variations in seawater temperature require extensive metabolic acclimatization in cold-blooded organisms inhabiting the coastal waters of Europe. Given the energetic costs of acclimatization, differences in adaptive capacity to climatic conditions are to be expected among distinct populations of species that are distributed over a wide geographic range. We studied seasonal variations in the metabolic adjustments of two very common bivalve taxa at European scale. To this end we sampled 16 populations of Mytilus spp. and 10 Macoma balthica populations distributed from 39° to 69°N. The results from this large-scale comprehensive comparison demonstrated seasonal cycles in metabolic rates which were maximized during winter and springtime, and often reduced in the summer and autumn. Studying the sensitivity of metabolic rates to thermal variations, we found that a broad range of Q 10 values occurred under relatively cold conditions. As habitat temperatures increased the range of Q 10 narrowed, reaching a bottleneck in southern marginal populations during summer. For Mytilus spp., genetic-group-specific clines and limits on Q 10 values were observed at temperatures corresponding to the maximum climatic conditions these geographic populations presently experience. Such specific limitations indicate differential thermal adaptation among these divergent groups. They may explain currently observed migrations in mussel distributions and invasions. Our results provide a practical framework for the thermal ecophysiology of bivalves, the assessment of environmental changes due to climate change and its impact on (and consequences for) aquaculture.
Introduction
A fundamental step forward in predicting the ecological and economic consequences of climate change would be to identify the mechanistic link between the physiology of species and climatic variations. How and to what extent climatic variations cause stress in eurythermal bivalve species is still not fully understood and is expected to differ among taxa. In general, for organisms that maintain their body temperature by absorbing heat from the environment (ectotherms), temperatures that are too high can act as a stressor in two ways. On the one hand, it may cause the denaturing of sensitive proteins. This damage can be minimized by the actions of heat-shock proteins, which increase the thermostability of proteins and chaperone cellular processes (Feder and Hofmann 1999; Lyons et al. 2003). On the other hand, excessive temperatures may cause oxygen limitation, due to a limited respiratory capacity, resulting in a maximum respiration rate at a specific temperature, beyond which anaerobic metabolic pathways are utilized and respiration rates usually drop drastically (Pörtner 2001, 2002). The temperature that corresponds to this respiratory maximum is referred to as the breakpoint temperature. Since breakpoint temperatures tend to correlate with the maximum habitat temperatures of several marine ectotherms (Somero 2002), climate-change-induced shifts in the distributions of these species may be due to their respiratory limitations.
The metabolic rate of an ectotherm is proportional to its respiration rate. To remain energy-efficient, and for protection against oxygen shortage during the warmer seasons, organisms need to adjust their metabolic energy requirements to their maximum food uptake and oxygen consumption rate. Seasonal variation in the respiratory response to temperature reveals how organisms adjust throughout the annual cycle. While several terrestrial and aquatic mollusks apply the strategy of metabolic down-regulation during the summer (Buchanan et al. 1988; McMahon 1973; McMahon and Wilson 1981; Storey and Storey 1990; Wilson and Davis 1984), others do not (McMahon et al. 1995). The level of metabolic down-regulation is also reflected by the temperature quotient (Q
10) of the metabolic rate, i.e., the sensitivity of the organisms’ metabolism to changes in body temperature. Experimental work performed by Widdows (1976) has demonstrated that when thermal fluctuations approach and exceed breakpoint temperatures, this sensitivity usually decreases. Such reduced sensitivities have been found for field populations of M. edulis (Newell 1969) and M. balthica (Wilson and Elkaim 1991) sampled from high-shore habitats in summertime. Taken together, the thermal sensitivity of the metabolic rate (Q
10) is expected to decrease towards the warm end of the species distribution range.
Lately, invasions (Geller 1999; Wonham 2004) and northward introgression (Luttikhuizen et al. 2002) have been reported for Mytilus spp., and a range contraction for M. balthica (Hummel et al. 2000). Aiming to bridge the gap between the observed migrations of these species and the changing climate in Europe, we studied seasonal adjustments to temperature in bivalve metabolism at the European scale, from 39° to 69°N. The analysis of patterns in the extent of metabolic acclimatization across widely distributed populations will reveal how core populations differ from marginal populations, presenting species-specific responses to cold winters and hot summers near the upper and lower edges of temperature-induced distribution ranges. Such latitudinal gradients may provide a powerful tool that can be used to understand the temperature-dependent distributions of species and to predict their adaptive tolerance to climate change. In macrophysiological studies, the possibility of differential adaptation to regional climates among distinct populations should be taken into account. Based on neutral genetic variation, both European mussels and clams can be subdivided into three main genetic groups (Hummel 2006; Daguin et al. 2001; Luttikhuizen et al. 2003; Skibinski 1985). Uncertainty exists about the nomenclature of European Mytilus species. Traditionally, mussels from the Mediterranean Sea and the coast of the Iberian Peninsula are referred to as Mytilus
galloprovincialis (Lamarck). Mussels from the English Channel, the North Sea coast, and the Atlantic coast of Norway and Iceland are named Mytilus edulis (L.), and the mussels from the Central Baltic Sea are called Mytilus trossulus (Gould), which is based upon their genetic resemblance to Mytilus trossulus from the Atlantic coast of Canada (Varvio et al. 1988). However, the morphological characteristics of the different holotypes do not diagnostically separate the three genetic groups found in European mussels. In addition, broad hybridization zones (Daguin et al. 2001) and deep introgressions (Luttikhuizen et al. 2003) have been reported; a genetic characteristic of a single species with separated clades. To avoid confusion, we will not use these species names in this study, but rather refer to them as Mytilus spp., with reference to their geographic distributions, i.e., a Baltic Sea group, a North Sea group, and a Mediterranean Sea and Bay of Biscay group. Also, different genetic groups have been distinguished for M. balthica (Hummel 2006; Luttikhuizen et al. 2003) that have never been described as different species. The coupling of phylogenetic and ecophysiological analyses is urgently needed to understand and predict current and future migrations of these bivalve taxa and their clades.
Methods
Fieldwork
To study the marginal and core populations of both taxa, including different genetic groups, we defined 21 research sites of interest along the European coastline, and sampled 11 M. balthica populations and 16 Mytilus spp. populations, respectively. All sampling stations are numbered (1–21) in Fig. 1. Whenever a station name is mentioned in this text, its number is given between parentheses. The sampling stations were located in the coastal areas of the different sea basins that represent much of the European coastline (including the Mediterranean Sea, the Bay of Biscay, the North Sea and the Baltic Sea), and a variety of microhabitats. The sampling stations in the Mediterranean Sea are characterized by a high and stable salinity (38–40 PSU). In the Bay of Biscay and North Sea estuaries, the ambient salinity at the sampling stations fluctuated, generally varying between ∼35 and ∼25 PSU. Due to elevated river runoff, oligohaline conditions may have occurred occasionally at these stations. The Baltic Sea stations were distributed along the Baltic salinity gradient. While the ambient salinity is still ∼15 PSU in the Mecklenburg Bight (7), it is around 6–7 in the Gulf of Gdansk (6) and Askö (5), and has decreased to 3 PSU in Umeå (3). Although high and low peak temperatures occur at the intertidal sampling stations in the Bay of Biscay and the North Sea, water temperatures were intermediate compared to the warmer Mediterranean Sea and the colder Baltic Sea sites (Fig. 2). In summer, the Baltic Sea warms rapidly, reaching temperatures that are comparable to North Sea conditions. In the tidal estuaries of the Bay of Biscay and the North Sea, sampling was carried out at mid-shore level. Lacking significant tidal movements, Baltic Sea populations of Mytilus spp. and M. balthica, and Mediterranean Sea populations of Mytilus spp. were sampled at a water depth of 0.5–1.0 m. During the period July 2003–May 2005 16 populations (i.e., 10 Mytilus spp. and 6 M. balthica) were visited seasonally and the others only once or twice. During each sampling occasion, mussels were sampled from hard substrate, and clams were sieved from the sediment. For Mytilus spp. populations, mean shell lengths ranged from 28 mm (SD: 2.0) for specimens sampled in the Gulf of Gdansk (6) to 33 mm (SD: 3.5) for mussels sampled from the Santa Giusta Lagoon (21). Mean shell lengths of mussels collected from the other populations was in the same range, and standard deviations were <3.5. About 95% of all sampled mussels fell within a size range of 25–35 mm. For M. balthica mean shell lengths ranged from 12 mm (SD: 1.5) for clams sampled from the Mecklenburg Bight (7) to 16 mm (SD: 1.8) for clams sampled at Point d’Aiguillon (15). Mean shell lengths of all other clam populations were within this range, standard deviations did not exceed 2.8 mm, and 95% of all sampled clams fell within the size range of 10–19 mm. After sampling, the collected animals were stored in foam boxes (clams were offered sediment from the field to bury) and transported to a nearby laboratory, where they were kept in (constantly aerated) aquaria, under ambient field conditions (±3 °C). Measurements were carried out within 24 h after sampling.
Fig. 1Map of research area with 21 research sites. The coastal area is subdivided into three parts representing the approximate distributions of the three genetic groups found in Mytilus spp. and M. balthica. Light gray indicates the Baltic Sea group, medium gray the North Sea group and dark gray the Mediterranean Sea and Bay of Biscay group (hybridization and introgression zones are not indicated). The white and the gray circles represent mussel and clam populations, respectively (see legend). Site no./name: 1, Rykyavik; 2/3, Umeå a/b; 4, Fallvikshamn; 5, Askö; 6, Gulf of Gdansk; 7, Lomma; 8, Mecklenburg Bight; 9, Grevelingenmeer; 10, Westerschelde estuary; 11, Granville; 12, Le Vevier; 13, Brest; 14, Loire estuary; 15, Point d’Aiguillon; 16, Bidasoa estuary; 17, Mundaka estuary; 18, Vias plage; 19, Marseille; 20, Gulfo di Oristano; 21, Santa Giusta lagoon
Fig. 2Annual sea surface temperature (SST) regimes for the sampling stations in the Baltic Sea, the North Sea, the Bay of Biscay, and the Mediterranean Sea. Lines and error
bars represent monthly averages and standard deviations, based on measurements taken by satellite twice daily, at 9 a.m. and 2 p.m., during the period 2003–2005. This data were taken from the NASA JPL website (NASA Jet Propulsion Laboratory 2005)
Temperature profiles
Field temperature profiles were obtained between April 2004 and May 2005 with temperature loggers (HOBO Water Pro®, Onset Computers, Bourne, MA, USA) at research sites 5, 7, 9, 10, 15, 16 and 18, with a resolution of one measurement per 30 min. The loggers were positioned in the direct vicinity of the animals. Logger output was compared with the sea surface temperature (SST) profiles obtained by satellite (NASA Jet Propulsion Laboratory 2005). Mean habitat temperature of the shallow water and intertidal habitats showed a constant relation with SST. In summer SST was about two degrees lower than the mean values calculated from the logger data at all sites, while in winter the SST was slightly lower for the Atlantic and Mediterranean sites. For the Baltic Sea sites, winter SSTs were similar to the logger data. Using the relation between SST and logger data, we estimated the acclimatization temperature of each mussel and clam population for each sampling occasion. This acclimatization temperature is defined in this study as the mean water temperature for the period of 30 days before sampling. We assume that this is a proper indication of the temperature to which the animals should be well adjusted.
Respiration rates
Within 24 h after sampling, groups of 3–6 mussels and 7–15 clams were gently removed from their aquaria, and without further acclimation transferred to respiration chambers of volume 264 and 154 ml, respectively. The chambers were positioned in a thermostated tank to maintain a constant temperature during the incubations (±0.3 °C). The chambers were filled with filtered habitat water, previously aerated to 100% oxygen saturation. Chamber lids contained Clark-type electrodes to record the change in oxygen tension in the water. In this way, 2–6 replicate measurements were taken per population and temperature after each sampling occasion. Control measurements were carried out using the same experimental setup, without animals. The total number of M. balthica specimens used from the populations that were sampled seasonally ranged from 475 from the Westerschelde estuary (10) to 685 from the Mecklenburg Bight (8). For Mytilus spp. the number of experimental animals ranged from 104 from Point d’Aiguillon (15) to 322 from Askö (5). To avoid light-induced stress in M. balthica, the chambers were made out of tanned Plexiglas. Respiration rates were measured at 3, 10, 17, 24 and 31 °C. Measurements continued until the oxygen tension in the chambers had decreased by 20–30%. After each measurement, experimental animals were frozen at −20 °C and subsequently lyophilized for 72 h to a stable weight. From the dried specimens, valves were removed and the soft tissue dry-weights determined to the nearest mg, after which the mass-specific respiration rates and the temperature quotients were calculated. In addition, we estimated the respiration rates and Q
10 values at the acclimatization temperature, per population, per season. Both, respiration rates and Q
10 values were based on the rates that corresponded to the seven-degree temperature interval (for instance 10–17 °C) nearest to the acclimatization temperature. The respiration rates were assessed by linear interpolation, and the Q
10 values were calculated with the following equation:
. Here k
2 and k
1 are the respiration rates measured at the higher and the lower temperatures, t
2 and t
1, respectively.
Statistical analysis
The following working hypothesis was formulated: H0: “Season” or “genetic group” have no effect on the metabolic temperature dependence of M. balthica or Mytilus spp. Because temperature is not the only the factor limiting ectotherm metabolism, commonly used statistical methods such as correlations and regressions are not very well suited for testing this relationship (Blackburn et al. 1992; Cade et al. 1999; Thompson 1996). Estimating the function along the upper edge of this distribution would describe the evolutionary relation between temperature and metabolism in our species. Data points scattered below this “slope of upper bounds” (Blackburn et al. 1992) are responses induced by other limiting factors. To estimate this slope of maximum respiration rate at a given temperature, we carried out a main axis regression analysis (after Thompson et al. 1996). The first step in this is a general regression through all data points. Subsequently, the data are divided into points that fall below and above the line of least squares. All data points that were found above are then used to fit a second regression that again divides the data into two subsets; etc. This was repeated three times. The regression lines were forced through the x, y point (−2, 0), assuming that at the approximate freezing point of seawater bivalve aerobic metabolism is near to zero. The final regression line for M. balthica populations was based on 12 data points, with an r
2 of 0.95. For Mytilus spp. populations, the final regression was based on seven data points with an r
2 of 0.99. The distance between measured respiration rates and this upper slope describes the extent of metabolic down-regulation in the populations. These rate-deviations (distances) were estimated for all populations and used for statistical comparison. ANOVAs based on rate deviations were carried out with “season” or “genetic group” as independent variables. For the “genetic group” analysis, only data points from the temperature range at which all three groups were sampled were included, i.e., the temperature range for the “genetic group” comparison for M. balthica was 9–18 °C, and for Mytilus ssp. 9–15 °C. Bonferroni’s multiple comparison test was used to test for specific differences among seasons or genetic groups.
Results
Respiration rates at experimental temperatures
For each population sampled seasonally, mean respiration rates at experimental temperatures are given in Fig. 3 for Mytilus spp. and in Fig. 4 for M. balthica. Standard deviations among replicate measurements were usually less than 20% of mean values at experimental temperatures near ambient conditions. At high experimental temperature (31 °C), variation among replicate measurements could be higher, with standard deviations occasionally exceeding 50% of the mean values. This was caused by the fact that some groups of specimens exhibited high respiration rates, while other groups consumed almost no oxygen.
Fig. 3Seasonal variation in the respiratory response to an experimental temperature range for ten European Mytilus spp. populations. Station names and degrees north are given in the graphs. Numbers in square boxes refer to the station names presented in Fig. 1. See legend for explanation of the symbols
Fig. 4Seasonal variation in the respiratory response to an experimental temperature range for six European M. balthica populations. Station names and degrees north are given in the graphs. Numbers in square boxes refer to the station names presented in Fig. 1. See legend for explanation of the symbols
The respiration rate of mussels usually declined between 24 and 31 °C (Fig. 3). Exceptions were some cold-acclimatized populations that showed maximum rates between 17 and 24 °C [e.g., the Askö (5), Gulf of Gdansk (6), and Grevelingenmeer (9) populations sampled in January]. Peaks in respiration rates between 1.5 and 2.5 (mg O2/g (dry weight)/h) at 24 °C were frequently observed in springtime (April–May) and occasionally in other seasons. Respiration rates never exceeded 1.5 (mg O2/g (dry weight)/h) in mussels from the Mecklenburg Bight (6) and the Gulf of Gdansk (5).
The highest respiration rates for the M. balthica specimens were mainly observed at 31 °C; the maximum temperature applied in this study (Fig. 4). Especially in the populations from the Westerschelde estuary (10) and Point d’Aiguillon (15), respiration rates never decreased at high experimental temperatures. However, in the Baltic populations, outside the summer period maximum rates were observed at a lower experimental temperature. In Umeå (3), respiration rates measured in January and April declined at experimental temperatures exceeding 10–17 °C.
Respiration rates at acclimatization temperatures
The respiration rates of both taxa increased with acclimatization temperature (Fig. 5). In winter and springtime, deviations from the upper edge slope were small for both Mytilus spp. and M. balthica. At acclimatization temperatures of >12 °C, an increasing number of data points were scattered at increasing distance from the upper edge slope, and the seasonal comparisons (ANOVA + Bonferroni) revealed that rate deviations were mainly restricted to measurements taken in summer and autumn (see column bar plots, superimposed in Fig. 5A,B). These data also revealed genetic group specific differences between the respiration rate and the acclimatization temperature for Mytilus spp. (P < 0.05); (Fig. 5C). Maximum acclimatized respiration rates occurred between 8 and 14 °C in the Baltic Sea group, 15–19 °C in the North Sea group and 20–24 °C in the Mediterranean Sea and Bay of Biscay group. The rate deviations found for the Mediterranean Sea and Bay of Biscay group differed significantly from those found for the Baltic Sea group. Several data points related to the North Sea group were found to fit the reaction norm of the Mediterranean Sea and Bay of Biscay group between 15 and 20 °C. Thus, the rate deviations of these two groups could not be distinguished (P > 0.05). No genetic group specific differentiation was observed for M. balthica (Fig. 5D).
Fig. 5A–DRespiration rates as a function of the acclimatization temperature. The black lines are the upper edge slopes. These graphs include measurements from all research sites presented in Fig. 1. In A and B, different labels represent different sampling seasons (see legend in A). In B and C respiratory response to temperature is presented per genetic group. The gray diamonds, white triangles and black circles represent the Baltic Sea group, the North Sea group and the Mediterranean Sea and Bay of Biscay groups, respectively. Superimposed are the rate deviations calculated per season and per genetic group, in which BS indicates the Baltic Sea group, NS the North Sea group, MS the Mediterranean Sea group and BC the Bay of Biscay group. *P < 0.05 and **P < 0.01 refer to the statistical differences for a given season compared to “January” in A and B and for a given group compared to “BS” in C and D
Thermal sensitivity of the metabolic rate
Seasonal variation in thermal sensitivity of the metabolic rate shows that Q
10 values of greater than four were exclusively found in winter and spring (Fig. 6). For M. balthica populations, such high Q
10 values were found at low (January) and high (April) latitudes. In April, Q
10 values decreased significantly with latitude. Maximum Q
10 did not exceed 3.0 in July and October. July values were about one for clams from the Westerschelde estuary (10) and Point d’Aiguillon (15). The clam population at Askö (5) also exhibited relatively low values (<2). Q
10 values of greater than four were only observed in January and April for mussels (Fig. 6). A significant decrease in Q
10 values with latitude was observed in January (P = 0.0083), with the lowest values observed (Q
10 = 1.2) for the population from Gulfo di Oristano (20), and the highest values for the population from the Gulf of Gdansk (6). In April, this trend vanished, mainly due to the reduced Q
10 values of mussels from the northernmost sampling stations. During the following seasons, July and October, mean Q
10 values decreased further and latitudinal variation disappeared.
Fig. 6Seasonal and latitudinal variations in mean Q
10 values of M. balthica (circles) and Mytilus spp. (diamonds) populations. Trend lines represent linear regressions. r
2 and P values are given in each graph
As a function of the acclimatization temperature, both low and high Q
10 values were found at low ambient temperatures (Fig. 7A,B). The maximum Q
10 values observed at a given temperature decreased with increasing acclimatization temperature for both taxa. To illustrate this, we fitted lines of maximum Q
10 values as a function of temperature. These lines crossed the Q
10 = 1 line at ∼23 °C for M. balthica and at ∼24 °C for Mytilus spp. (Fig. 7A,B, line a). In addition to the maximum Q
10 values for all Mytilus spp. populations, group-specific lines could be drawn (lines a–c in Fig. 7A). This resulted in different intercepts for the Q10 = 1 line: at ∼17 °C for the Baltic Sea group, at ∼19 °C for the North Sea group and at ∼24 °C for the Bay of Biscay and Mediterranean Sea group. No differentiation of the maximum thermal sensitivity of the metabolic rate was observed for M. balthica populations (Fig. 7B).
Fig. 7A–B Q10 values as a function of the acclimatization temperature, presented per genetic group. The diamonds, triangles and circles represent the Baltic Sea group, the North Sea group and the Mediterranean Sea and/or Bay of Biscay group, respectively. These graphs include measurements taken from all research sites. Lines of maximum Q10 values are fitted for M. balthica (B; a) and for Mytilus spp. (A) per genetic group: a the Mediterranean Sea and Bay of Biscay group, b the North Sea group, and c the Baltic Sea group
Discussion
Rates at high experimental temperatures
When exposed to experimental temperatures of 24 or 31 °C, respiration rates in both Mytilus spp. and M. balthica may exceed 2.0 (mg O2/g (dry weight)/h). Such high rates were mostly observed in winter and spring, probably due to the low activation energy required for enzyme-catalyzed reactions at this time of the year (Hochachka and Somero 2002). The population from the Mecklenburg Bight (7) exhibited exceptionally high respiration rates for Baltic Sea mussels in the summer. In general, these high respiration rates measured at high experimental temperatures are not expected to occur in the field. When the summer starts, and peaks in habitat temperatures (24 °C+) occur in the field, the amplitude of the respiratory response at these higher temperatures declined. We propose that this modulation of metabolic thermal sensitivity is a protection mechanism that prevents excessive metabolic rates at high ambient temperatures. At the northern Baltic Sea station (3), the high thermal sensitivity of the metabolic rate did not result in high respiration rates in wintertime (which includes April in this area). Rates declined above 10 °C, indicating that the respiratory capacity was low and that acclimatization to increasing temperatures in springtime requires physiological changes in these clams.
Comparing both taxa, their respiratory responses to the experimental temperature range were rather different. The most striking difference is the regular occurrence of the breakpoint temperature between 24 and 31 °C in Mytilus spp., which was mostly absent for M. balthica (Fig. 8A,B; in these graphs we summarize all respiration rates assessed for all populations during all seasons between 2003 and 2005). This breakpoint temperature may reflect the thermal tolerance limits of marine ectotherms. It has been suggested that species with lower breakpoint temperatures are less tolerant to high temperatures (Hochachka and Somero 2002; Pörtner 2002). Interestingly, despite its apparent breakpoint temperature between 24 and 31 °C, Mytilus spp. seem to tolerate higher environmental temperatures than M. balthica, given the geographical distribution of mussels, which reaches as far south as northern Africa (Comesana et al. 1998). Nonetheless, the lethal temperatures of both taxa under submerged conditions are comparable. The LT50 values for both species were 30–31 °C in a 24-h experiment using mussels and clams that were previously acclimatized to 20–25 °C (Kennedy and Mihursky 1971; Wallis 1975).
Fig. 8A–B Mean respiratory responses to temperature of Mytilus spp. (B; diamonds) and M. balthica (A; circles). Data are presented as the averages of all measurements taken (all populations and all seasons, 2003–2005). The numbers next to the labels are the number of replicate measurements taken, and the dashed lines indicate the standard deviations
Rates at acclimatization temperatures
The relations between the mean acclimatized respiration rates of the studied taxa and temperature are based on specimens from a great variety of microhabitats. Therefore, we expect these data to give a representative overview of metabolic temperature dependence in acclimatized mussels and clams. A narrow relation between temperature and acclimatized respiration rates in winter and springtime suggests that variations in the metabolic rate during this time of the year were directly dependent on temperature, and not limited by other physiological or environmental variables. A temperature-limited metabolic rate was also reflected by the high thermal sensitivity of the metabolic rate of some of the populations during those seasons. Only a few observations confirmed that the constant increase in respiration rates with acclimatization temperature can continue in summer and autumn, when most data points were found to be scattered below the upper edge slope. We suggest that the upper edge slopes represent the “metabolic scopes” of these taxa. However, since measurements are based on groups of specimens, respiration rates of single individuals can be higher.
The relatively high acclimatized respiration rates (1.0–1.5 mgO2/g/h) most probably result from metabolic up-regulation, related to elevated rates of digestion and protein synthesis. The present study did not find any acclimatized routine respiration rates that exceeded 1.5 (mg O2/g/h) in mussels or clams. In general, respiration rates exceeding 1.5 mg O2/g(dry weight)/h can be considered to be high and will rarely occur under ambient conditions. Also, in other studies no acclimatized routine respiration rates higher than 1.5 mg O2/g(dry weight)/h have been reported for M. balthica (McMahon and Wilson 1981; Wilson and Elkaim 1991; Hummel et al. 2000) or Mytilus spp. (Arifin and Bendel-Young 2001; Bayne and Widdows 1978; Tedengren et al. 1999; Thompson 1984). Towards tolerance limits, the arising breakpoint temperature will force down the “metabolic scope,” as indicated by the mussel populations from the Santa Giusta lagoon (21) and Gulfo di Oristano (20) in July, and for clams from Point d’Aiguillon (15) in July and October. Physiological rates that are acclimatized to (near) breakpoint temperatures are rarely described for ectotherms.
The relatively low respiration rates at acclimatization temperatures >12 °C are interpreted as metabolic down-regulation resulting from both intrinsic and extrinsic factors (Brokordt et al. 2000; Burkey 1971; Velasco and Navarro 2003). The extremely low respiration rate of mussels from the Bidasoa estuary (16) in January was an exception, presumably caused by a temporary drop in ambient salinity (from 34 to 9 PSU). Such hypo-osmotic conditions are known to induce a metabolic depression in Mytilus spp. (Newell 1969; Storey and Storey 1990).
In general, Baltic Sea populations exhibited relatively low respiration rates, intermediate respiration rates were found in the North Sea populations, and relatively high metabolic rates in the Bay of Biscay and Mediterranean Sea populations. These observations fall together with the low growth rates in bivalves from the Baltic Sea, intermediate growth rates in North Sea populations, and the highest growth rates found in the Bay of Biscay and in some Mediterranean Sea populations (Bachelet 1980; Fuentes et al. 1998, 2000; Gangnery et al. 2004; Hummel et al. 1998; Peteiro et al. 2006; Westerborn et al. 2002). This indicates that mean routine respiration rates, measured at ambient temperatures, do indeed reflect the metabolic rate and ultimately the physiological performance of bivalves.
Separate curves describing the respiratory response to acclimatization temperatures were found for the three genetic groups of Mytilus populations. This resulted in group-specific respiration rates at a given temperature, which was significantly lower for the Baltic Sea group that exhibited reduced metabolic rates at intermediate temperatures (12–15 °C), where populations from the other genetic groups mostly exhibited optimal respiration rates (see Fig. 5c). In line with our earlier conclusion that the observed suboptimal respiration rates represent metabolic adjustment to limiting extrinsic factors (including energy–substrate availability), we expect that these group-specific response curves mainly reflect the food conditions in the respective sea-basins during the warmer months of the year. This assumption is supported by the seasonal variation in chlorophyll concentrations The chlorophyll concentrations in the Baltic Sea decline (Heiskanen and Leppänen 1995) at thermal conditions that correspond to the maximum annual chlorophyll concentrations in the North Sea and the Bay of Biscay (Colebrook 1979) and their adjacent estuaries (Rybarczyk et al. 1993; van Bergeijk et al. 2006). It also explains the exceptionally high respiration rates of some populations from the North Sea group at high habitat temperatures, e.g., specific dynamic action, facilitated by high ambient food availability. Still, differential genetic adaptation to temperature may add to the observed physiological differences, and cannot be excluded as an explanation.
The relation between the respiration rate and the acclimatization temperature shows great overlap upon comparing Mytilus spp. with M. balthica. Further comparison with other studies demonstrates that seasonal variation in the acclimatized respiration rates obtained from two Mytilus populations from England (Bayne and Widdows 1978) fits the relation described in this study. The respiratory performances of other bivalve species, such as Cerastoderma edule (Newell and Bayne 1980), Ostrea edulis (Beiras et al. 1994), or Dreissena polymorpha (Sprung 1995), were highly comparable as well. This great similarity in respiratory performance among European bivalve species suggests that they share a comparable evolutionary relation with temperature.
Sensitivity of the metabolic rate to temperature changes
We hypothesized that the sensitivity to temperature of the metabolic rate would decrease towards more southern localities, especially in the warmer seasons. Such latitudinal clines were only observed in January for Mytilus spp., and in April for M. balthica populations. In October no specific pattern was observed for Mytilus populations, since the Q
10 values were low in all populations. This may be related to limiting food conditions in autumn. Absence of significant latitudinal clines in July was caused by cline-interruption, which corresponded to the geographic transition of one genetic group to the next. These genetic group specific clines became especially apparent in mussels when the Q
10 was presented as a function of the acclimatization temperature (Fig. 6).
The obtained Q
10 values decreased with increasing acclimatization temperature for three reasons. First, metabolic down-regulation for energetic balancing results in a reduced Q
10. Second, Q
10 will decrease near breakpoint temperatures. Third, during thermal fluctuations that involve high peaks in habitat temperature, ectotherms will minimize the sensitivity of their metabolic rate, avoiding excessive rates when exposed to elevated temperatures (Peck et al. 2002; Widdows 1976; Wilson and Elkaim 1991). Thus, the increasing abundance of relatively nutrient-poor and thermally dynamic habitats causes a gradual shrinking of the ecological niche of these bivalve taxa towards the warm end of their distribution range. The group-specific clines discussed in the preceding paragraph indicated differential adaptation to temperature among Mytilus spp. populations. Since these bivalve species have great dispersal capacities, strongly coupled to hydrodynamic circulation (Gilg and Hilbish 2002), genetic divergence within these species requires geographic isolation. During isolation in different climatic regions, selective genetic variation may have evolved at the same spatial scale as the observed neutral genetic variation. This may explain why the biogeography of these genetic groups is associated with European temperature gradients. Physiological studies revealed that mussels from different genetic groups exhibit different growth rates when hatched under similar conditions (Beaumont et al. 2004; Hillbish et al. 1994). Differential thermal adaptation has recently been demonstrated for different mussel species from the West coast of North America as well (Fields et al. 2006). The differential adaptation to temperature indicated by our results, combined with the strong spatial variability of the coastal climate in the Bay of Biscay (Puillat et al. 2004), may explain the broad and mosaic-like transition from one mussel-group present in this area to the next (Bierne et al. 2003).
Q10 values lower than one only occur beyond the breakpoint temperature, where the metabolic rate decreases with increasing temperature. Since thermal conditions beyond breakpoint temperatures are not beneficial for bivalve performance and are most probably lethal when extended, it is most interesting to observe that the southernmost Mytilus spp. populations in our research area exhibited Q10 values of less than one at acclimatization temperatures in July 2004. In the Santa Giusta Lagoon (21), the monthly mean water temperature reached 27 °C, exceeding the breakpoint temperature of mussels. Very few bivalves survived the summer of 2004 in the Santa Giusta lagoon (21), indicating that thermal tolerance limits were indeed crossed under field conditions. Survival of temperature-induced stress depends on its duration and on the physiological status and condition of the organisms. Widdows and Bayne (1971) found that mussels can cope with relatively high temperatures so long as they can regularly recover in water at a suitably low temperature. This is an important strategy that allows survival of temporary heat exposure, e.g., when exposed to the air during low tide in summer. The range and limits of this relation for mussels or clams have not been studied in depth yet.
In conclusion, we demonstrated that the respiratory responses to temperature of two European bivalve taxa are greatly dependent on seasonal variations in temperature. These responses, obtained throughout all seasons and at a large geographic scale, fit together in a framework when presented as a function of the acclimatization temperature. This framework is useful in both the fundamental and the applied sciences, facilitating the interpretation of respiration rates measured under ambient conditions, and the further development of ecophysiological theory. We observed that the maximum thermal sensitivity of the metabolic rate decreases with increasing acclimatization temperature, crossing a threshold (Q
10 = 1) in Mytilus spp. at the maximum acclimatization temperatures observed in the field. Whether the temperature quotient will become less than one in M. balthica populations when the acclimatization temperature exceeds 23 °C cannot be answered with any certainty, since no breakpoint temperature is observed in the experimental southern populations under or near ambient conditions.
(Onto)genetic adaptation to regionally different climates implies that climate change will affect not only marginal populations via their metabolic rates, but all genetic groups are expected to shift northward with increasing temperatures. Although the dispersal capacity of mussel larvae and the active transport of juvenile and adult mussels for aquaculture purposes support range shifts with the speed of climate change, other ecological and physiological variables and their impact on the environment need to be investigated to predict the fate of mussels and other bivalve populations under changing climatic conditions. For M. balthica populations, no differential adaptation to temperature was observed among divergent groups, which leads us to expect that the direct impact of climate-induced temperature changes will be restricted to the southernmost populations.
Electronic supplementary material
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metabolic rate
climate change
respiration rate
distribution range
thermal tolerance


Assessment of normal tricuspid valve anatomy in adults by real-time three-dimensional echocardiography

Background The tricuspid valve (TV) is a complex structure. Unlike the aortic and mitral valve it is not possible to visualize all TV leaflets simultaneously in one cross-sectional view by standard two-dimensional echocardiography (2DE) either transthoracic or transesophageal due to the position of TV in the far field.
Introduction
The tricuspid valve (TV) is a multi-component complex structure [1]. In classic anatomic studies the anterior, septal and posterior TV cusps were described [2, 3]. Unlike the aortic and mitral valve it is not possible to visualize all TV cusps simultaneously in one cross-sectional view by standard transthoracic two-dimensional echocardiography (2DE) [4]. During transesophageal 2DE small changes in transducer angle, probe position and rotation may bring to light some additional TV details [5, 6]. However, because of the position of the TV in the far field in relation to probe, transesophageal 2DE can still only provide limited information and can also not visualize all TV cusps simultaneously. In three-dimensional (3D) transesophageal image reconstruction and intracardiac echocardiography studies this goal could be achieved but at the cost of some procedural risks and an increase in procedural duration [7, 8]. Real-time three-dimensional echocardiography (RT3DE) can visualize the atrio-ventricular valves from both the ventricular and atrial side in detail without these limitations [9]. This study aimed to apply RT3DE for quantitative and qualitative assessment of normal TV anatomy.
Subjects and methods
In one hundred patients (mean age 30 ± 9 years, 65% males) the TV was examined by transthoracic RT3DE after an informed consent. All patients had sinus rhythm and normal right-sided heart (normal right ventricular dimensions and function, normal right atrial dimension, trivial or absent tricuspid regurgitation and normal tricuspid valve function). Patients with good 2DE image quality only were included. RT3DE was done with a commercially available ultrasound system (Philips Sonos 7500, Best, The Netherlands) attached to a X4 matrix array transducer capable of providing real-time B-mode images. The 3D data set was collected within approximately 5–10 s of breath holding in full volume mode from an apical window and transferred for off-line analysis with TomTec software (Unterschleissheim, Munich, Germany). Data analysis of 3D images was based on a two-dimensional approach relying on images obtained initially from the apical 4-chamber view. The images were adjusted to put the TV in the center of interest. To exclude non-relevant tissue, the TV was sliced between the two narrowest lines by which all parts of the TV leaflets were still in between. The TomTec software allows in this way visualization of the short-axis TV view in a 3D display (see Fig. 1). RT3DE gain and brightness were adjusted to improve delineation of anatomic structures. The following points were checked for visualization: 1) tricuspid annulus diameter and area, 2) TV leaflets (number, mobility, thickness and relation to each other), 3) TV area, and 4) TV commissures (antero-septal, antero-posterior, and postero-septal) including the position of their closure lines. All these structures were classified according to a subjective 4-point scale for image quality (1 = not visualized, 2 = inadequate, 3 = sufficient and 4 = good).
Fig. 1TomTec quadri screen display of the tricuspid valve. The upper two images represent two-dimensional views created from the 3D data set (4-chamber, left and orthogonal view, right). The lower left image represents a two-dimensional short axis view and the lower right image represents the 3D image
For quantitative assessment of TV the following RT3DE data were obtained: 1) TV annulus diameter defined as the widest diameter that could be measured from an end-diastolic still frame, 2) maximal TV annulus area obtained from an end-diastolic still frame and measured by manual planimetry, 3) TV area defined as the narrowest part of the TV at the time of maximal opening and measured by manual planimetry, and 4) TV commissural width obtained from a late-diastolic still frame using zoom function to avoid underestimation. The images were optimized for each commissure along its plane to measure the maximal width of the angle formed by the two adjacent TV leaflets.
To identify the TV leaflets visualized by the standard 2DE images the TomTec quad screen display was used. As seen in Fig. 1, this screen contains four images; the upper two images are 2DE images perpendicular to each other, the lower two images are a short-axis 2DE image and a RT3DE image. From the properly chosen two-dimensional image a mid-diastolic frame was selected to visualize the TV leaflets just separated from each other. Each leaflet was defined by a marker, after which this marker position was compared with the RT3DE image to detect which leaflet was shown in the 2DE images. Analysis of images was done by two experienced echocardiographers (AMA, JSM) independently. Each one dealt with the full volume image as acquired from echo machine and the selection of cut plane, angulation and gain setting adjustment were dependable on his experience.
Statistical analysis
All data obtained by RT3DE were presented as mean ± SD. Interobserver and intraobserver agreements for the visualization score were estimated using kappa values for each morphologic feature and classified as poor (kappa < 0.4), moderate (kappa 0.4 to 0.7), or good (kappa > 0.7). Interobserver and intraobserver variabilities for RT3DE measurements was assessed according to the Bland and Altman method in a randomly selected group of 50 patients [10] Table 1.
Table 1Scores for real-time three-dimensional echocardiography visualization of TV structuresScoreTV annulusTV leafletsTV areaTV commissuresGood (4)60%80%55%50%Sufficient (3)30%10%30%20%Inadequate (2)10%10%15%20%Not visualized (1)0%0%0%10%Mean score3.5 ± 0.73.7 ± 0.63.4 ± 0.73.1 ± 1.0Median score3.03.03.02.5Abbreviations: TV = tricuspid valve
Results
Acquisition and analysis of the RT3DE data was performed in approximately 10 min per patient. The TV could be visualized in 90% of patients enface from both ventricular and atrial aspects in relation to adjacent cardiac structures. In these 90 patients detailed analysis of the TV was performed including tricuspid annulus shape and size, TV leaflets shape, size, and mobility, and commissural width.
Tricuspid annulus
Tricuspid annulus visualization was good in 54 patients (60%), sufficient in 27 patients (30%), and inadequate in 9 patients (10%). As seen in Fig. 2, tricuspid annulus shape appeared as oval rather than circular. Tricuspid annulus diameter and area could be measured in 63 patients (70%), normal values were 4.0 ± 0.7 cm and 10.0 ± 2.9 cm2, respectively.
Fig. 2Oval-shaped Tricuspid annulus (the line represent the tricuspid annulus diameter, the dots demark the area)
Tricuspid valve leaflets
Visualization of the three TV leaflets (in motion) was good in 72 patients (80%), sufficient in 9 patients (10%), and inadequate in another 9 patients (10%). The anterior leaflet was the largest and most mobile of the three leaflets and had a nearly semicircular shape. The septal leaflet was the least mobile and had a semi-oval shape. Its position was parallel to the interventricular septum. The posterior leaflet was the smallest one with variable shape. It was clearly separated from the septal leaflet in all patients but in 10% of patients it was hard to discriminate the posterior leaflet from the anterior leaflet even during maximal TV opening.
From the RT3DE data set all standard two-dimensional TV cross-sections (apical 4-chamber, parasternal short-axis and parasternal long-axis right ventricular inflow) were simulated. As seen in Fig. 3, in the apical 4-chamber view in all patients the septal leaflet was seen adjacent to the septum and the anterior leaflet was seen adjacent to the right ventricular free wall. In the parasternal short-axis view, the posterior leaflet was seen adjacent to the right ventricular free wall in 92% of patients and in the remaining 8% no leaflet could be obtained although modification of the cut plane downward could identify this leaflet. In this view the leaflet adjacent to the aorta was the anterior in 52% and the septal leaflet in 48%. In the parasternal right ventricular inflow view the leaflets seen were identical to the apical 4-chamber view with in all patients the septal leaflet seen adjacent to the septum and the anterior leaflet seen adjacent to the right ventricular free wall.
Fig. 3Identification of the tricuspid valve leaflets seen on two-dimensional imaging. Below the 2D images, percentage of leaflet identification in each standard view depending the RT3DE images
Tricuspid valve area
Visualization of the triangular shaped TV area was good in 50 patients (55%), sufficient in 27 patients (30%), and inadequate in 13 patients (15%). As seen in Fig. 4, the anterior and septal leaflets formed the TV area’s angle and the small posterior leaflet formed its base. TV area could be measured in 77 patients (86%) and mean TV area in these patients was 4.8 ± 1.6 cm2.
Fig. 4Triangular shape TV area and commissural views
Tricuspid valve commissures
As seen in Fig. 4, the three TV leaflets were separated from each other by three commissures. The commissures and the direction of closure lines were good visualized in 45 patients (50%), sufficient in 18 patients (20%), inadequate in 18 patients (20%), and not visualized in 9 patients (10%). TV commissural width could be obtained in 63 patients (70%), mean commissural width in these patients was 5.4 ± 1.5 mm for the antero-septal commissure, 5.2 ± 1.5 mm for the postero-septal commissure, and 5.1 ± 1.1 mm for the antero-posterior commissure, respectively. Visualization and measurement of commissures was relatively easy for the antero-septal commissure and most difficult for the antero-posterior commissure. All measurements are listed in Table 2 as absolute value and indexed to body surface area.
Table 2Normal (absolute and index) values of tricuspid valve annulus (diameter and area), Tricuspid valve area and the width of the 3 commissuresParameterAbsolute valueIndex valueTricuspid annulus diameter 4.0 ± 0.7 (cm)2.2 ± 0.4 (cm/m2)Tricuspid annulus area 10.0 ± 2.9 (cm2)5.5 ± 1.6 (cm2/m2)Tricuspid valve area 4.8 ± 1.6 (cm2)2.7 ± 0.9 (cm2/m2)Antero-septal commissure 5.4 ± 1.5 (mm)2.9 ± 0.8 (mm/m2)Postero-septal commissure5.2 ± 1.5 (mm)2.9 ± 0.7 (mm/m2)Antero-posterior commissure 5.1 ± 1.1 (mm)2.8 ± 0.6 (mm/m2)
Interobserver variability
The visualization score between two observers was good for the TV annulus (kappa value 0.91) and TV leaflets (kappa value 0.71) and moderate for the TV commissures (kappa value 0.59). As seen in Fig. 5, good interobserver correlations were found for measurement of TV annulus (r = 0.98, P < 0.0001) and TV area (r = 0.95, P < 0.0001) and fair correlation was found for TV commissural width (r = 0.51, P < 0.001). In the same Figure, the interobserver agreement for TV annulus diameter (mean difference −0.28 ± 1.20 mm, agreement: 2.12, −2.68), for TV area (mean difference: 0.17 ± 0.52 cm2, agreement: 1.21, −0.87), and for mean TV commissural width (mean difference: 0.01 ± 0.62 mm, agreement: 1.25, −1.24) is displayed.
Fig. 5Interobserver correlations (top) and Bland–Altman analysis (bottom) of TV annulus, leaflets, and commissures
Intraobserver variability
The visualization score by the first observer at 2 separate settings was good for the TV annulus (kappa value 0.92) and TV leaflets (kappa value 0.73) and moderate for the TV commissures (kappa value 0.58). Intraobserver agreement was (mean difference −0.26 ± 1.15 mm, agreement: 2.14, −2.56) for TV annulus diameter, (mean difference: 0.15 ± 0.50 cm2, agreement: 1.15, −0.85) for TV area, and (mean difference: 0.02 ± 0.60 mm, agreement: 1.22, −1.18) for the mean TV commissural width.
Discussion
Two-dimensional echocardiography is a valuable imaging modality for the functional assessment of TV [11–13]. However, with 2DE it is not possible to visualize all TV cusps simultaneously in one cross-sectional view nor can detailed anatomical information of the TV annulus, leaflets, and commissures be provided. Previous studies and case reports described visualization of TV by RT3DE [9, 14] in abnormal states, while this study applied RT3DE for the morphological assessment of the normal TV anatomy. RT3DE allowed analysis of TV annulus, leaflets and commissures in the majority of patients. Beside this morphologic description, quantitative assessment could be obtained. However, it should be noticed that only patients with good 2DE image quality underwent RT3DE. In our experience these patients represent over 50% of the total number of patients referred to our echocardiographic laboratory. Nevertheless, RT3DE allowed TV analysis to a level quite comparable to that recently reported by others for the mitral valve leaflets [15].
One of the salient findings in our study was the identification of the TV leaflets as seen in the routine 2DE views. It is still a matter of controversy in echocardiographic textbooks. In one well known echocardiographic textbook [16], the leaflet seen in the apical 4-chamber view adjacent to the right ventricular free wall was described as being the anterior or posterior leaflet depending on the exact rotation and angulation of the image plane. However, in our study this leaflet was consistently found to be the anterior leaflet (see Fig. 6 for explanation), as described in another textbook [17]. Also, in both these echocardiographic textbooks [16, 17] in the parasternal short-axis view the leaflet adjacent to the right ventricular free wall was described as being the anterior. However, as shown in Fig. 3 and explained in Fig. 6, in all patients in whom a leaflet could be identified in our study it was the posterior one.
Fig. 6Surgical view of the heart valves demonstrating the range of the two-dimensional echocardiographic 4-chamber and short-axis planes
In our study tricuspid annulus diameter (and area) could be reliably obtained with RT3DE. Tricuspid annulus measurement is of critical importance in the TV surgical decision-making process if a patient is operated for mitral valve disease and has concomitant TV regurgitation [18, 19]. In addition, TV area could be reliably obtained and this may have important implications for the diagnosis of tricuspid stenosis [20, 21]. Visualization of commissures and measurement of its width were obtained with difficulty, in particular for the antero-posterior commissure. Commissural width also showed weak interobserver correlation. This may be due to differences in the commissural levels and tissue dropout. For proper assessment of the three commissures, more cut planes with different angles are needed. However, assessment of commissural width may also be a valuable tool for the diagnosis, follow up, and selection of therapeutic strategy of tricuspid stenosis. All our RT3DE measurements were consistent with the measurements described in anatomical studies [2, 3]. Our data may potentially take RT3DE a step further into clinical routine (providing accurate TV measurements) and may enhance the understanding of TV valve morphology during the cardiac cycle (Fig. 7). Also, the detailed assessment by RT3DE may affect the therapeutic decision of various TV abnormalities and thus expand the abilities of non-invasive cardiology [22].
Fig. 7Visualization of the 3 TV leaflets during valve closure (A), at early diastole (B), and at late diastole (C)
Limitation of study
The main limitation of this study is that RT3DE data were not compared with a “gold standard” such as magnetic resonance imaging, autopsy or surgical findings. Also, RT3DE images more critically depend on 2DE image quality and could be obtained only in patients with sinus rhythm during hold breath that limits its application to all. The study included patients with narrow age range (21–39 years), and thus the normal findings in this study is confined to this age group and could not be applied to both extremities (<21 and >39 years).
Conclusion
Three-dimensional imaging of the TV is feasible in a large number of patients. RT3DE may add to functional 2DE data in description of TV anatomy and providing highly reproducible and actual reality (anatomic and functional) measurements.


Duration of breast-feeding and the incidence of type 2 diabetes mellitus in the Shanghai Women’s Health Study

Aims/hypothesis The aim of this study was to examine the association between lifetime breast-feeding and the incidence of type 2 diabetes mellitus in a large population-based cohort study of middle-aged women.
Introduction
The prevalence of type 2 diabetes mellitus has been increasing rapidly worldwide [1], making knowledge of risk factors and protective factors associated with type 2 diabetes mellitus essential for the development of prevention strategies.
Results from animal and human studies suggest an improvement in glucose and insulin sensitivity during lactation [2–5]. Data from two large cohorts in the USA also indicate that longer duration of breast-feeding may reduce the risk of type 2 diabetes mellitus by improving glucose homeostasis [6].
We examined the association between lifetime breast-feeding and the incidence of type 2 diabetes mellitus in a large population-based cohort study of middle-aged women, the Shanghai Women’s Health Study (SWHS).
Methods
Study population The SWHS is a population-based prospective cohort study of middle-aged women (40–70 years old) conducted in seven urban communities of Shanghai, China. Details of the SWHS survey have been reported elsewhere [7]. From a total of 81,170 women who were invited to participate, 75,221 were recruited (92.7% participation rate). Reasons for non-participation were refusal (3.0%), absence during the enrolment period (2.6%) and other reasons (health, hearing, speaking problems; 1.6%). After exclusion of women younger than 40 years or older than 70 years at the time of interview (n = 278), 74,942 women remained for the study. Participants completed a detailed survey as part of an in-person interview, which included assessment of dietary intake, physical activity, measurement of anthropometrics and other lifestyle factors. The original questionnaires, in Chinese, are available from the corresponding author upon request. Protocols for the SWHS were approved by the Institutional Review Boards of all institutes involved in the study and written informed consent was obtained prior to interview. Biannual in-person follow-up of all living cohort members was conducted by in-home visit from 2000 to 2002 and from 2002 to 2004, with a response rate of 99.8 and 98.7%, respectively; only 934 participants were lost to follow-up.
Outcome ascertainment Incident type 2 diabetes mellitus was identified through follow-up surveys. A total of 1,561 parous women reported a type 2 diabetes mellitus diagnosis since the baseline survey. For the current study we considered a case of type 2 diabetes mellitus to be confirmed if the participants reported having been diagnosed with type 2 diabetes mellitus and met at least one of the following criteria: fasting glucose level ≥7 mmol/l on at least two separate occasions or an oral glucose tolerance test with a value ≥11.1 mmol/l and/or use of hypoglycaemic medication (i.e. insulin or oral hypoglycaemic drugs). All tests were performed as part of patients’ primary care. Of the self-reported cases, a total of 869 participants met the study outcome criteria and are referred to herein as confirmed cases of type 2 diabetes mellitus. We performed analyses restricted to confirmed cases as well as analyses including all cases of type 2 diabetes mellitus.
Breast-feeding duration assessment Information on each pregnancy was obtained during the in-person interview. This included date and outcome of each pregnancy and whether and for how long the participant breastfed each child. Based on this information, we calculated the duration of breast-feeding (months and years) and duration of breast-feeding per live birth (years).
Measurement of potential confounders Anthropometric measurements were taken at baseline recruitment according to a standard protocol by trained interviewers who were retired medical professionals [8]. Self-reported body weight history was obtained for ages 20 and 40 years and at baseline recruitment. We calculated BMI (weight in kg divided by the square of height in m), WHR (waist circumference divided by hip circumference) and standardised weight change (difference between measured weight at baseline and weight at 20 years divided by the interval between study recruitment and 20 years; kg/year).A structured questionnaire was used at the baseline survey to collect information on socio-demographic factors such as age, level of education (none, elementary school, middle/high school, college), family income in yuan per year (<10,000, 10,000–19,999, 20,000–29,999, >30,000), occupation (professional, clerical, manual labour/other, housewife/retired), smoking (smoked at least one cigarette per day for more than 6 months continuously) and alcohol consumption (had ever drunk beer, wine or spirits at least three times per week). History of diseases such as diabetes, cancer, cardiovascular disease and high blood pressure was also collected.Information on physical activity was obtained using a validated questionnaire [9]. The questionnaire evaluated exercise and sport participation, daily activity and daily commuting round-trip journey to work. We calculated the metabolic equivalents (METs) for each activity, using a compendium of physical activity values [10]. We derived a quantitative estimate of overall non-occupational activity (MET-h per day).Dietary intake was assessed through an in-person interview using a validated food frequency questionnaire at the baseline recruitment survey and at the first follow-up survey [11]. The Chinese food composition tables [12] were used to estimate energy intake (kJ/day).
Statistical analysis Person-years for each participant were calculated as the interval between the baseline recruitment to one of the following: diagnosis of type 2 diabetes mellitus; censored at death; or completion of the second follow-up survey.The Cox proportional hazards model was used to assess the effect of breast-feeding on the incidence of type 2 diabetes mellitus. Tests for trend were performed by entering the categorical variables as continuous parameters in the models. In all models, we adjusted for the following potential confounding variables: age, BMI, WHR, total energy, physical activity, number of live births (entered as continuous variables), income level, education level, occupation, smoking status, alcohol consumption status and presence of hypertension at baseline (as categorical variables).We also derived a propensity score by regressing breast-feeding (yes/no) on risk factors for type 2 diabetes mellitus (age, daily energy intake, BMI, WHR, smoking, alcohol consumption, physical activity, hypertension, income, education level, occupation status, oral contraceptive use, vegetable intake, legume intake, meat intake and staples intake). We then examined the association between breast-feeding (yes/no) and type 2 diabetes mellitus by including the propensity score in the Cox proportional hazard model. All analyses were performed using SAS (version 9.1, SAS Institute, Cary, NC, USA), and all tests of statistical significance were based on two-sided probability. A p value of less than 0.05 was considered statistically significant.
Results
The average age of the entire SWHS cohort (n = 74,942) was 52.1 (SD = 9.1). The average age at first pregnancy was 25.5 years (median: 25.7 years) and the median number of live births was 1.0. A total of 3.3% women had no children, 54.4% had only one child, 21.2% had two children, 10.5% had three children, 6.2% had four children and 4.3% had five or more children. The incidence of diabetes mellitus was 6.0 cases per 1,000 person-years at risk.
There were 62,095 parous women in this study with no prior history of type 2 diabetes mellitus, cancer or cardiovascular disease at study recruitment; of these 50,700 (81.65%) reported having breastfed their children. The average number of live births was 1.7, the average number of months of breast-feeding was 14.6 and the average number of years of breast-feeding per child was 0.6. The number of live births was associated with months of breast-feeding (Spearman correlation coefficient, 0.70; p < 0.001). Age-standardised characteristics of the study population by duration of breast-feeding are shown in Table 1. Women with a longer duration of breast-feeding were older, had less education, lower income and were less likely to be employed at the time of the survey or to hold a professional job. Duration of breast-feeding was also associated with smoking and higher physical activity in this population. We did not find an association between differences in participation in exercise or prevalence of hypertension at baseline and duration of breast-feeding (p > 0.05).
Table 1Age-standardised characteristics of parous women in the Shanghai Women’s Health Study by duration of breast-feeding Duration of breast-feeding (months)0>0 to 6>6 to 11>11 to 35≥36p value for trendaWomen (n)11,39510,46314,95818,1447,135Cases of diabetes (n)225163253585335Person-years (n)53,03848,77069,74183,87032,535<10 years since last pregnancy (%)26.921.928.918.63.7Age, median (Q25–Q75)45 (42–49)45 (42–49)45 (42–49)54 (49–61)64 (62–67)<0.001Energy intake, mean kJ/day (SE)6,875 (14)6,927 (15)6,920 (13)6,985 (11)6,738 (21)<0.001Live births, median (Q25–Q75)1 (1–1)1 (1–1)1 (1–1)2 (1–2)4 (3–5)<0.001BMI, mean kg/m2 (SE)23.4 (0.03)23.2 (0.03)23.6 (0.03)24.2 (0.02)24.7 (0.04)<0.001WHR, mean (SE)0.80 (0.0005)0.8 (0.0005)0.80 (0.0005)0.81 (0.004)0.82 (0.007)<0.001Weight gain, mean kg/year (SE)0.34 (0.003)0.31 (0.003)0.33 (0.003)0.36 (0.003)0.37 (0.005)<0.001Weight at age 20 years, median kg (Q25–Q75)48 (45–53)49 (45–53)50 (45–54)50 (45–54)50 (45–55)<0.001Smoker (%)1.7 (1.5–1.9)1.4 (1.2–1.9)1.5 (1.3–1.7)2.4 (2.3–2.5)3.6 (3.2–4.0)<0.001Ever drinker (%)1.9 (1.7–2.1)2.1 (1.9–2.3)2.1 (1.9–2.3)2.4 (2.3–2.5)1.9 (1.7–2.1)<0.001Exercise (%)30.5 (29.5–31.5)32.9 (31.9–33.9)34.3 (33.3–35.3)33.7 (32.7–34.7)31.4 (30.4–32.4)0.18High physical activityb (%)22.8 (22.0–23.6)22.1 (21.3–22.9)24.4 (21.6–25.2)27.3 (26.9–27.7)41.2 (40.2–42.2)<0.001Education (%)None11.0 (12.4–11.6)8.5 (7.9–9.1)9.6 (9.0–10.2)17.8 (17.4–18.2)41.2 (40.2–42.2)<0.001Elementary38.2 (37.2–39.2)34.4 (33.4–35.4)39.2 (38.2–40.2)44.3 (43.8–44.8)46.9 (47.9–48.9)Middle/high school32.5 (31.5–33.5)34.3 (33.3–35.3)32.4 (31.4–33.4)27.1 (26.7–27.5)8.6 (8.0–9.2)College18.3 (17.5–19.1)22.7 (21.9–23.5)18.8 (18.0–19.6)10.8 (10.5–11.1)3.3 (2.9–3.7)Household income (%)<10,000 yuan14.0 (13.4–14.6)11.6 (11.0–12.2)12.6 (12.0–13.2)15.6 (15.3–15.9)22.1 (21.3–22.9)<0.00110,000–19,999 yuan38.8 (37.8–39.8)35.8 (34.8–36.8)37.1 (36.1–38.1)38.0 (37.5–38.5)38.7 (37.7–39.7)20,000–29,999 yuan29.4 (28.5–30.3)31.3 (30.3–32.3)30.4 (29.4–31.4)28.6 (28.2–29.0)27.8 (27.0–28.6)>30,000 yuan17.8 (17.0–18.6)21.4 (20.6–22.4)19.8 (19.0–20.6)17.8 (17.4–18.2)11.4 (10.8–12.0)Occupation (%)Professional21.6 (20.8–22.4)25.9 (25.1–26.7)22.2 (21.4–23.0)16.4 (15.6–17.2)6.8 (6.2–7.4)<0.001Clerical12.8 (12.2–13.4)12.4 (11.6–12.8)13.2 (12.6–13.8)13.7 (13.4–14.0)19.5 (18.7–20.3)Manual labour/others21.8 (21.0–22.6)21.0 (20.2–21.8)23.1 (22.3–23.9)23.6 (23.2–24.0)20.0 (19.2–20.8)Housewife/retired43.8 (42.8–44.8)40.7 (39.7–41.7)41.6 (40.6–42.6)46.3 (45.8–46.8)53.6 (52.6–54.6)Hypertension (%)18.4 (17.6–19.2)18.3 (17.5–19.1)18.8 (18.0–19.6)19.8 (19.4–20.2)16.6 (15.8–17.4)0.12Data are presented as n, percent, median (Q25–Q75, interquartile range) and mean (SE), and are directly standardised in years to the age distribution of the populationMeans of energy intake, BMI, WHR and standard weight are adjusted by ageWeight at 20 years and number of live births were not normally distributed and could not be adjusted for ageap values for trend were calculated by: proportional odds model for prevalence of population characteristics; ANOVA test for daily energy intake, BMI, WHR and standard weight gain; and Kruskal–Wallis test for age, number of live births and weight at 20 yearsbHigh physical activity: participants in the upper quartile of total METs
Women who had breastfed tended to have a lower risk of type 2 diabetes mellitus [relative risk (RR) = 0.88; 95% CI, 0.76–1.02; p = 0.08] than women who had never breastfed, in analyses adjusted for age, daily energy intake, BMI, WHR, number of live births, occupation, income levels, education, smoking, alcohol consumption, physical activity and presence of hypertension (Table 2). When we adjusted the analysis by a propensity score of predictors of type 2 diabetes mellitus, the RR was 0.81 (95% CI, 0.70–0.94; p < 0.01).
Table 2Associations between type 2 diabetes mellitus and duration of breast-feeding for all participants, Shanghai Women’s Health Study %Cases per person-yearsRR95% CIp value for trendBreast-feedingNo18.35225/53,0381.00Yes81.651,336/234,9160.880.76–1.02Duration of breast-feeding Years  018.35225/53,0381.000.01  >0–0.9947.99416/118,5110.880.75–1.04  >0.99–1.9917.31343/57,4180.890.75–1.06  >1.99–2.997.57242/26,4510.880.71–1.07  >2.99–3.994.15148/14,9290.750.59–0.96  ≥44.63187/17,6060.680.52–0.90 Years per child  018.35225/53,0381.000.11  >0–0.4915.10200/43,6150.910.75–1.10  >0.49–0.9944.20659/127,4530.870.74–1.02  ≥122.35477/63,8480.870.78–1.03Values are adjusted for age, daily energy intake, BMI, WHR, number of live births, smoking, alcohol consumption, physical activity, education, income, occupation and hypertension
The duration of breast-feeding and duration of breast-feeding per child were associated with a lower risk of type 2 diabetes mellitus (Table 2). The fully adjusted RR for 0, >0 to 0.99, >0.99 to 1.99, >1.99 to 2.99, >2.99 to 3.99 and ≥4 years of breast-feeding with the risk of type 2 diabetes mellitus were 1.00, 0.88, 0.89, 0.88, 0.75 and 0.68. The fully adjusted RR between years of breast-feeding per child and type 2 diabetes mellitus risk were 1.00, 0.91, 0.87 and 0.87 (p = 0.11 for trend) for 0, >0 to 0.49, >0.49 to 0.99 and ≥1 years of breast-feeding per number of births. Associations between months of breast-feeding and incidence of type 2 diabetes mellitus are presented in Table 3. BMI and WHR are the two major confounding factors for the association between the duration of breast-feeding and risk of type 2 diabetes mellitus. Both BMI and WHR were weakly correlated with breast-feeding (r = 0.22 and 0.22, respectively). The correlation between BMI and WHR was 0.46. The association between the duration of breast-feeding and diabetes mellitus was accentuated and the trend was only marginally significant when we adjusted the model for BMI. Additional adjustment for WHR resulted in the trend becoming statistically significant (p = 0.04). The association between breast-feeding and type 2 diabetes mellitus did not change much after adjustment for number of live births. When we restricted the analysis of breast-feeding and incidence of type 2 diabetes to confirmed cases of type 2 diabetes mellitus, we found similar patterns of association (Table 4).
Table 3Incidence of type 2 diabetes by duration of breast-feeding, Shanghai Women’s Health Study 0>0 to 6 months>6 to 11 months>11 to 35 months≥36 months RR95% CIRR95% CIRR95% CIRR95% CIRR95% CIp value for trendModel 11.000.810.66–0.990.880.74–1.071.080.92–1.271.030.84–1.250.20Model 21.000.840.69–1.030.890.74–1.070.990.85–1.180.880.71–1.080.75Model 31.000.840.69–1.030.880.73–1.050.900.76–1.050.760.62–0.930.06Model 41.000.870.71–1.060.910.76–1.090.980.83–1.150.840.68–1.030.39Model 51.000.890.71–1.060.890.74–1.070.890.76–1.050.750.61–0.920.04Model 61.000.870.71–1.060.890.75–1.070.890.75–1.050.730.58–0.910.05Model 1 age-adjusted only; Model 2: age plus other confounders, but not BMI, WHR or number of live births; Model 3: model 2 plus BMI; Model 4: model 2 plus WHR; Model 5: model 2 plus BMI and WHR; Model 6: model 5 plus number of live birthsTable 4Associations between type 2 diabetes mellitus and duration of breast-feeding, Shanghai Women’s Health Study (confirmed cases of diabetes only) %Cases per person-yearsRR95% CIp valueBreast-feedingNo18.40128/52,8481.000.17Yes81.60741/233,8360.870.72–1.06Duration of breast-feeding Months  018.40128/52,8481.000.17  >0–616.9186/48,6170.800.61–1.05  >6–1124.21159/69,5580.980.77–1.24  >11–3529.11314/83,3980.840.68–1.05  >3611.37182/32,2630.740.55–1.01 Years  018.40128/52,8481.000.01  >0–0.9948.20297/11,8170.910.73–1.13  >0.99–1.9917.25200/57,1560.900.72–1.13  >1.99–2.997.4899/26,2420.720.54–0.95  >2.99–3.994.1065/14,8090.720.52–1.01  >44.5778/17,4540.670.46–0.96 Years per child  018.40128/52,8481.000.23  >0–0.4915.13116/43,4430.940.73–1.21  >0.49–0.9944.21359/126,9160.840.69–1.04  >122.25266/63,4470.880.70–1.11Values are adjusted for age, daily energy intake, BMI, WHR, number of live births, smoking, alcohol consumption, physical activity, education, income, occupation and hypertension
In analyses restricted to women who reported having been pregnant within the last 10 years (Table 5), the RR for type 2 diabetes mellitus by the duration of breast-feeding per number of live births was 1.00, 1.01, 0.74 and 0.65; (p = 0.03 for trend) for 0, >0 to 0.49, >0.49 to 0.99 and ≥1 years of breast-feeding per number of live births. Among women who had been pregnant within the last 10 years, the RRs of type 2 diabetes mellitus for 0, >0 to 0.99, >0.99 to 1.99, >1.99 to 2.99, >2.99 to 3.99 and ≥4 years of breast-feeding were 1.00, 0.80, 0.78, 0.58, 0.47 and 0.45 (p = 0.04 for trend). The RRs of type 2 diabetes mellitus for 0, >0 to 0.99, >0.99 to 1.99, >1.99 to 2.99, >2.99 to 3.99 and ≥4 years of breast-feeding among women who had been pregnant within the last 5 years were 1.00, 0.87, 0.68, 0.44, 0.69 and ≥0.49 (p = 0.02 for trend; data not shown in tables). In addition, we found that the RRs for diabetes mellitus associated with ≥12 months of breast-feeding were 0.66 (95% CI, 0.45–0.95), 0.63 (95% CI, 0.40–0.98), 0.49 (95% CI, 0.28–0.85) and 0.46 (95% CI, 0.26–0.86), respectively, when analyses were conducted with time since last pregnancy defined as ≤13, ≤10, ≤5 and ≤2 years (data not shown). In analyses restricted to women who had not been pregnant in the last 10 years, we still found inverse associations between breast-feeding and risk of type 2 diabetes mellitus, although the associations between months of breast-feeding and years of breast-feeding per child were not significant. The association between years of breast-feeding and type 2 diabetes mellitus was of marginal significance. The RRs of type 2 diabetes mellitus for 0, >0 to 0.99, >0.99 to 1.99, >1.99 to 2.99, >2.99 to 3.99 and ≥4 years of breast-feeding were 1.00, 0.90, 0.94, 0.94, 0.79 and 0.72 (p = 0.06 for trend). Similar trends were found when the analysis was restricted to confirmed cases of type 2 diabetes mellitus. Finally, we found that the beneficial effect associated with the duration of breast-feeding was more evident among women over 60 years of age (data not shown).
Table 5Duration of breast-feeding and risk of type 2 diabetes mellitus, Shanghai Women’s Health Study Time since last pregnancy≤10 years since last pregnancy>10 years since last pregnancyRR95% CIp value for trendRR95% CIp value for trendAll participants Months  01.000.051.000.20  >0–60.900.57–1.420.880.70–1.10  >6–110.830.55–1.250.910.74–1.12  >11–350.630.40–0.980.940.79–1.13  ≥360.660.30–1.480.760.60–0.97 Years  01.000.041.000.06  >0–0.990.870.60–1.240.900.75–1.08  >0.99–1.990.680.43–1.090.940.77–1.13  >1.99–2.990.440.21–0.920.940.76–1.17  >2.99–3.990.690.29–1.620.790.61–1.02  ≥40.490.18–1.350.720.54–0.96 Years per child  01.000.031.000.45  >0 –0.491.000.64–1.590.910.74–1.13  >0.49–0.990.740.51–1.070.900.76–1.07  ≥10.650.41–1.050.920.76–1.10Confirmed diabetes only Months  01.000.261.000.34  >0–60.800.41–1.530.810.60–1.09  >6–110.960.55–1.650.980.75–1.27  >11–350.690.39–1.220.870.69–1.11  >360.600.23–1.570.780.56–1.08 Years  01.000.101.000.06  >0–0.990.900.55–1.480.910.72–1.16  >0.99–1.990.800.44–1.440.930.72–1.19  >1.99–2.990.440.18–1.090.760.56–1.03  >2.99–3.990.530.17–1.590.770.54–1.09  >40.530.16–1.730.710.48–1.04 Years per child  01.000.251.000.44  >0–0.490.970.52–1.820.940.71–1.25  >0.49–0.990.810.49–1.330.860.68–1.08  >10.730.40–1.340.920.71–1.17Values are adjusted for age, daily energy intake, BMI, WHR, number of live births, smoking, alcohol consumption, physical activity, education, income, occupation and hypertension
Discussion
In this large, prospective population-based study of middle-aged Chinese women, breast-feeding was associated with a reduced risk of type 2 diabetes mellitus [13] independently of known risk factors for type 2 diabetes mellitus.
Our study adds to the limited data on the association between breast-feeding and risk of type 2 diabetes mellitus in mothers. Longer duration of breast-feeding was associated with reduced incidence of type 2 diabetes mellitus in two large US cohorts of young and middle-aged women, the Nurses’ Health Studies I and II [6]. Similar to our results, the RRs in these studies for parous women with 0, >0 to 3 months, >3 to 6 months, >6 to 11 months, >11 to 23 months and ≥23 months of breast-feeding were 1.00, 0.98, 1.03, 0.96, 0.92 and 0.88 (p = 0.02 for trend) for middle-aged women and 1.00, 1.04, 0.91, 0.87, 0.88 and 0.67 (p < 0.01 for trend) for younger women. The RR for women who had breastfed compared with women who had never breastfed was 0.97 (95% CI, 0.91–1.02) for middle-aged women and 0.90 (95% CI, 0.77–1.04) for younger women. In agreement with our findings, these studies also found that the protection conferred by breast-feeding appeared to wane with time since last birth and that longer duration of breast-feeding per pregnancy was associated with a greater benefit. However, among women with gestational diabetes, breast-feeding was not associated with lower risk of type 2 diabetes mellitus later in life [6]. To our knowledge, no other studies have examined the long-term association between breast-feeding and subsequent development of type 2 diabetes mellitus.
In the Nurses’ Health Studies I and II, analyses were adjusted for participants’ birthweight and BMI at 18 years, as the investigators found that duration of breast-feeding was inversely related to BMI at 18 years [6]. In our study, we only had information on birthweight for a subset of participants. The correlation coefficients between duration of breast-feeding and both birthweight of participants and BMI at 20 years were 0.02 and 0.12, respectively. We chose to adjust for current BMI and WHR in our analysis, rather than BMI at 20 years or weight at 20 years, as BMI and WHR were strongly associated with type 2 diabetes mellitus. In addition, measurements used to calculate BMI and WHR in our study were taken by trained professionals at the time of the interview, while weight at 20 years was self-reported. In analyses adjusted for BMI at 20 years, the fully adjusted RRs for 0, >0 to 0.99, >0.99 to 1.99, >1.99 to 2.99, >2.99 to 3.99 and ≥4 years of breast-feeding were 1.00, 0.91, 1.02, 1.09, 0.77 and 0.73 (p = 0.30 for trend). We conducted additional analyses adjusting for standardised weight gain (kg per year) since age 20 years and weight at age 20 years. The fully adjusted RRs for 0, >0 to 0.99, >0.99 to 1.99, >1.99 to 2.99, >2.99 to 3.99 and ≥4 years of breast-feeding were 1.00, 0.90, 0.96, 0.99, 0.76 and 0.68 (p = 0.08 for trend).
It has been suggested that breast-feeding may protect against type 2 diabetes mellitus by facilitating weight loss, although the association between breast-feeding and weight loss remains inconclusive [14–20]. In our population, the age-adjusted means of standard weight gain since age 20 years by months of breast-feeding were 0.34, 0.31, 0.33, 0.36 and 0.37 kg per year for 0, >0 to 6 months, >6 to 11 months, >11 to 35 months and ≥36 months of breast-feeding, respectively (p < 0.01). Thus, weight loss is unlikely to be the reason for the inverse association between breast-feeding and type 2 diabetes mellitus observed in our study.
Another possible mechanism is that breast-feeding may improve insulin sensitivity and glucose intolerance. In a study of both breast-feeding and non-breast-feeding non-diabetic women, insulin levels and insulin/glucose ratios were lower, while carbohydrate use and total energy expenditure were higher in the breast-feeding group [21]. Data from studies of women with gestational diabetes suggest that breast-feeding affects insulin and glucose homeostasis. In a study of 809 Latina women, breast-feeding was associated with improved glucose tolerance, fasting glucose and total area under the glucose tolerance curve [2], while in another study of 26 white women (14 breast-feeding, 12 non-breast-feeding), the breast-feeding group had a higher disposition index, indicating more efficient pancreatic beta cell function [3]. Data from animal studies also suggests that in the post-partum period, breast-feeding is associated with a decrease in insulin resistance. Blood glucose levels were reduced 20% and insulin levels were reduced 35% in lactating rats compared with non-lactating rats [4], while in another study, a 12-fold increase in insulin uptake was observed in the mammary glands of lactating rats, as well as a marked decrease in the plasma half-life of insulin [5]. Breast-feeding may also influence pituitary hormones [22] and may induce long-term changes in the hypothalamic-pituitary axis [23].
Our study has several strengths. Our population is representative of urban Shanghai. The high follow-up rates minimised the possibility of selection bias. In addition, the prevalence of breast-feeding in this population was very high (81.62%), and there was a high number of live births (of the total number of women in our study at baseline, 74,942 or 96.4% were parous). In addition, the extensive information on potential confounders and the large study size allowed us to examine the effect of duration of breast-feeding on the development of type 2 diabetes mellitus in detail.
The major limitation of the study is reliance on self-reported diabetes, which is an important factor to consider when interpreting these results. A recent report suggested that the prevalence of diabetes was under-diagnosed in Shanghai [13]. We are not aware of any programme for systematic screening for diabetes in our study area. At baseline recruitment, we conducted a urinary glucose test for all cohort members who donated a urine sample (88.2% of participants). We found that 1% of participants who reported never having been diagnosed by a physician as having diabetes had a positive urinary glucose test. These participants were excluded from the current analysis. However, it is possible some other type 2 diabetes cases remained undiagnosed in our study. Similarly, self-reported diabetes may also include some false positive cases. Misclassification of diabetes could weaken the association between duration of breast-feeding and the risk of type 2 diabetes mellitus. To address the possibility of surveillance bias, we conducted analyses restricted to women with confirmed diabetes and found similar results.
In our study, duration of breast-feeding was correlated with parity. In a recent study from the UK, having more children was associated with a higher risk of diabetes in women [24]. However, the association was attenuated after adjustment for BMI and socioeconomic factors. Similarly to the UK study, we found parity to be associated with lower income and education levels, higher BMI, WHR and standard weight gain, as well as smoking and alcohol consumption in this population. In age-adjusted analyses the incidence of diabetes was positively associated with number of children. The RRs of diabetes were 1.00, 1.38, 1.32, 1.31 and 1.62 for one, two, three, four and five or more children (p < 0.01). When we adjusted the analysis for BMI and WHR, parity was no longer associated with incidence of type 2 diabetes. We found that the association between breast-feeding and type 2 diabetes did not change much after adjustment for number of live births. Before and after inclusion of the number of live births in the analysis, the RRs of type 2 diabetes mellitus for 0, >0 to 6 months, >6 to 11 months, >11 to 35 months and ≥36 months of breast-feeding were 1.00, 0.87, 0.89, 0.89 and 0.75 (p = 0.04 for trend) and 1.00, 0.87, 0.89, 0.89 and 0.73 (p = 0.05 for trend), respectively.
Breast-feeding could also reflect differences in other risk factors in our population that might not have been completely controlled for or measured in our study. We applied a propensity score adjustment, including a wide array of lifestyle and dietary intake variables, and found little change in the pattern of associations between breast-feeding and type 2 diabetes mellitus (data not shown). Nevertheless, residual confounding from unmeasured or less than perfectly measured confounders, such as socioeconomic status at the time of breast-feeding, is still a concern. Thus, our results should be interpreted with caution, particularly given that the association was relatively weak and that the reduced risk was limited to participants with a longer duration of breast-feeding (≥36 months). In addition, over-adjustment due to the inclusion of possible mediating variables such as BMI and WHR in the model may also be a concern. In our study population, breast-feeding was weakly associated with BMI and WHR (r = 0.22 and r = 0.22, respectively). Further studies conducted in other populations and focused on the underlying biological mechanisms are warranted.
In summary, we found that breast-feeding duration was inversely associated with the risk of type 2 diabetes mellitus in this population. Together with results from two other large US cohorts and some clinical evidence of improved glucose homeostasis in breast-feeding women, these data suggest that breast-feeding may reduce the risk of type 2 diabetes mellitus in middle-aged women.

type 2 diabetes mellitus
middle-aged women
parous women
lifelong breast-feeding


Primary retroperitoneal mucinous cystadenoma with sarcoma-like mural nodule

Primary retroperitoneal cystadenomas are extremely rare. This is the first report in literature to describe a primary retroperitoneal cystadenoma with a sarcoma-like mural nodule. A 45-year-old woman complained of a left-sided abdominal mass. A computed tomography scan revealed a cystic mass with a mural nodule, which seemed to originate from the tail of the pancreas. At laparotomy the cyst was not adhered to the pancreas but localized retroperitoneally. Histologic examination showed a mucinous cystadenoma with only foci of borderline malignancy with a mural “sarcoma-like” nodule. In view of the surgical and histopathological findings, the mucinous cystadenoma was regarded as primary retroperitoneal. This case demonstrates that in the era of radiological preoperative refinement, pathological diagnosis remains of utmost importance, especially for rare cases.
Introduction
Mucinous cystadenomas of the ovary are clinically and histopathologically well-established and common tumors. Primary retroperitoneal mucinous cystadenomas are extremely rare. Such tumors are histologically similar to ovarian mucinous cystadenomas. Their histogenesis is still unclear. We report a case of primary retroperitoneal mucinous cystadenoma with foci of borderline malignancy containing a mural “sarcoma-like” nodule.
Case report
Clinical history
A 45-year-old, para 2, woman presented at the emergency room with a 3-week history of left-sided abdominal pain. She had felt a mass in the left lower quadrant 2 days before. Her clinical history included endometriosis and a car accident. The mass was progressive but not painful. Apart from the palpable mass of 15 cm in the left lower abdomen, physical examination was unremarkable. Ultrasonography demonstrated a 15-cm cystic mass with a 3.8-cm nodule in its wall. The uterus was normal in size, and internal ultrasonography showed small ovaries. Carcinoembryonic antigen, cancer antigen (CA) 125, and CA 19-9 levels were within normal limits. The next day, a contrast-enhanced computed tomography scan of the abdomen revealed a 15-cm left-sided cystic mass, which seemed to originate from the tail of the pancreas (Fig. 1a). The cystic mass showed a 4-cm nodule in its wall (Fig. 1b) and was suspected for a cystic papillary adenocarcinoma. At laparotomy, the cyst was not adhered to the pancreas and could be easily separated from its location near the tail without opening the pancreatic capsule. Vascularization appeared to arise from the mesentery of the left colon. It was localized in the retroperitoneal space extending caudally from the spleen to the lower abdomen with medial displacement of the left colon. Total resection of the cyst was performed, and the specimen was sent for histopathological examination. Further inspection showed two normal ovaries. Her postoperative recovery was uneventful. One year after surgery, the patient was without signs of recurrence or metastasis.
Fig. 1Contrast enhanced CT scan shows images of a pancreatic cyst (arrowheads) originating from the tail of the pancreas (a) and a mural nodule within the wall of the cystic mass (b)
Materials and methods
The specimen was fixed in 4% buffered formalin. Representative samples were routinely processed and embedded in paraffin blocks. Four-micrometer-thick sections were stained with hematoxylin and eosin and with parallel routine immunohistochemical procedures. The antigens tested by immunohistochemistry were: pan-keratin, keratin Cam 5.2, cytokeratin 7, cytokeratin 10, cytokeratin 18, cytokeratin 20, epithelial membrane antigen, vimentin, desmin, actin, myosin, CD34, CD68, CD99, CD117, S-100 protein, and bcl-2.
Pathological findings
The specimen consisted of a unilocular cyst measuring 20 × 11 cm with a smooth surface. The content was a watery mucinous material. The wall was thin with a smooth gray-white inner surface and contained a circumscribed bean-shaped solid mural nodule of 3.5 × 3.5 × 2.5 cm, which showed a brown-yellow and focally hemorrhagic cut surface. Microscopically, most of the cyst was lined by single-layered tall columnar cells, abundant clear cytoplasm, and small basally located nuclei (Fig. 2a).
Fig. 2Cyst wall with typical tall columnar mucous-secreting epithelium and fibrous wall (a). Low cellular proliferation with some stratification of the cells and slight nuclear atypia (b). Heterogenous proliferation of pleomorphic cells, spindle cells, osteoclast-like giant cells, and some mononuclear cells with some pigment (c). Detail of sarcoma-like nodule (d)
Occasionally (over less than 1% of the surface), the epithelium showed slightly atypical proliferation with glandular budding, tufting of the epithelium, decreased cytoplasmic mucin, some stratification of slightly irregular nuclei, and an occasional mitoses (Fig. 2b). There was no infiltrative growth but foci of borderline malignancy. The nodule was well circumscribed without vascular invasion and consisted of a heterogenous population of spindle-shaped cells, pleomorphic cells with bizarre nuclei, mixed mononuclear inflammatory cells, benign osteoclast-like giant cells, and foci of hemorrhage. There were mitotic figures, including some atypical forms (Fig. 2c and d). The sarcoma-like cells proved to be keratin negative (Table 1).
Table 1Immunohistochemical results of the sarcoma-like cellsAntigenResultPan-keratin−Keratin Cam 5.2−Cytokeratin 7−Cytokeratin 10−Cytokeratin 18−Cytokeratin 20−Epithelial membrane antigen−Vimentin+Desmin−Actin+/− (some)Myosin−CD34−CD68+CD99−CD117−S-100 protein−bcl-2+/− (some)
Discussion
Mucinous cystadenomas can be located in the ovaries, pancreas, and in the retroperitoneum. The mucinous cystadenoma presented was localized retroperitoneally near the pancreas but was clearly not adhered to it. Because normal-appearing ovaries were found, the cystadenoma was thought to be primary retroperitoneal.
According to the literature, symptoms are nonspecific, and most patients complained of an abdominal distension or mass with or without pain [9]. Mucinous cystadenomas were relatively large, varying from 10 to greater than 20 cm in diameter, which is large enough to cause symptoms like abdominal fullness [9]. Preoperative diagnosis is very difficult, not because the tumors are often overlooked in the differential diagnosis but also because no sensitive methods or reliable markers are available [2]. As retroperitoneal mucinous cystadenomas are histologically similar to mucinous cystadenomas of the ovary, the ultrasonographic image pattern is in general of no help in distinguishing between ovarian and retroperitoneal origin. In our case, the diagnosis of the retroperitoneal mucinous cystadenoma could not be established preoperatively by ultrasonography or computed tomography. Although ultrasonography, computed tomography, or magnetic resonance imaging can detect retroperitoneal cysts, the diagnosis of mucinous cystadenoma is seldomly made preoperatively. The usual preoperative differential diagnosis consists of ovarian cyst, cystic mesothelioma, cystic lymphangioma, nonpancreatic pseudocyst, and renal cyst [4, 9, 18]. Although aspiration is a good method for delineating the nature of the cyst, cytologic analysis of the aspirated fluid frequently fails to reveal the type of epithelial cells lining the cyst. Therefore, exploratory laparotomy with complete excision of the cyst is usually indicated both for diagnosis and treatment [2].
Retroperitoneal mucinous cystadenomas are histologically similar to mucinous cystadenomas of the ovary. The histogenesis of these tumors is still unclear. Four main hypotheses have been proposed [2, 14]. According to the first three hypotheses, the tumor arises either from ectopic ovarian tissue, although ovarian tissue was only rarely found [14], from a teratoma in which the mucinous epithelium has overgrown all other components or from urogenital remnants. The most widely accepted theory suggests coelomic metaplasia as the causal agent, whereby tumors arise from invagination of the peritoneal mesothelial layer that undergoes mucinous metaplasia with cyst formation [3, 9]. Such origin rather than from ectopic ovarian tissue is supported by the occurrence of such a tumor in a male patients [5, 8, 10, 17].
Primary mucinous tumors of the retroperitoneum are very uncommon. These tumors can be classified into three clinicopathologic types: mucinous cystadenoma, mucinous cystic tumor of borderline malignancy, and mucinous cystadenocarcinoma. Our case was diagnosed as a primary retroperitoneal mucinous cystadenoma with only foci of borderline malignancy and a mural “sarcoma-like” nodule.
Mural nodules have been described in ovarian and pancreatic mucinous cystic tumors [7, 16]. Mural nodules may be malignant representing anaplastic carcinoma, containing a predominant population of cytokeratin-positive cells with high-grade malignant nuclei, or a genuine soft tissue-type sarcoma [7, 15, 16]. Benign pseudosarcomatous mural nodules are composed of a heterogeneous cell population of epulis-type giant cells, atypical spindle cells with bizarre nuclei and mitotic figures, mixed inflammatory cells, and signs of hemorrhage and necrosis. In these cases, immunohistochemical staining shows a weakly or focally cytokeratin positivity in the pseudosarcomatous cells.
We performed a literature review using Embase and Medline starting in 1966 and identified approximately 45 cases of retroperitoneal mucinous cystadenoma and 25 cases of mucinous cystadenocarcinoma. Only eight cases of mucinous cystadenoma with borderline malignancy have been reported (Table 2). Thus, our case is the ninth case of a retroperitoneal mucinous cystadenoma of borderline malignancy. By our knowledge, however, combination with a mural “sarcoma-like” nodule has not been described earlier in the literature. The patient should be followed. However, in this case, long-term follow-up seems not warranted regarding the only focal aspect (<1% of the surface) of borderline malignancy of the cyst and the benign reactive nature of the mural nodule.
Table 2Cases of primary retroperitoneal mucinous cystadenomas of borderline malignancyStudy (year)Age (years)SexSymptomSize, imageTumor markerPreoperative diagnosisHistoryPathologyExtracystic extensionTherapyOutcomeNagata et al. [11] (1987)41FAbdominal swelling, pain12 × 10 × 9 cmUDUDUDMCAborNoTRUDBanerjee and Gough [1] (1988)47FAbdominal mass10 cm (US) next to spl.NDLt adrenal tumorApp HystMCAborNoTR, resection spl. + lt adrenalNEDMotoyama et al. [10] (1994)63MAbdominal pain6 cm (US), under rt kidneyHigh CEA in cystic fluidRt renal cystNRMCAborNoNDNEDPearl et al. [13] (1996)33FAbdominal swelling, painLarge, unilocular lt (CT)NDNDNRMCAborNoLRNED, 10 monthsPapadogiannakis et al. [12] (1997)33FAbdominal mass13 × 9 cm (US + CT)NDMesenteric cystNRMCAborNoTRNED, 12 monthsChen et al. [2] (1998)48FAbdominal fullness15 × 13 × 9 cm (CT)NDMesenteric cystNRMCAborNoLRNED,8 monthsGutsu et al. [6] (2003)41FFlank pain, abdominal distension21 × 16 cm rt (CT)NDRetroperitoneal cystNRMCAborNoTRNED, 18 monthsMatsubara et al. [9] (2005)36FAbdominal distension12 × 8 cm rt (CT)CA 125:51 CA 19-9: 55Ovarian cystNRMCAborNoTR, App, MyoNED, 6 monthsPresent case (2007)45FAbdominal pain15 cm (US + CT)CEA, CA125, CA19-9: normalCystic papillary adenocarcinoma or mucinous cystic neoplasmEndomMCAborNoTRNED, 12 monthsF Female; M male, UD unknown data, NR not remarkable, MCAbor mucinous cystadenoma borderline malignancy, TR tumor resection, LR laparoscopic resection, NED no evidence of disease, US ultrasonography, spl. spleen, App appendectomy, Hyst hysterectomy, Myo myomectomy, Endom endometriosis, rt right, lt left

mucinous cystadenoma
mural nodule
retroperitoneum